Starburst Galaxies in the Early Universe

Transcription

1 Starburst Galaxies in the Early Universe Theresa Nilsson Degree project for Master of Science (Two Years) in Physics 30 hec Department of Physics University of Gothenburg 2015

2 Thesis for degree of master of science in physics Starbust Galaxies in the Early Universe Theresa Nilsson Department of Physics University of Gothenburg Gothenburg 2015

3 Supervisors: Kirsten Kraiberg Knudsen and Guillaume Drouart Chalmers University of Technology Examiner: Maria Sundin University of Gothenburg c Theresa Nilsson, 2015 Gothenburg, Sweden

4 Abstract Understanding the evolution of galaxies requires observational constraints of the physical properties of galaxies in order to develop models of galaxy evolution. To accomplish this, large efforts have been made and resulted in large and deep surveys of galaxies over a wide wavelength range from UV to far-infrared. Many different telescopes and instruments have been used to observe galaxies and the data have been collected in extensive catalogues containing many high-redshift galaxies. Enabling a large part of the history of the Universe to be studied. The data in these catalogues are often composed out of measurements in photometric bands. To interpret the observed data a method called spectral energy distribution (SED) fitting. It consists in using a galaxy evolution code which is able to predict the flux of the galaxy given a set of parameters (age, mass of the stellar populations, star formation history, gas and dust mass...) and fit this predicted SEDs on photometric measurements. SED is a statistical comparison between spectral evolution models and observations. In this thesis the SED fitting code MAGPHYS is used to analyse a sample of around 1500 star-forming galaxies in the redshift range 0.3 z 2.5 (probing the Universe when it was billion years) and observed from optical to far-infrared wavelengths. With this sample it is possible to probe a large part of the history of the Universe and enables the study of how properties of galaxies have changed during a large cosmological time scale. The model MAG- PHYS can be fitted to the whole wavelength range in a consistent way, including both the light from stars (optical) and dust (infrared) in the galaxy. Physical parameters are estimated for the sample and used to select some of the most extreme objects called starburst galaxies. When trying to understand the formation and evolution of galaxies it is not only interesting to look at the most common objects, it is also interesting to study the outliers. One type of outliers are starburst galaxies which are galaxies forming new stars at a much

5 higher rate than what is observed in normal galaxies. By understanding the driving mechanism of these particular galaxies and also the importance of these objects to the overall evolution of galaxies, will thus improve our knowledge of galaxy evolution. In this study a large effort have been made to have reliable estimated properties of the galaxies. From the estimated physical properties, starburst galaxies have been selected as the sources with highest specific star formation rate and these objects are among the most dusty objects in the sample. The relation between star formation rate and stellar mass of galaxies, know as the main sequence have also been recovered for this sample with a small scatter of outliers. The evolution of the dust mass fraction has been studied and the result is in agreement with previous studies which have mainly focused on the gas mass fraction. We found that the dust mass fraction is increasing at higher redshifts and a high dust mass fraction corresponds to a lower temperature of the interstellar medium in the galaxy. The starburst galaxies have a higher average dust mass fraction then the whole and thus indicate that these objects also have a higher gas mass fraction with can explain their high star formation rate. The specific star formation rate have been found to increase with redshift in good agreement with previous studies. This forms a picture where galaxies at higher redshift have a higher gas mass fraction and are forming star at a higher rate compared to galaxies at low redshift.

6 Acknowledgements I would like to express my sincere gratitude to my two supervisors Kirsten Kraiberg Knudsen and Guillaume Drouart. Without their continuous help and support this work would not have been possible. I want to thank them both for their patience, motivation and knowledge which have helped me throughout this thesis. Beside my supervisors I would like to thank Lukas Lindroos who has also helped me during this project. Then I would like to thank Onsala Space Observatory for letting me conduct my master thesis work at their facility and for the warm welcome from all the people working there and a special thanks to Susanne Aalto for inspiring me to do my master thesis in astrophysics. Lastly but not least I would like to thank all my classmates for these fun and intensive 5 years, without you my studies would not have been the same. Thank you all! Theresa

7 Contents 1 Introduction 1 2 Galaxies, near and far Galaxies in general Spiral galaxies Elliptical galaxies Starburst galaxies Distant galaxies Main sequence galaxies and starburst Observing galaxies Cosmology Distance measurements Selecting star-forming galaxies Lyman-break selection technique BzK galaxies UV J galaxies Distant Red Galaxies PACS detected galaxies Spectral Energy Distribution fitting SED of a galaxy SED components Initial mass function i

8 3.2.2 Isochrones Stellar spectra Star formation history Chemical composition Dust MAGPHYS, SED fitting code Libraries of SEDs Fitting procedure Version Version Samples and Method Data COSMOS GOODS-S Robustness of SED fit and selecting reliable fits Resolution and blending Photometry coverage Goodness of fit Limitations in underlying models Results SFR estimates Stellar mass estimates Specific star formation rate Dust properties Discussion The model and uncertainties Star-forming galaxies Dust properties Evolution of galaxies Conclusion 103 ii

9 8 Outlook 105 APPENDICES 107 A Evaluating the different versions of MAGPHYS 107 A.1 BzK and PACS galaxies in GOODS-S A.2 Comparison at lower redshift (z 1) Bibliography 114 iii

10 iv

11 Chapter 1 Introduction Looking up in the sky, what is seen is a Universe filled with stars, planets and galaxies. One distinctive feature seen on a clear night is the broad band of stars across the sky, which is the disk of the Milky Way, the galaxy we live in. The Milky Way is just one out of 100 billion galaxies in the observable Universe. There is a huge variety in the shape and size of galaxies in the nearby Universe. To understand why this apparent zoo of galaxies exits, it is necessary to understand how the first galaxies formed and how they have evolved with time. Understanding the process of galaxy evolution is a complex and interesting subject where many different aspects needs to studied. Both observations and theoretical models are needed to understand the morphology of the galaxies we see today. Due to fact that the evolution of galaxies is extremely slow compared to the human lifespan, one cannot gain a complete picture of how galaxies evolve by studying a single galaxy. Instead, it is necessary to look at the whole range of galaxies and at different evolutionary phases in their lifetime. Due to the finite speed of light, emission from distant objects will take time to reach us, making it possible to look back in time and studying galaxies at different epochs. With modern instruments, we 1

12 are now able to see galaxies 13.2 Gyr (billion years) ago (Bouwens et al., 2013). The current age of the Universe is 13.7 Gyr, which means that the first galaxies formed relatively early in the Universe. To progress in the field of galaxy evolution it is necessary to make observational constraints on how physical properties of galaxies has changed with time, in order to construct evolutionary models. Astronomical objects are studied using telescopes which measure the intensity of photons. By observing the flux of photons from a galaxy, one is able to obtain information about the object and determine different properties. To obtain as much information as possible it is important to observe across a wide range of electromagnetic spectrum since the radiation at different wavelengths origins form different physical and chemical processes. Stars, which radiate mainly in the optical and ultraviolet (UV) are an important part of the spectrum, but it is not the only source of radiation. Interstellar dust absorb starlight and re-emits radiation in the infrared (IR). The extragalactic background light, (EBL) is the integrated radiation from all the radiating sources in the Universe. This consist mainly of two parts, optical light coming mainly from stars and infrared light due to gas and dust. Despite infrared photons are much less energetic than optical, the IR and optical part of the background radiation have similar brightness, see figure 1.1. The cosmic microwave background radiation is another important background, and is the thermal radiation remnants of the recombination epoch and thus have a very different origin than the EBL. By observing in a large range of wavelengths, one can have a better understanding of the composition of galaxies and determine important properties such as mass, age and the rate of star formation. The aim of this thesis is to study the star formation of distant galaxies and look closer at a certain type of galaxies called starburst. What characterises starburst galaxies is a very high star formation rate (up to 1000 M yr 1 ), which means that they form more star compared to ordinary star-forming galaxies ( 10 M yr 1 ). The reason why they are not behaving in the same way is not fully understood. The reason for comparing starburst galaxies with ordinary 2

13 Figure 1.1: The Cosmic Optical Background (COB), the Cosmic Infrared Background (CIB) Cosmic Microwave Background (CMB). radiation Credit: H. Dole et al./ias. 3

14 galaxies is because the underlying mechanisms for star formation are probably different (e.g. mergers, galaxy interactions). The aim of this thesis work is thus, to find differences, if any, in the spectral energy distribution (SED) of starbursts and ordinary galaxies at high redshift. The SED of galaxies is the light emitted by a galaxy over the whole electromagnetic spectra. The spectrum contains information about the different constituents that make up the galaxy. By analysing the SED it is possible to extract information about the galaxy and a powerful tool commonly used to interpret observations of galaxies is SED fitting, which is a statistical comparison of stellar evolution models with observations. At present, many galaxies are only observed in a limited number of photometric bands, the power of SED fitting allows one to constrain physical properties from these type of observations, which makes this is a very important technique for studying the early Universe. By fitting a SED model to the observations, important parameters can be constrained, e.g. stellar mass, star formation rate, dust mass, dust temperature etc. The technique of SED fitting will be used in this thesis to study galaxies in the early Universe. By the use of publicly available catalogues of deep surveys, a large number of galaxies is analysed in order to study the evolution of star-forming galaxies. The thesis is organized as follows, chapter 2 is general introduction to galaxies in the present and distant Universe and description of different methods of identifying star-forming galaxies. A more detailed description about the SED of galaxies as well as SED fitting is discussed in chapter 3. In chapter 4 follows a short introduction of the data and how galaxies for this study have been selected. The results are presented in chapter 5, followed by discussion and conclusions in chapter 6 and 7 respectively. 4

15 Chapter 2 Galaxies, near and far 2.1 Galaxies in general Galaxies are gravitationally bound systems in space consisting of mainly stars, gas and dark matter. The stars make up a considerable amount of the baryonic mass of a galaxy, and the interstellar medium (ISM) is the matter which occupies the space between the stars. The ISM includes atomic and molecular gas and also dust grains. Stars are important building block of galaxies and are formed out of dense molecular gas in the ISM. When a cloud starts to collapse, there must be an efficient mechanism for cooling, otherwise the thermal pressure will halt the contraction. Dust, molecules, and metals (in astrophysics metals refer to all elements heavier than helium He) are efficient at cooling the gas and are therefore very important for the formation of stars. When the collapsing cloud has reached a temperature and pressure high enough for hydrogen H to fuse into He, a star is said to be born. Galaxies can be made up of a range of different types of stars from very massive blue stars to low-mass red stars and stars of different ages and chemical compositions. This is due to the fact that stars are usually born in clusters with a wide 5

16 range of masses and the stars will go through different evolutionary stages depending on their mass. When a star is born it enters what is called the main sequence (characterised by hydrogen burning) and will continue to burn H until about 10% of H has been converted into He (Sparke and Gallagher, 2011). The lifetime on the mainsequence is determined by the mass of the star, where more massive stars have a much shorter lifetime since they are hotter and burn hydrogen more effectively. The low-mass stars with a mass 0.8 M have a lifetime of 25 Gyr and the most massive stars 120 M have a lifetime of 2.6 Myr (Sparke and Gallagher, 2011). Since the current age of the Universe is 13.7 Gyr, this means that lowmass stars formed at early epochs are still on the main-sequence phase burning hydrogen, while other stars have ended their life a long time ago. Stars are important producers of gas and dust in galaxies. A star will during its life replenish the ISM, due to feedback processes such as jets, stellar winds and supernovas. This will enhance the ISM with chemically enriched gas, molecules and dust grains. The chemical composition of a galaxy will thus change with time as stars produce heavier elements. This will affect the properties of the galaxy and its SED. Since dust, molecules, and metals are important for the cooling process, it will also change the conditions for star formation in the galaxy. The different phases of the ISM contribute to the spectra of the galaxy and it is essential to characterise each of these phases to be able to put constraints on galaxy evolution. In this thesis, the focus will be on the continuum emission of galaxies. For these types of studies dust plays an important role since dust grains absorb UV and visible light very effectively and re-emit the absorbed light in the IR. Because of these properties, the presence of dust grains will dramatically affect the continuum emission of galaxies. Figure 3.1 shows an example of how the spectra of a galaxy only containing stars is changed when the effect of dust is taken into account. Galaxies come in a wide ranch of sizes and shapes. There are different ways of classifying them, one common way is to divide 6

17 galaxies according to their optical morphology, with the most common classes being, elliptical and spiral galaxies and with a small subset of galaxies classified as irregulars. Here follows a description of galaxies in the two main classes. There is also a section dedicated to starburst galaxies which are not part of the classical morphology classification. Some of the starburst galaxies have irregular structures and can in that case be classified as irregulars (Sparke and Gallagher, 2011) Spiral galaxies Spiral galaxies are characterized by the appearance of spiral arms and having a disc-like shaped. Spiral galaxies consist of two main regions where the first is the flat disc made up of stars and gas which rotate around the center. The second is the bulge which is a concentration of stars in the center of the galaxy that can vary in size. There is also a faint halo of stars surrounding the galaxy. The spiral arms are well-defined regions within the disc and origin from the central part of the galaxy. There is a huge variety in the appearance of spiral galaxies and figure 2.1 shows a few example of different spiral galaxies. The number of spiral arms can vary and they can be both tightly wrapped or very open. Some spiral galaxies also exhibit a barred central structure and in many cases the spiral arms appears to originate from the ends of the bar. In the case of a bar the central bulge will have an elongated structure (Longair, 2008). The arms are not a rigid body but is a density wave that propagates throughout the galaxy. The gas then becomes compressed inducing star formation in the regions of the spiral arms. The spiral arms are regions with ongoing star formation and thus contains young stars. The bulge consist mainly of an old generation of stars and does not contain many young bright blue star, and thus appear more red in color compared to the spiral arms. The light distribution of the bulge is therefore similar to elliptical galaxies (Longair, 2008). The star formation rate (SFR) is the mass bond up in new 7

18 Figure 2.1: Six different type of spiral galaxies found in the local Universe. From left to right the galaxies are NGC 5247, NGC 4321, NGC 1300, NGC 4030, NGC 2997 and NGC 1232 Credit: ESO/P. Grosbøl. 8

19 stars per unit of time, and is often described in the unit M yr 1. The SFR in spiral galaxies varies, but have a typical values of 1-10 M yr 1. To given a point of reference, the Milky Way is a spiral galaxies with SFR=1.65 M yr 1 (Licquia and Newman, 2014) Elliptical galaxies Elliptical galaxies have a spheroidal or ellipsoidal shape, are often smooth and have a featureless brightness profile. Elliptical galaxies thus differ from spiral galaxies since they have no prominent inner structures such as spiral arms (Longair, 2008), see figure 2.2. Elliptical galaxies exist in a wide range of sizes from among the most luminous galaxies (called cd elliptical) that are up to 100 times as luminous as the Milky Way, to the very faintest (dwarf elliptical and dwarf spheroidal) with a luminosity as low as one tenth of the Milky Way. The stars in normal and giant elliptical galaxies have random orbits around the center. The smallest elliptical galaxies have a less random motion and the stars have a more rotating trajectory around the center (Sparke and Gallagher, 2011). A common feature of elliptical galaxies is that they are usually gas poor and have a very low SFR. This results in a stellar population dominated by red old stars and there are almost no massive stars Starburst galaxies A starburst galaxy is a galaxy that undergoes a brief period of intense star formation. These galaxies are not part of the morphological classification and figure 2.3 shows an example of a starburst galaxy in the local Universe. There is no rigorous definition of a starburst galaxy. However, there are many different criteria used. Common are that starburst galaxies have a high rate of star formation compared to the average rate observed in normal star-forming galaxies. The duration of a starburst must also be much shorter than the age of galaxy, since starburst is a transient event. The SFR in these galaxies is so high that it will consume all of the gas 9

20 Figure 2.2: Giant elliptical galaxy NGC 1316 in the local Universe, some dark structures can be seen in front of the galaxy which are dust clouds. Credit: ESO (a). 10

21 in a timescale much shorter than the age of a galaxy. To identify and find starburst galaxies, there are different parameters one can study, some of them being the specific star formation rate ssfr (SFR divided by stellar mass), rate of star formation per unit area Σ SFR (typically given as M yr 1 kpc 2 ) and as extreme outliers to the SFR-stellar mass relation known as the main sequence of starforming galaxies (Noeske et al., 2007; Rasmussen et al., 2012; Heckman, 2000; Rodighiero et al., 2011). It is however not possible to define a particular values for these parameters that separates normal and starburst galaxies. There is a continuum of values observed and changes depending on how distant the galaxies are. The rate at which stars have been forming is not constant but has change during the history of the Universe (Madau and Dickinson, 2014). This means that normal galaxies at high cosmological distance would be classified as starburst galaxies if compared to galaxies in the local Universe. An important part of the evolution of galaxies is thus to understand why there are so many galaxies with exceptionally high SFR at high redshift and to understand the driving mechanism behind them. It is also important to understand which role, if any, that starburst have in the evolution and structure of present galaxies. 2.2 Distant galaxies A huge leap in extragalactic astronomy was made when it became possible to observe very distant normal galaxies through the first deep field, Hubble Deep Field North (Williams et al., 1996). The location was chosen to be a part of the sky as empty as possible, in order to minimize the contamination from bright foreground sources which is very important in order to observed very distant and faint objects. The field was observed with a exposure time of more than 100 hours, over 10 days in order to detect very faint sources. What was observed was that galaxies in the early Universe seems to be smaller and more irregular compared to the galaxies in the present Universe (Hubble). This was the start of an era when it became 11

22 Figure 2.3: Starburst galaxy NGC 1313 in the local Universe. Credit: ESO (b). 12

23 possible to look back in time and study the evolution of galaxies. It was realised that the physical properties of galaxies have changed considerably with time. One example is the the cosmic SFR density which have not been constant over time, it reached a peak around 2-3 billion years after the Big Bang (e.g. Bouwens et al., 2011), since then the SFR have decreased. A second big leap happened when the first IR satellite ISO and IRAS were deployed in 1995 and 1983 respectively (ISO; IRAS). With these new telescope new discoveries were made and one thing realised was that a large fraction of the star formation is hidden at optical wavelength due to the dust obscuration. Infrared observation were found to be a vital complement to optical observation in order to access to the full picture of the star formation process in galaxies. The Herschel Space Observatory allowed for the first time to cover the entire IR SED of obscured star formation out to z 4. The main advantage is that this allows the dust properties in galaxies to be constrained. This is important due to the strong absorption of starlight by dust grains and where the presence of dust can strongly effect the interpretations of UV and optical observations Main sequence galaxies and starburst Star-forming galaxies at all redshifts seem to follow a SFR-stellar mass relation known as the main sequence of star-forming galaxies 1 (Noeske et al., 2007). This corresponds to a line in the SFRstellar mass plot, which star-forming galaxies scatter along. The basic feature of the main sequence is that the more massive a galaxy is the higher star formation rate is. The galaxies that lie along this line are called main sequence galaxies, but perhaps, the most interesting galaxies are those that do not follow this relation. At different epochs after the Big Bang, SFR-stellar mass relation has changes since the average SFR changed with redshift. The location of main sequence thus depends on redshift. The main sequence will 1 Not to be confused with the more common term of stellar main sequence, which is the first period after stars are born, characterised by H-burning. 13

24 in this report be defined by the ssfr-mass relation of Tacconi et al. (2013) defined as, ssfr = a (M / M ) p ((1 + z)/2.2) q, (2.1) with a = 0.68(±0.1) Gyr 1, p = , q = The ssfr is defined as the star formation rate per stellar mass, (ssfr=sfr/m ) and is a common term used to describe star formation of a galaxy. It is simply the timescale for a galaxy to double its stellar mass at the current SFR, and relates to the star formation efficiency Observing galaxies Galaxies and all other objects is the Universe are mainly studied by measuring the intensity of photons emitted by the object. The flux density F ν [W m 2 Hz 1 ] is the rate energy is transfered by photons, per unit surface area per unit frequency in the detector. The flux density can also be given by wavelength F λ (energy per unit surface area per wavelength), and the following relation holds, νf ν = λf λ. (2.2) A common unit used in astrophysics for flux density F ν is Jansky (1 Jy= W m 2 Hz 1 ) since the SI unit is to large for practical use. For extended sources the surface brightness is often quantified in units of Jy per solid angle. The spectral luminosity L ν of a source is defined as the total power per frequency, W Hz 1 so the inversesquare law relation between spectral luminosity and flux density in free space is given by L ν = 4πd 2 F ν, (2.3) where d is the distance to the source. In the past, the intensity was not been measured in F ν but in magnitudes. The magnitude system originates from a time when the brightness of a source where determined only using the bare eyes, and is thus in a logarithmic 14

25 scale. The Vega magnitude system uses the star Vega as a reference point and the apparent magnitude in band x is calculated by ( ) Fx m x m vega = 2.5 log 10, (2.4) F vega where m vega and F vega is the Vega reference apparent magnitude and flux measured in band x. The Vega magnitude system have been used for a long time, but have the disadvantage of being wavelength dependent. The magnitudes in the so-called the AB system is calculated by, ( ) Fν m AB = 2.5 log (2.5) Jy where the F ν is given in Jy and m AB is the monochromatic AB magnitude (Oke and Gunn, 1983). The magnitude given in Eq. (2.5) is called the apparent magnitude m and is the observed magnitude. The absolute magnitude M relates to m by m M = 5 (log 10 (d) 1), (2.6) where d is the distance to the source measured in parsec, pc. When using a magnitude system it should always be keep in mind that it is a reversed scale, the brighter the object appears, the lower magnitude value it has Cosmology The Universe we live in is not static but it is expanding where observed galaxies are all moving away from each other. Not only is the Universe expanding, but it is doing so at an accelerating rate (Perlmutter et al., 1999; Riess et al., 1998). The modern cosmological models are based on the cosmological principle, which states that our location in the Universe it not special and that the Universe is isotropic (looks the same in all directions) and homogeneous (looks the same from all locations) on large scales. 15

26 The current standard model is called the ΛCDM standing for cold dark matter model, where Λ refers to the cosmological constant. The cosmological constant is associated with dark energy, introduced to reproduce the observed accelerated expansion. In the current cosmological model, there are two types of matter components, the ordinary baryonic matter (which everything we can see is made up of) and dark matter. Dark matter was introduced in order to account for gravitational effects observed at large scales. There were some difficulties in understanding the observed rotation curve of galaxies which did not look like what one expected when taken into account the distribution of visible matter. A way of describing the observed flat rotation curve of galaxies where to include the gravitational effect of large amount of matter surrounding the galaxies. But this additional matter has not been observed in any other way than its gravitational effect. This inferred type of matter does not interact with itself or with the electromagnetic force and thereby the name, dark matter since it does not radiate any photons. The Universe is currently believed to be flat, with a dark energy density Ω Λ = 0.69 and matter density Ω m = 0.31 determined from observation (Planck Collaboration et al., 2014). This means that the almost 70% of energy in the Universe consists of dark energy and matter only make up 30% of the total energy. Dark matter accounts for most of the Ω m with an energy density Ω c 0.27, ordinary baryonic matter only account for Ω b 0.04 of the energy density of the Universe (Planck Collaboration et al., 2014). This means that everything we can see and observe is less than 5% of the total energy of the Universe. In this thesis a ΛCDM cosmological model with H 0 = 70 km s 1 Mpc 1, Ω Λ = 0.7 and Ω m = 0.3 will be adopted. Redshift Living in an expanding Universe, one thing to bear in mind when observing distant galaxies is the fact that galaxies are moving away from the observer. This means that the wavelength of the emitted 16

27 light will shifted to longer wavelengths and appear more red that the rest-frame wavelength. This effect is called redshift and is similar to the Doppler effect of sound waves. The redshift, z it defined as z λ obs λ 0 λ 0, λ obs = (1 + z)λ 0 (2.7) with, λ obs the observed wavelength and λ 0 the wavelength of the emitted rest-frame light (Schneider, 2015). Through spectroscopic measurements of an object it is thus possible to determine the redshift by studying how much the wavelength of characteristic spectral lines are shifted compared to their rest-frame wavelength 2. Redshifts determined by this method are referred to as spectroscopic redshifts, which is the most precise way of measuring the redshift, but are costly in terms of observation time needed. Another technique uses broad band photometry to determine the redshift and relies upon the continuum emission from galaxies. Redshifts determined in this way are referred to as photometric redshift but are not as precise as spectroscopic redshift. This technique is cheaper in terms of telescope time and are thus a good alternative method for determining the redshift, especially for large sample of sources, but requires the assumption of a SED template (observationally or theoretically built) Distance measurements There are different ways of defining a distance to an object from a cosmological point of view. One common way is to use the redshift z as a distance measurement, keeping in mind that the redshift is determined by the relative velocity in respect to the observer. It may be convenient to translate the z parameter into a length or distance. 2 Galaxies consist of atomic and molecular gas, which are excited during different processes, when there is an electron transitions, light of a known specific frequency will be emitted 17

28 For small redshifts (z 1), one can use the Hubble relation z = H 0 D, (2.8) c where H 0 it the Hubble constant and D is the distance (e.g. Schneider, 2015). But at higher redshift, luminosity distance D L is used instead, defined in terms of the relationship between the apparent magnitude m and the absolute magnitude M, M = m 5(log 10 D L 1). (2.9) But this requires that one knows the actual luminosity of the object which it not always the case. The relation between the observed luminosity and flux of an object is given by F = L 4πDL 2. (2.10) This is another way of expressing the luminosity distance. Equation (2.10) can be used to convert observed flux into luminosity. In that case it is essential to know the luminosity distance. Another way of determining D L is by, D L (z) = (1 + z) 2 D A (z) (2.11) where D A is the angular-diameter distance (Schneider, 2015). The angular-diameter distance depends on value of the cosmological parameters and it is not trivial in most cases. Instead of using the exact value of D A to determine D L, one can use an analytical fit of the luminosity distance described in (Pen, 1999) give as, D L = c [ ( )] 1 (1 + z) η(1, Ω m ) η H z, Ω m, (2.12) where η(a, Ω m ) =2 s [ 1 a s ] 1/8 s a3 a s3 a s4 18

29 with s 3 = 1 Ωm Ω m. 2.3 Selecting star-forming galaxies The physical processes taking place in galaxies appear in the observed spectrum. The observed spectrum of galaxies can look very different depending on the history of the galaxy. The amount of old and young stars can greatly affect the observed optical color. The peak temperature being proportional to the mass of stars, with massive star emitting a lot of blue light and low mass stars emits more red light. The optical color of a galaxies can thus indicate whether a galaxy has a young or old stellar population. However, gas and dust can also greatly change the spectra of a galaxy by absorption and re-emission of light. When working with catalogues and tables of astronomical objects it is important to be able to select the objects of interest. This can be done by finding a parameter that describes certain features and which enables categorisation of objects depending on the value of the parameter. One example of this is the redshift z, by selecting only objects with same z value you will have a sample of objects that are at the same distance at the same epoch in time. Since there are such a wide range in galaxy types it is important to find a way to distinguish them. If a galaxy is close (z 1) it may be possible to determine what type of galaxy it is by simply looking at it (elliptical, spiral, ex.), but at high redshift it is not possibles since instruments are not able to resolve the galaxies. Doing multi-band photometry, it is possible to define selection criteria base on the observed flux. Below follows a description of some of the most common photometric selection techniques for high-z star-forming galaxies Lyman-break selection technique The Lyman series is a hydrogen spectral line series due to electron transitions from excited states n 2 (n principal quantum 19

30 number) to the ground state n = 1 resulting in UV emission lines. The Lyman-break selection technique (e.g. Steidel et al., 1996) relies on the fact that radiation at higher energies than the Lymanlimit (n = ) at 912 Å is almost completely absorbed by neutral gas around star-forming regions in galaxies. In the rest frame of the emitting galaxy, the emitted spectrum is bright at wavelengths longer than 912 Å, but very dim or imperceptible at shorter wavelengths and this creates a sharp drop or break, and this can be used to find the position of the Lyman limit. When the galaxies are redshifted, so is the break. For instance, at z 3, the Lyman-break will appear to be at wavelengths of about 3600 Å, which is long enough to be detected by ground- or space-based optical telescopes. Galaxies at high redshift (z 3) can thus be selected using the Lyman-break selection techniques BzK galaxies The BzK selection is a simple two-colors selection based on the observed B-, z- and K-band and it allows one to select passively evolving galaxies or actively star-forming galaxies at z > 1.4, independent of there dust reddening (Daddi et al., 2004). The following definition is used BzK (z K) AB (B z) AB, and where BzK 0.2 selects star-forming galaxies with z > 1.4, and BzK < 0.2 (z K) AB > 2.5 selects the passive evolving galaxies which are the reddest objects (Daddi et al., 2004) UV J galaxies The UV J selection is a technique used to distinguish quiescent and star-forming galaxies at redshift 0 < z < 2 (Williams et al., 2009). The method uses a simple two-colors selection based an the U-, V - and J-band. By determining the rest-frame U V and V J colors the two different types of galaxies can be separated, see figure

31 Figure 2.4: Rest-frame U V vs V J color for galaxies in five different redshift bins. The solid line shows the division between passive and star-forming galaxies. Credit: Williams et al. (2009). The reason why the galaxies are divided in two regions are because blue U V galaxies usually correspond to unobscured star-forming galaxies and red U V galaxies could be both dust obscured starforming galaxies or passive galaxies containing an old red stellar population. The V J color is used to distinguish between these two different types of red galaxies since dust free passive galaxies are blue in V J due to the fact that they are are less affected by obscuration and therefore emits more in the optical. This gives that the passive and star-forming galaxies will occupy a separate regions in the UV J plane. 21

32 2.3.4 Distant Red Galaxies Another way of selecting galaxies is based on the J K color and are defined as, J K > 2.3 mag, where magnitudes are given in the Vega system (described in section 2.2.2). This criteria selects what is called Distant Red Galaxies (DRGs). It overcomes the inability of the Lyman-break selection technique to find intrinsically red objects by using observed NIR images to select high-redshift galaxies via their rest frame Balmer/4000 Å break (the Balmer series are electron transitions from n 3 to n = 2) and the 4000Å break is absorption lines due to metals in the atmospheres of old cool stars. Looking for a continuum break in J K will selects objects at 2 < z < 4 since that break correspond to the redshifted Balmer/4000 Å break, (Franx et al., 2003) PACS detected galaxies PACS (the Photoconductor Array Camera and Spectrometer) is an instrument installed on the Herschel Space Observatory that was active from 2009 to 2013 (Herschel). PACS has 3 bands in the range µm, which is optimal for studying dusty, young, distant, star-forming galaxies. This is due to the nature of the infrared emission for galaxies, which mainly origins from heated dust grains. The dust is heated by starlight and radiates approximately as a black body. The dust therefore probes a high star formation rate in the galaxy since it is mainly the UV/optical light from the most massive young stars that heats up the gas. The photometric PACS bands cover the wavelength region important for constraining the dust temperature. Thus by only selecting the sources that have a flux higher than a given threshold value in the PACS bands, it is possible to single out galaxies with high dust luminosity and high star formation rate. An active galactic nucleus (AGN) can also heat dust. However, for AGN the dust will typically be heated to a temperature much higher than when heated by light from star formation 22

33 regions. The dust emission will therefore consist of radiation from dust of two distinct temperatures and the peak of the black body emission of the AGN heated dust will be at lower wavelength. This makes it possible to single out the emission of the dust heated by starlight. 23

34 24

35 Chapter 3 Spectral Energy Distribution fitting 3.1 SED of a galaxy General reference: Conroy (2013) The observed spectral energy distribution (SED) of a galaxy reveals a lot information about the properties of the galaxy. The total light emitted by a galaxy is very complex and to determine the physical properties of the galaxy one has to be able to disentangle the components which make up the SED. It is not enough to just consider the different sources which emits photons but also consider what happens to the light as it travels out of the galaxy and through space. There are many different processes that absorb, scatter and re-emit photons which all contribute to alter the light in different ways. One also have to keep in mind the expansion of the Universe and that the observed photons will be redshifted. Galaxies can be studied by broad band photometry, where the flux is measured over a certain range of wavelength. The flux in different bands is measured via a 25

36 z= F ν [mjy] Unattenuated starlight Dust emission Attenuated starlight λ [µm] Figure 3.1: Example of SED fit using MAGPHYS for a galaxy (ID 703) in the COMSOS catalogue, observation in 22 photometric bands ranging from optical to FIR. The solid blue line shows the unattenuated stellar spectra and the solid black line is the best fit model where the effect of dust has been taken into account. Red points show the observed photometric data with errorbars indicating the 1σ uncertainty. 26

37 filter which will only let through light of certain wavelengths, in a range specific for each band. Depending on the specifications of the telescope and the instrument, the flux is only measured in a limited number of bands. Therefore, to fully sample the SED of a galaxy, a combination of instruments/telescopes is necessary. To be able to determine the physical properties of the galaxy such as stellar mass, age and SFR etc. one can use a program that is able to fit a modeled SED of the galaxy to the observed flux. Figure 3.1 shows an example of a galaxy observed in 22 photometric bands which haven been fitted to a model SED, shown as a black curve. There are many different codes available for SED fitting but all have limitations in which wavelength range they are able to handle. In this thesis, the code MAGPHYS is used. This code uses compiled libraries of model spectra for galaxies and compares the observed fluxes with the libraries to find the best match. Here follows a description of some of the most important SED components in general, and afterwards a specific description of the basic behind the MAGPHYS fitting code. 3.2 SED components A model SED is a prediction of how a real galaxy spectrum looks like, constructed by taking several different aspects in considerations. First of all, the foundation is the light emitted by stars and it is therefore necessary to have a library of stellar spectrum of all different types of stars. By combining this with how the stars evolve depending on mass (known as isochrones) and the number of stars born of different masses (the initial mass function), it is possible to construct what is called simple stellar populations SSP. This is the spectrum of a population of star born at the same time for a fixed metallicity and time. By combining SSP with the star formation history and the effect of emission and absorption of starlight by dust, a composite spectra is created. This is the modeled SED of a single galaxy given a certain star 27

38 b a c f e d g Figure 3.2: Components needed to construct a composite stellar population. a) three different initial mass function, b) different isochrones, c) spectra of individual stars, d) example of two different types of star formation and chemical enrichment histories, e) simple stellar populations and, f) dust attenuation and emission spectra, g) the model SED, construed as a combination of all the other components. Credit: Conroy (2013). 28

39 formation history, initial mass function (IMF) and type of dust attenuation. Figure 3.2 shows a schematic view of the different components and how these are combined to form a model SED. In the following each of the main parts will be described in some more detail Initial mass function The initial mass function (IMF) describes the distribution of the initial mass of a stellar population that enters the main sequence 1. Since the mass will determine fundamental properties such as color, luminosity, chemical enrichments and lifetime of the star, the IMF is an important tool needed when studying SEDs. There are several different IMF models, one of the first are called Salpeter IMF defined by the pioneering work of Salpeter (1955). The Salpeter IMF has the form dn/dm M 2.35, where the number of stars in each mass range decreases rapidly with increasing mass. Two more recent estimates are the Kroupa and Chabrier IMF (Kroupa, 2001; Chabrier, 2003) which only differ in details, but for M 1M they are consistent with the power law dn/dm M 2.3 very similar to the Salpeter IMF. The biggest difference between the Kroupa/Chabrier IMF and Salpeter IMF is the number of low mass stars (M < 1M ), where the Salpeter IMF estimate a much higher number of low mass stars compared to Kroupa, Chabrier IMF, see figure Isochrones An isochrone is a curve on the Hertzsprung-Russell (HR) diagram 2, representing the specific position of a population of stars with the same metallicity and age. The isochrones show how stars of differ- 1 The main sequence refers to the first stage after a star is born, characterised by H-burning. 2 HR-diagram is a plot showing the relationship between the stars absolute magnitudes or luminosities versus their spectral classifications or effective temperatures. 29

40 Figure 3.3: Three different IMFs, Salpeter (violet, solid line), Kroupa (blue, dashed line), and Chabrier (red, dot-dashed line), over a mass range of M. Credit: Crosby et al. (2013). 30

41 ent masses move through the HR-diagram as the stars evolve, see figure 3.2 for an example of isochrones. There are several different isochrones in the literature covering a range of masses, chemical composition and include different evolutionary phases that stars go through during their lifetime and these libraries are defined observationally and theoretically Stellar spectra Stars are typically classified on their spectral properties. The most commonly used classification groups stars according to their surface temperature; O, B A, F, G, K and M. O-stars are the most massive, which typically have an effective temperature T eff > 30000K and are blue in color, while low-mass M-stars have T eff < 3500K and have a red color (LeBlanc, 2010). The more massive a star is, the warmer it is and the faster it burns hydrogen and therefore becomes more luminous. But massive stars will on the other hand have a much shorter lifetime since they will run out of fuel more quickly. Since blue massive stars have such a short lifetime one can use this to determine if there are ongoing star formation in a galaxy. If there are mainly red stars in a galaxy it means that the blue stars are dead and what is observed is an older stellar population and hence there is little ongoing star formation in that galaxy. This is not always the case for galaxies which appear red in color, because it can also be that the galaxy is very dusty and the light of young stars have totally obscured by dust. Since the stars vary in color and luminosity they will have very different stellar spectra so depending on which type of stars a galaxy consist of, the SED may look very different. It is necessary to have have a library of stellar spectra of a wide range of different stars. The spectral libraries can be construed out of purely theoretical models or from empirical studies. 31

42 3.2.4 Star formation history When modelling a SED of a galaxy, it is not only important to know the distribution of stars born at the same time, but also know the star formation history of the galaxy. That is, how the rate of star formation has change during the lifetime of the galaxy, see figure 3.2 for an example of two different histories. For instance, a galaxy will look very different if there have been a fairly constant rate of star formation or if there was just a short period of massive star formation in the galaxy. It is thus important to choose the right star formation history since it will have major impact on the SED. The chemical enrichment of metals is closely related to the star formation history and are described in more detailed in section Chemical composition The chemical composition of a galaxies will change with time due the activity of stars. During the different phases a star goes through, they will enrich the surrounding with heavier elements. This will change the chemical composition in the galaxy and effect the shape of the SED. How the chemical composition changes with time is closely related to the star formation history since the replenishment of the ISM comes from stars in the galaxy, see figure 3.2. When talking about the chemical composition of astronomical objects a common term used is metallicity (Z) which is the mass fraction of metals. Together with the mass fraction of hydrogen (X) and helium (Y ) one covers the whole range of elements (with Z + X + Y = 1). One often use the solar metallicity Z = as a reference, with X = and Y = (LeBlanc, 2010). The metallicity is an important attribute since it will effect the properties of a galaxy. Metals, molecules and dust grains are efficient cooling agents, vital for the ability of collapsing cloud to continue contracting. The amount of metals can also indicate how old an stellar object is since the metallicity is constantly increasing with time as more heavy elements are produced. 32

43 3.2.6 Dust Dust refers to small grains in a range of sizes from a few molecules up to 0.2 µm. The grains are mainly made out of C, O, M, Si, S and F (Draine, 2011). The presence of dust in galaxies affect the measured SED and is a very important component. When making a model SED it is thus very important to include the effect of dust since it absorb optical light very effectively. Usually when one describe the effect of dust one make a distinction between attenuation and emission, see figure 3.2. Attenuation is the gradual loss in intensity due to extinction and scattering in and out of a given line of sight. The extinction in a line of sight can be determined by observing an object and then compared with the expected spectrum if there were no obscuring of the light. The geometrical distribution of dust must also be take into account when modelling SEDs of galaxies. If for example a star is totally embedded in dust, it can be that all the light in the UV and visible is absorbed. When dust absorbs and scatters starlight it gives rise to interstellar reddening since blue light is much more strongly attenuated than red light, so extinction causes objects to appear redder than expected. The attenuation of dust is usually characterised by the extinction, A λ at wavelength λ, defined as A λ mag = 2.5log 10 [ F 0 λ /F λ ], (3.1) where, Fλ 0 is the flux that would have been observed with no dust present and F λ is the observed flux. It is common to measure the extinction in certain photometric bands, such as the V -band ( 547 nm), denoted by A V. The extinction when measured in magnitudes is also proportional to the optical depth τ λ as, A λ mag = 2.5log 10 [e τ λ ] = 1.086τ λ. (3.2) The optical depth is another measure of absorption in the line of sight and is defined as, dτ ν = κ ν (3.3) 33

44 where κ ν is the absorption coefficients in the radiative transfer equation di ν = j ν ds κ ν I ν ds and I ν is the specific intensity and j ν emissivity. It follows that the specific intensity along the path of a ray falls as I ν (τ ν ) = I ν (0)e τν, (3.4) where I ν (τ ν ) is the observed intensity and I ν (0) is the emitted intensity. Emission for dust grains is due to fact that the dust grains are heated up and release thermal radiation. The emission from dust is the dominating part of the IR radiation of a normal galaxy. 3.3 MAGPHYS, SED fitting code MAGPHYS 3 - Multi-wavelength Analysis of Galaxy Physical Properties is a code designed to fit the observed spectral energy distribution spectra and estimate physical parameters of the galaxy (da Cunha et al., 2008). The code is able to interpret multi-wavelength observations of galaxies (912 Å λ 1 mm) and uses a Bayesian approach to fit UV, optical and near-infrared galaxy spectra. The fit with MAGPHYS is done in two steps: 1. Assemble a library of models SEDs at the same redshift and in the same photometric bands as the source is detected in. 2. Determine the likelihood distribution of the free parameters, through comparison with the observed SED and the created model library. There are two different versions of the MAGPHYS codes and they are described in section and The only difference between the versions are the optical library which have been generated using slightly different models. Through this thesis only the version refereed as MAGPHYS v2 will be used, and will thus in chapter 4, 3 Publicly available at 34

45 5 and 6 be called MAGPHYS. The difference between this two versions are important when fitting high-redshift galaxies. A important investigation and discussion between the two versions are made in appendix A where the main conclusion is that only MAGPHYS v2 is suitable for studying high-redshift galaxies and is thus the version used in this thesis Libraries of SEDs The library files are contained in two different sets of libraries, optical and infrared spectra. The optical libraries are derived from the spectral evolution of the stellar population calculated using the a new version of Bruzual and Charlot (2003) population synthesis code. The population synthesis code computes the light produced by the stars in galaxies, for different metallicities, star formation histories and uses a Chabrier (2003) IMF. It also includes the effect of dust extinction by applying the model from Charlot and Fall (2000), which takes into account the fact that stars are born in dense molecular clouds (refereed to as stellar birth clouds) which dissipates over time-scale of 10 7 yr and the starlight of young stars is attenuated by both the ambient ISM and birth clouds than the older population of stars is only attenuated by the ISM. The star formation history is described by an underlying continuous model of exponential declining form and added to this is random bursts of star formation which are constant over a time t burst uniformly distributed between and yr. The infrared libraries are calculated using a model presented in da Cunha et al. (2008). It relies on a simple assumption that the infrared luminosity from galaxies is due to dust emission. The total dust emission from a galaxy is computed as the sum of the emission from dust in the ambient ISM and from the emission of stellar birth clouds. The emission of the birth clouds are described as the sum of 3 components: a fixed Polycyclic aromatic hydrocarbons (PAHs) template spectrum, mid-ir continuum emission from hot grains fixed to a combined black body spectrum of two temperatures 130 and 250K 35

46 and a component of grains in thermal equilibrium with adjustable temperature in range K. The emission form the ambient ISM has a fixed ration between the three components (PAHs, hot grains and warm grains in thermal equilibrium) to reproduce the diffuse emission seen in the Milky Way, but include a fourth component of cold grains in thermal equilibrium with adjustable temperature in range K. Detailed description on how the emission of these different components are calculated can be found in section in da Cunha et al. (2008). By varying the adjustable model parameters (Temperature of warm grains TW BC, temperature of cold grains TC ISM, relative ration of cold dust in ISM ξism C, relative ratio of PAHs, mid-ir and warm grains in birth clouds ξpah BC,ξBC MIR and ξbc W ) from the prior distribution one gets a library containing different model infrared spectra. In total, the library contains stellar population spectra and dust emission spectra. A complete list of all 16 free parameters of MAGPHYS is given in table 3.1, for a more detail description see (da Cunha et al., 2008) and the technical documentation of MAGPHYS 4. The optical and infrared spectra are combined using the main underlying assumption that the energy of the obscured starlight is due to dust and is then equal to the energy radiated by dust in the infrared (i.e. energy conservation). This means that they assume also that starlight is the only source for dust heating in the galaxy. The final best fit will be a combination of 2 SED one from each of the two libraries, respecting the energy balance. The code does not include the possible effects from active galactic nucleus (AGN) which can contribute to the heating of dust. Dust self-absorption is also not included (i.e. dust can be optically-thick to its own radiation). To account for this a more complete radiative transfer model needs to be included in the calculation. 4 files/readme.pdf 36

47 Parameter T BC W T ISM C τ V µ L dust f µ ξc ISM ξpah BC ξmir BC ξw BC γ M tf orm Z A t burst Description Temperature of warm grains in thermal equilibrium in birth clouds Temperature of clod grains in thermal equilibrium in ISM Total effective V -band absorption of dust seen by young stars in birth clouds Fraction of τ V contributed by dust in ISM µ = τv ISM /(τv ISM + τv BC) Total luminosity absorbed by dust Fraction of L dust contributed by dust in the ISM Relative ratio of cold dust in ISM Relative ratio of PAHs in birth clouds Relative ratio of hot mid-ir in birth clouds Relative ratio of warm dust in birth clouds Star formation time-scale parameter Stellar mass Age Metallicity allowed range times solar abundance Relative mass of bursts M burst /M cont Time of a burst Table 3.1: All 16 free parameters of in MAGPHYS. 37

48 3.3.2 Fitting procedure MAGPHYS uses a Bayesian model approach to find the best fit correspondence between observation and model values. The code creates two files starformhist cb07 z*.lbr and infrared dec08 z*.lbr (* stands for the redshift rounded to 4 decimals) for each galaxy containing the model libraries at the redshift of the source. The files contain the model flux in the same bands as provided in the input file and they are calculated from the different model spectra and filters used in the observation. This make it possible to compare the observed flux and model flux (which has been shifted and modified to the same conditions as the observed flux). The fit is performed by finding all the optical libraries and all models in the infraread which fulfill fµ IR = fµ sfh ± δf, with δf = 0.15 and where fµ IR is the fraction of total dust luminosity contributes by dust in the ambient ISM calculated from the the infrared fit and fµ sfh is the fraction of total stellar luminosity absorbed by dust in the ambient ISM calculated from the optical fit. For each combination of optical and infrared libraries the goodness of the fit χ 2 is calculated. χ 2 j = N obs i=1 ( ) L obs i a j L j 2 i σi obs, (3.5) with L obs i being the observed luminosity in the ith band, σi obs the uncertainty for the observation in the ith band, L j i is the luminosity of the j model in the ith and a j = ( Nobs i=1 L obs i σ obs i L j i 2 ) ( Nobs i=1 ) 2 L j i σ obs i 1 (3.6) is a scaling factor that minimizes χ 2. For the best fit model the corresponding probability density functions are build by computing the probability as exp( χ 2 /2). 38

49 3.3.3 Version 1 The version of MAGPHYS which in this thesis will be refered to as MAGPHYS v1 is the standard version publicly available 5 but where some minor modifications have been made. The filter files have been updated to includes a larger library of transmission filters and was provided by the author Elisabete da Cunha 6. More precisely, the files FILTERBIN.RES, filter.dec and filters.log have been replaced in the MAGPHYS packages by newer versions. The standard version of MAGPHYS have been tested and calibrated for local galaxies and only very recently have a new version been released which have been calibrated for high-redshift galaxies (z > 1). The code has also been altered in an attempt to make the fitting procedure more effective. Instead of creating the libraries for each galaxy every time it is fitted, the model libraries are calculated ones for a redshift grid of size z = 0.005, in the range 0.3 z 2.5. The model libraries are calculated for the exact set of filters that will be provided in the observation file, see table 4.1 and 4.4. The number and type of filters must be the same when generating the library files and preforming the SED fit. If the libraries are generated with a larger set of filters (to make the library more general and not catalogue specific) it may cause problems with the fitting procedure. The best-fit index of the infrared and optical models will change and may result in a fit which is not as good as it could be. The code has also been modified to include the age of the galaxies. This is one of the free parameters calculated during the fitting procedure but not included in the standard output files. So the only change is that the parameter called tform is included in the name.fit output file. 5 MAGPHYS available at 6 cunha@mpia.de 39

50 3.3.4 Version 2 This version of MAGPHYS includes a new set of libraries 7 presented in da Cunha et al. (2015). These libraries include new optical libraries which have been created using new set of priors more appropriate to fit high-redshift (z > 1). Instead of using the new version of the population synthesis code, they have gone back to the original code of Bruzual and Charlot (2003), which is more representative of the late stages of evolution of stars (in particular AGB phase). The new libraries were released in middle April 2015, which was late in terms of the time scale of this thesis and this version, calibrated for high-redshift galaxies is the only one used for the result in this report. The only change one has to make is to either add the new binary files OptiLIB bc03.bin and OptiLIBis bc03.bin and change the path to the environment variables in the.magphys tcshrc file. Or it is now also possible (since August 2015) to download a complete package for fitting galaxies at (z > 1). This new version will be referred to as MAGPHYS v2 in appendix A, and included the same optimizing modifications as MAGPHYS v1 described in section MAGPHYS v2 also include the age (tform) as an output parameter. 7 New libraries available at 40

51 Chapter 4 Samples and Method 4.1 Data COSMOS Cosmic Evolution Survey (COSMOS) 1, is a survey designed to probe the formation and evolution of galaxies (Scoville et al., 2007). It covers a 2 square degree equatorial field. This field has been further studied with many different telescopes resulting in many catalogues (e.g. Capak et al., 2007; Ilbert et al., 2009) which include data in various wavelength intervals and photometric bands. In this thesis the deblended NMBS (NEWFIRM Medium-Band Survey) catalogue 2 of Whitaker et al. (2011) will be used and is based on the 30 bands catalogue of Ilbert et al. (2009), but this version included additional J and H-bands. This catalogue was chosen as complementary FIR observation are available, which is important in order to cover the star and dust emission simultaneously. The FIR catalogue includes Spitzer MIPS (24 µm) and Herschel PACS (100 and 160 µm) and SPIRE (250, 350, and 500 µm) photometry Publicly available at Products.html 41

52 The two COSMOS catalogues are combined using TOPCAT 3 (Taylor, 2005). TOPCAT provides tools to identify objects within catalogues and match objects between different catalogues based on for example their coordinates. In case of the NMBS and the far infrared catalogue, the cross matching is straightforward as the sources have been assigned the same ID numbers in both catalogues. This reduces the possibility of mis-matching sources. The match is made between the photometric UV-NIR catalogue and the MIR-FIR catalogue. The spectroscopic and/or photometric redshift for the sources in these catalogues are also available 4 and are matched to the combined catalogue. The matched optical-fir COSMOS catalogue includes 36 photometric bands and 31,306 sources. Some of the bands included are multiples, meaning the same wavelength is measured but with two different telescopes/instruments. Due to this, not all available bands will be used in the SED fit with MAGPHYS. The full set of photometric bands used in this thesis are given in table 4.1. All galaxies in the catalogue are not always well-detected in every band, and for a substantial number of sources the FIR bands have no or only a few IR detections. PACS sample The PACS detected galaxies of the COSMOS catalogue is selected within the redshift range 0.3 z 2.5. The galaxies are selected by implementing a detection 5σ limit of 8 and 17 mjy at 100 and 160 µm, respectively. This limit is chosen to be consistent with Rodighiero et al. (2011), so possible comparisons can be made. The galaxies that fulfill the detection criteria in both or either one of the PACS100 and PACS160-band are selected. The selected sources must also have a 5σ detection in the U, B, V and K s bands. This is to make sure that the sources are robustly detected over the whole considered wavelength range. This selection results in a sub sample 3 Publicly available at mbt/topcat/ 4 Publicly available at Products.html 42

53 Filter name Telescope/instrument λ eff [µm] ID in filter log U CFHT/Megacam B Subaru/Suprime-Cam V Subaru/Suprime-Cam Rp Subaru/Suprime-Cam Ip Subaru/Suprime-Cam Zp Subaru/Suprime-Cam J1 NOAO/NEWFIRM J2 NOAO/NEWFIRM J3 NOAO/NEWFIRM H1 NOAO/NEWFIRM H2 NOAO/NEWFIRM Ks CFHT/WIRCAM IRAC1 Spitzer/IRAC IRAC2 Spitzer/IRAC IRAC3 Spitzer/IRAC IRAC4 Spitzer/IRAC MIPS24 Spitzer/MIPS PACS100 Herschel/PACS PACS160 Herschel/PACS 153,9 171 SPIRE250 Herschel/SPIRE SPIRE250 Herschel/SPIRE SPIRE500 Herschel/SPIRE Table 4.1: The 22 filters used for the COSMOS data when performing SED fitting with MAGPHYS. The last column indicate the ID number used in filters.log file for each filter. 43

54 Filter rest-frame wavelength [µm] redshifted wavlength λ=0.65 [µm] U Rp V Zp J H2 Corresponding redshifted filter Table 4.2: method Table of the redshifted filters for the U V J selection of 456 PACS detected sources. In order to have a sample of sources which are detected in the whole range of photometric bands an additional detection is implemented. That is, the requirement that the sources also should be detected in 5 of the IR bands (MIPS, IRAC and SPIRE). This sample will be refereed to as 5 IR detected sources and the size of the sample decrease to 313 5IR-PACS sources. UVJ sample This sample of galaxies are selected using the UV J method described in section Since the UV J selection is based on the rest-frame colour of galaxies the selection had to be modified for this sample. The reason for this is that only the observed wavelengths are provided in the catalogue and not the rest-frame wavelength. Before the U V J selection criteria was implemented a redshift selection in the range 0.3 z 1.4 was made. This was implemented by requiring that either the spectroscopic or photometric redshift fulfills the redshift requirement, leaving 14,185 sources. Second, the U V J bands are redshifted and we make use of different bands, see table 4.2 to be able to use this criteria of selection. By redshifting the U, V and J-band one can obtain which these band corresponds to at observed wavelength, see table 4.2. The U V J method works best for lower redshift (z 1) since two regions of galaxies in the U V - V J plot are not as distinct for higher redshifts. Because of this, the 44

55 rest-frame wavelength of the U, V and J-band are shifted by (1+z), with z = 0.65, the median of 0.3 z 1. The corresponding bands at observed wavelength are the Rp, Zp and H2-bands. The UV J selection is thus transformed into a RpZpH2 selection. The observed flux is converted to AB magnitude and the (Rp Zp) AB and (Zp H2) AB colour are calculated and plotted in a colour-colour diagram, see figure 4.1. A single cut is made in the color-color plot to separate the passive and star-forming galaxies. The cut is chosen in such a way that the distinct separated region of passive galaxies and the intermediate region consisting of two bumps in the upper part of the star-forming region are separated from the rest of the star-forming galaxies. The cut is optimized visually and the result is shown in figure 4.1. An additional criteria is also implemented, 0 < (Rp Zp) AB < 2.5 and 0 < (Zp H2) AB < 2.5, to be consistent with the UV J selection in Williams et al. (2009). The final sample is obtained with a 5σ detection criteria in the V, B, U, Rp, Zp, H2 and K s -band. The reason for this is to make sure that the sources are robustly detected. This result in a UV J selected sample of 8445 sources. If a 5IR detection criteria should be met, the sample decreases to IR-UVJ sources. BzK sample This sample consists of high-redshift star-forming galaxies and where galaxies are selected using the BzK method described in section Before the color selection is applied to the sample, a redshift selection is made. Only galaxies in the range 1.4 z 2.5 are kept, since this is the high-redshift interval chosen to be studied in this thesis work. The AB magnitude in B, Zp and K s band is calculated and the galaxies that fulfils (Zp K s ) AB (B Zp) AB > 0.2, (4.1) are selected, see figure 4.2. A 5σ detection in the B, Zp and K s - band is also required as well as in the U and V -band. These results 45

56 2:4 2:2 2:0 1:8 1:6 Rp Zp 1:4 1:2 1:0 0:8 0:6 0:4 0: :2 0:4 0:6 0:8 1:0 1:2 1:4 1:6 1:8 2:0 2:2 2:4 Zp H2 Figure 4.1: Rp Zp vs Zp H2 (AB magnitude) colour diagram, star-forming and passive galaxies split up in two regions. The blue squares passive galaxies and red filled circles are the selected starforming galaxies. 46

57 ID redshift Number of sources PACS 0.3 z IR-PACS 0.3 z UVJ 0.3 z IR-UVJ 0.3 z BzK 1.4 z IR-BzK 1.4 z COSMOS-5IR (PACS+UVJ+BzK) 0.3 z Table 4.3: Summary of COSMOS samples used in this thesis. in a sample of 2461 sources. When an additional criteria of 5IR detection is made the 5IR-BzK sample includes 326 sources GOODS-S GOODS-S (Great Observatories Origins Deep Survey-South) 5 is a survey designed to probed most distant galaxies. It is centred around the Chandra Deep Field South (CDFS) and covers an area of 173 square arcmin. This field has been studied with many different ground based and space based telescope in a wide wavelength range make it possible to grate a extensive multivawlength catalogue form ultraviolet to mid-infrared. This field has also been observed in the far-infrared with the Hershel space telescope resulting in a catalogue 6 containing observations in the photometric SPIRE and PACS bands. In this thesis, the GOODS-S catalogue 7 of Guo et al. (2013) containing 34,930 sources is used. The photometric catalogue is combined with a catalogue 8 con Publicly available at 7 Publicly available at access/goods- S.html 8 Publicly availible at v3.0.dat 47

58 3:0 2:5 2:0 Zp Ks 1:5 1:0 0:5 0 0:5 0:5 0 0:5 1:0 1:5 2:0 2:5 3:0 3:5 4:0 B Zp Figure 4.2: B Zp vs K s Zp (AB magnitude) colour diagram. Red filled circles correspond to the BzK selected sample with 5σ detection in the U, V, B, Zp and K s -band and 5IR detection. Blue squares are the sources which are not classified as BzK galaxies in the interval 1.4 z 2.5, these are not used in the analyse. 48

59 Filter name Telescope/instrument λ eff [µm] ID in filter log U VLT/VIMOS B (435w) HST/ACS V (f606w) HST/ACS R (f775w) HST/ACS I (f814w) HST/ACS z (f850lp) HST/ACS J HST/WFC H HST/WFC Ks VLT/ISAAC IRAC1 Spitzer/IRAC IRAC2 Spitzer/IRAC IRAC3 Spitzer/IRAC IRAC4 Spitzer/IRAC Table 4.4: The 13 filters used for the GOODS-S data when performing SED fitting with MAGPHYS. The last column indicate the ID number used in filters.log file for each filter. taining the spectroscopic redshift of sources in the CDFS containing the redshift of 7,581 sources. The sources in the two catalogues are match to each other by their coordinates. Using the provided coordinates a sky match in done using TOPCAT and allowing a 1 arcsec error, resulting in a subset of 2128 sources common for both catalogues. PACS sample Galaxies are selected from the GOODS-S catalogue if they are detecting in one or both of the PACS100 and PACS160 bands. A detections limit of 5σ is chosen, corresponding to a limit of 8 and 17 mjy at 100 and 160 µm, respectively. Only galaxies in the redshift range 1.4 z 2.5 are selected using the provided spectroscopic redshift. This results in a PACS detected sample of 152 sources. 49

60 ID redshift Number of sources PACS 1.4 z BzK 1.4 z Random 0 < z 400 Table 4.5: Summary of the GOODS-S samples used in this thesis. BzK sample First galaxies in the range 1.4 z 2.5 are selected, then the B, z and K s flux are converted into AB magnitudes and the B z and z K s color is calculated. Star-forming galaxies are selected if the BzK criteria (Zp K s ) AB (B Zp) AB > 0.2, (4.2) is fulfilled. This results in a sample of 491 BzK sources. 4.2 Robustness of SED fit and selecting reliable fits This thesis is a statistical study of galaxies and the data consist of publicly available catalogues of a large number of sources. It takes an enormous effort in gathering the data, calibrating and extracting the photometric flux in all of the bands. However, even if this job is done in a very good way, statistically, there will always be some parts of the catalogue where the obtained fluxes are less reliable. This is partly due to the complexity in identifying and matching objects given the decrease of resolution with increasing wavelength. The data used in this thesis will thus contain objects for where the model SED may not be able to fit properly. There are also many other limitations in fitting model SEDs to galaxies which have to counted for and the first important part if to recognise and understand where 50

61 these limitations arise from and how it impacts the SED fitting. It is then possible to select only the galaxies which are more likely to have a good SED fit and for which the estimated physical parameters can be more or less trusted. The following are some of the most important limitations in SED fitting: Blending and resolution Photometric coverage High χ 2 Restrictions in underlying models Each of these limitations will be described in more detail in the following subsections Resolution and blending The sources in catalogues are usually observed using different types of telescopes, both ground and space based. This is due to the wide wavelength range the sources are detected from optical to far infrared, for which is not possible to obtain only using a single instrument. All telescopes have a limitation in resolution which is primarily due to the finite aperture size and the observed wavelength. For a single telescope the resolution, θ is given as, θ = 1.22 λ D, (4.3) where λ is the wavelength of observed photons and D is the diameter of the telescope. Thus by increasing the telescope size a better resolution is obtained for a fixed wavelength. This also implies that to study longer wavelength with a high resolution the telescope size needs to increase. The increase in telescope size is a technical limitation since there is a limit in how big telescope can be constructed 51

62 (a way of overcoming this limitation is by interferometry). Groundbased telescopes are also limited by the atmosphere, due to the fact that the atmospheric opacity is very high in most of the electromagnetic spectrum. It is only transparent for optical and radio emission. When observing in these wavelength ranges the imaging is still effected by atmospheric seeing, which is the blurring of astronomical objects caused by turbulence in the atmosphere. A way to decrease the effect of seeing is to build telescopes at high altitudes where the atmosphere is thinner. Space-based telescopes overcome the problems caused by Earth s atmosphere but they are on the other hand very limited by the aperture size. To get good resolution is thus still a challenge, especially for long wavelengths. The necessary resolution to resolve a galaxy depends on the angular size, θ of object which is given by θ = D/D A (4.4) where D is the proper length of the object and D A is the angular diameter distance. The D A is dependent on the cosmological model and can be calculated using the relation D L (z) = (1+z) 2 D A (z) and the analytical fit Eq. (2.12) to the D L described in (Pen, 1999). Figure 4.3 shows how the angular size varies for a fixed half light diameter of 8 kpc (typical size of a galaxy like the Milky Way) is evolving with redshift. This shows how the observed size on the sky of a galaxy change depending on the distance it is located from Earth. For instance, at z 1 the angular size corresponds to 1 arcsec. In order to resole the galaxy it is preferably to have 3 pixels acrosee the object, which in this case corresponds a resolution of θ 0.3 arcsec. Table 4.6 shows the range and resolution of a few different instruments. In order to resolve the source in the example it was necessary to have a θ 0.3 arcsec resolution and from this example of instruments it is clear that only the measurements in optical (Hubble) will be able to resolve a typical galaxy at z 1. So the best infrared instrument present today are not able to revolve individual sources at high-redshift. 52

63 10 8 angular size [arcsec] redshift Figure 4.3: Angular size evolution of a galaxy of half light radius of 8 kpc. Telescope/instrument wavelength aperture angular resolution Hubble/WFC µm 2.4 m arcsec Spitzer/IRAC 3 8 µm 0.85 m 1 2 arcsec Herschel/PACS µm 3.5 m 5 9 arcsec Herschel/SPIRE µm 3.5 m arcsec Table 4.6: Example of resolution of a few different instruments over a wide wavelength range. 53

64 Blending is due to the observational resolution at different wavelength and occurs when objects are matched to each other. Figure 4.4 shows the same source observed with different instruments and is an example of blending. At shorter wavelength the object is observed with higher resolution and it is possible to single out several objects close to the galaxy centred in the magenta circle with a radius of 5 arcsec. At longer wavelength the sources start to smear out and it is not as easy to decide how much of the measured flux belongs to the right objects in the higher resolution observations. To account for this effect the catalogue must be deblended. In the case there are several sources at shorter wavelength associated to the same detection at longer wavelength, it is possible to determine the luminosity ratio between the individual sources at lower wavelengths. Depending on the brightness of the individual sources, if one source is dominant all the flux at longer wavelength may be associated to this source or in the case the different source are of similar brightness the same luminosity ratio can be applied to the longer wavelength observation to associate the measured flux to the individual sources at lower wavelength. This process is very complex and even though the deblending is done in the best possible way, there will always be some miss-match between sources. This means that the flux in some of the photometric bands may not belong to the right source or that the flux is too low/high compared to the real value. By studying the model and observed SED it is possible to evaluate if some photometric points may be wrong and by inspecting the image. Figure 4.6 shows and example of this where some of the outlying observations in the IR may be due to blending or mis-matches. This way of checking the fits will however not be done in this thesis since this is a statistical study looking for general trends in large catalogues, and the sources are not inspected individually. This effect will however result in that part of the sample may not be fitted properly. 54

65 Figure 4.4: Example of blending, magenta circle show a 5 arcsec radius centred on a galaxy. 55

66 4.2.2 Photometry coverage Since MAGPHYS is constructed out of two parts, optical and infrared spectra, it is desirable to have photometry covering both parts of the electromagnetic spectrum. Detection in a broad range of the electromagnetic spectrum for a source thus allows in general for a more reliable constrained SED fit. If there is no detection in either of the MIPS, PACS or SPIRE bands, this means that the infrared part of the SED is not observationally constrained. Due to the fact that many different infrared spectra fulfill the energy balance equation for a certain optical spectra, it is important to have photometry constraining the IR. Even in the case of a few (1-3) infrared detections, it can still be difficult during the fitting procedure, finding a good match between optical and infrared spectra compared to observations given the high number of free parameters in MAGPHYS. Since the optical part of the spectrum is in general observed in many photometric bands, this part has a large weight and the observation in the infrared part may be neglected to some extent. An example of this is shown in figure 4.5, where the galaxy has been detected in the IR MIPS 24µm and PACS 160 µm band. The model SED fits the observed flux in optical bands and MIPS 24µm well, but the observed flux in PACS 160µm is deviating considerably from the best model value. When studying the model SED compared with the observed SED it becomes clear that for sources with few IR detections, the model SED usually does not fit the observations as well as for the optical part. Since the IR part of the spectrum is very important, it is necessary to have good observational IR coverage. By requiring a certain amount of detections in the IR the remaining sources should have a better IR coverage. When implementing a 5 IR band detection requirement only 14% of the sample is left. The COSMOS catalogue includes a large set of sources. Even in the case of only selecting the galaxies which have 5 IR detections, there are still a large number of sources left. Good photometry coverage is important to constrains the SED fit, but does not imply that the best model SED is good. An exam- 56

67 z= χ 2 = F ν [mjy] λ [µm] Figure 4.5: Example of a SED fitted galaxy from COSMOS (ID: 1084) where the IR part does not match observation. Blue line shows best SED fit and red points is the observed data in 16 bands with 1σ errorbars, red arrows show the 1σ upper limit in the cases of S/N< 1. 57

68 ple of this can be seen in figure 4.6, where a SED has been fitted with MAGPHYS. The infrared part of the spectrum is observed in all but the SPIRE 500µm band, but the best fit SED is not good. One reason for the bad fit might be due to problem arising for the calibration of the catalogue or due to blending of sources. The COSMOS catalogue is de-blended, which means sources have been identified and matched between the different photometric bands, but there is always cases when a mis-matched might have occurred, due to the large difference in beam size between the different instruments. This can explain why, for example the SPIRE and PACS photometry sometimes does not seem to be consistent Goodness of fit This is a statistical study of star-forming galaxies and it is important to know how many sources within the sample are not robustly fitted and do not have reliable results. It is not possible to study each galaxy in details, instead galaxies for which physical parameters are not robustly constraint or if the SED fits is not good, can be singled out by studying a few critical aspects within the fitting procedure. To understand which restrictions arise during SED fitting, three different galaxies will be studied in more details. In general the χ 2 is an indicator of the goodness of the model fit compared to observations. A high χ 2 value corresponds to a model which deviates strongly for the observed SED. To define a general fixed χ 2 limit for which a fit is considered as unreliable is not possible since the χ 2 depends on the quality of the data and the number of data points used during fitting. One way to decide if the fit is reliable or not is by examining the fitted SED compared to the observed SED. An example of this is shown in figure 4.7, where the best fit model SED for this galaxy has a very high χ 2 = 32. By studying figure 4.7 is it clear that the SED if not well fitted for several of the photometric bands. The 4 IRAC band deviates strongly from the best fitted SED as well as the PACS 100 µm and the three SPIRE bands are not fitted either. The error is relatively small for all of the 58

69 z= χ 2 = F ν [mjy] λ [µm] Figure 4.6: Example of SED fitted galaxy from COSMOS (ID: 2107) detected in 21 bands up to SPIRE 350µm but for which the best fit SED is not goog in the IR, even though the data contains 5 IR bands. The blue line shows the best fit SED and red points is the observed data with 1σ errorbars, red arrow indicate the 1σ upper limit in the cases of S/N< 1. 59

70 observed fluxes, this is one reason why the χ 2 is high, since if the model fit deviated slightly from the 1σ uncertainty of the observed flux the χ 2 will increase rapidly. The probability density function (pdf, function that describes the relative likelihood for a variable to take on a given value) is shown in figure 4.7 for ssfr, M, L dust and fµ IR. These are 4 of the free parameters that are constrained during the fitting procedure. For ssfr, M and L dust the pdf have a quite strange shape, since it would be expected that the likelihood distribution of a constrained parameter would have some type of distribution, maybe Gaussian. The red, dashed lines in figure 4.7 shows the 1σ uncertainty and judging from that ssfr, M and L dust seem to be very tightly constrained. Due to the bad fit and the high χ 2 it is questionable to have free parameters constrained in such a way. There is always an uncertainty associated to a measurement and model with parameters fitted with a uncertainty of 1σ 0 is unrealistic. Because of this, galaxies that show distributions like that of figure 4.7, have to be dealt with caution, since the fitted parameters are to some extent not reliable. An explanation why the model was not able to reproduce the observed SED of this galaxy is because there might be an AGN component to this galaxy. The reason for asuming this is because of the very high flux in the four IRAC bands and since MAGPHYS dose not include a model for AGN, it will not be able to fit galaxies with an AGN. It could also be that the IRAC bands have been mis-matched or there have been some calibration issue. A high χ 2 usually indicates that the best fit model SED is deviation from the error of the observed flux. In figure 4.8 the distribution of the the χ 2 divided by the number of bands used to fit the sources for the COSMOS-5IR sample is shown. This parameter is plotted instead of the ordinary χ 2 because that value is dependent on the number of data points and since the number of bands used in the fitting procedure vary for the sources, χ 2 /(number of bands) is therefore a better value to use when comparing the goodness between the sources. But studying the whole sample it is possible to determine which values that are considered as high. In the case of this sample, 60

71 F ν [mjy] z= χ 2 = λ [µm] log(ssfr/yr 1 ) log(l dust /L ) log(m /M ) f IR µ Figure 4.7: SED of a galaxy from COSMOS (ID: 26513) blue solid line show best fit SED and red points are observed data. The pdf of 4 free parameters, ssfr, M, L dust and fµ IR, are shown in the bottom 4 panels, where the dashed red line indicate the 1σ uncertainty of the constrained parameter. 61

72 :01 0:02 0:05 0:1 0:2 0:5 1 2 Â 2 =(number of bands) Figure 4.8: Distribution of χ 2 divided by number of photometric bands with detection, for COSMOS-5IR sample at 0.3 z 2.5, black dashed line show 0.4 cut. the limit is set to χ 2 /(number of bands)> Limitations in underlying models One important limitation to SED fitting is the underlying models, how they are created and which physical processes that have been taken into account. However, it is difficult to quantify exactly how the limitations in the model affect the SED fitting and how they can contribute to a bad fit. The aspect that might limit the SED fit is the parameter space over the allowed range of the free parameters. 62

73 This sets a strict lower and upper bounds and may cause limits during the fitting procedure. In many cases the value of the prior settings are physically motivated but if the real parameters of the fitted objects are not covered by the prior settings this can cause the fit to be bad. If there is no model of the object in question then the code will no be able to find a good fit and in that case the result is not reliable. A reason why the parameter space may not span over a wide enough range is because the object have a value that is more extreme than anticipated. A second reason, the SED fit may be bad because the model libraries are created from a set of physical assumptions but does not take into account the full physics of galaxies. For example, for MAGPHYS the effect of AGN is not included, the code will therefore be unable to provide a satisfying and reliable solution in case of an AGN contribution. The limit in the constructed model libraries are due to the fact that galaxies are very complicated objects and it is not possible, with the present knowledge of galaxy evolution and computational power to generate a complete spectral library for all possible galaxies. To illustrate in further how these limitations may affect the SED fit, a few galaxies will be studies in more details. Figure 4.9 shows the SED fit and pdf of a star-forming galxy. In this case, the model SED is good and follows the observed fluxes quite well. Due to the very small uncertainty in the observation the fit has a slightly high χ 2 value even though the fit might look good. Despite that the fit is good χ 2 = 4.3, the pdf of ssfr, M and L dust have an almost delta like shape. In figure 4.7 the SED fit was clearly not good with a very high χ 2 which can explain why there might have been problems with constraining the free parameters. This is not the case of the galaxy in figure 4.9 and the explanation for this lies elsewhere. A possibility is an effect of the fitting procedure, when perhaps, the code finds a local minima and cannot find a better value. This can lead to one of the parameter getting a fixed value which effect the other free parameters and force them to have a fix value too. Limitations in the underlying models can also be an explanation, that the models are not simply able to reproduce 63

74 the SED of this galaxy. It can also be restrictions in the parameter space and that the prior settings do not cover a large enough regime. To know exactly why the pdf for the galaxy in figure 4.9 has this behaviour is very difficult and beyond the scope of this thesis. Figure 4.10 highlights another example of a fitted SED of a starforming galaxy. In this case the SED fit is good (χ 2 = 0.7) and the best fit model is within the 1σ uncertainty for all except 3 bands, but looks a bit strange for the dust peak in the IR. For this galaxy the pdf have a distribution with a more reasonable shape and different from a delta function, which means that the free parameters are likely to be constrained in a proper way. As pointed out previously, the pdf of the free parameters should not have a uncertainty 1σ 0 but for some of the galaxies the fitting procedure results in pdf with the shape of a delta function. In these cases the fit may not be reliable. It is possible to single out these fits by studying the uncertainty of the free parameters. It turns out that it is only for the 8 parameters, M, ssfr, L dust, tform, τ v, µ, τv ISM and fµ SFH for which this occurs. When one of these parameters have a 1σ = 0, then 90% of the others also have a 1σ By requiring that at least two of the 8 critical parameters (M, ssfr, L dust, tform, τ v, µ, τv ISM and fµ SFH ) have a 1σ = 0, galaxies that might not be robustly fitted can be single out. The reason for requiring that at least two of the parameters have 1σ = 0 it is because in some cases it is only one of the 8 parameters that is constrained in that way but the overall fit is still satisfying. When there are at least two parameters with 1σ = 0 most of the other will also have 1σ = 0 and this criteria will then select the galaxies for which the fitting procedure have likely run into some sort of problems. By combining the two ways, 1σ = 0 for any two of the 8 parameters and require a χ 2 /(number of bands)> 0.4, it is possible to single out many of the sources where the model SED have failed to reproduce the observed SED or constrain the free parameters in a proper way. Applying this criteria to the COSMOS-5IR sample results in subsample of 1, 362 sources. This means that 88% of 64

75 F ν [mjy] z= 2.14 χ 2 = λ [µm] log(ssfr/yr 1 ) log(l dust /L ) log(m /M ) f IR µ Figure 4.9: SED of a galaxy from COSMOS (ID: 12455) blue solid line show best fit SED and red points are observed data. The pdf of 4 free parameters, ssfr, M, L dust and fµ IR, are shown in the bottom 4 panels, where the dashed red line indicate the 1σ uncertainty of the constrained parameter. 65

76 F ν [mjy] z= χ 2 = log(ssfr/yr 1 ) log(l dust /L ) λ [µm] log(m /M ) f IR µ Figure 4.10: SED of a galaxy from COSMOS (ID: 31074) blue solid line show best fit SED and red points are observed data. The pdf of 4 free parameters, ssfr, M, L dust and fµ IR, are shown in the bottom 4 panels, where the dashed red line indicate the 1σ uncertainty of the constrained parameter. 66

77 the sample are likely to have a have a good SED fit. 67

78 68

79 Chapter 5 Results 5.1 SFR estimates The SFR is a free parameter in MAGPHYS, and it is determined through the SED fitting. The SFR is an important property of galaxies and relates to other physical properties, which is why it is an interesting property to study. The main processes for dust heating in normal galaxies is bright UV and optical emitted by young stars. The L dust is therefore to a large extent related to the SFR, since a higher SFR would mean more emission from young stars, which can heat the surrounding dust and gas. Figure 5.1 shows the SFR versus L dust, and it shows a tight correlation (with a few outliers) between the two quantities, where a high SFR corresponds to a high dust-luminosity. The scatter in figure 5.1 is because the dust properties of galaxies are different. The amount of dust is not constant, for instant, a star-forming galaxy with high SFR but with low dust content will have a lower total dust luminosity, just because there is less dust that can be heated up by starlight and radiate photons. The relation between the SFR and L dust has been studied and modeled to estimate the SFR from the L dust have been 69

80 established. One of these is described in Calzetti (2013), where a model-based luminosity-to-sfr calibration is presented, and given as SFR(λ) = C L(λ), (5.1) where L is the IR luminosity in erg s 1 and SFR in M yr 1 are used. The model-dependant variable C = M yr 1 erg 1 s is applied corresponding to an assumed Kroupa (2001) IMF of M and star-formation timescale τ=10 Myr over which the SFR needs to remain constant. The SFR is an important property and it is thus interesting to know how well the SFR is recovered from the fitting procedure. When studying the most extreme star-forming galaxies it is important to know if the constrained SFR is reliable. This is investigated by estimating the SFR from the model-based luminosity-to- SFR calibration of Calzetti (2013), where the output L dust value is approximated to be the total IR luminosity. In figure 5.2, the L dust estimated SFR is plotted against the SED determined SFR for the PACS-5IR and UVJ-5IR samples and the one-to-one relation is indicated by a black solid line. There is no significant difference between the scatter for the two different samples but both are found to deviate from the one-to-one relation in a similar way. The SFR is overestimated for the L dust estimated SFR compared to the SFR determined by MAGPHYS. The deviation is quantified by calculating the mean of SFR(L dust estimated)/sfr (MAGPHYS) were the most extreme outliers have been removed. The black, dashed line in figure 5.2 indicates the best fit mean value of the sample and this shows that the SFR of MAGPHYS is systematically underestimated by a factor of 2.1 compared to the model-based infrared luminosity SFR estimator. In the case when the SFR is determined by L dust the resulting SFR is higher, compared to the SED fitted values. It is important to keep in mind that the conversion factor (the C factor in Eq. (5.1)) used to determine the SFR from the IR luminosity is dependent of the chosen model (i.e star formation history, IMF, and 70

81 SFR [M yr 1 ] 1 0:1 0:01 1e9 1e10 1e11 1e12 1e13 L dust [L ] Figure 5.1: SFR vs dust luminosity estimated by MAGPHYS, full COSMOS-5IR sample at 0.3 z

82 100 SFR ( L dust estimated) [M yr 1 ] :1 0:01 0:01 0: SFR (MAGPHYS) [M yr 1 ] Figure 5.2: SFR estimated by L dust vs SFR determined by MAG- PHYS, blue triangles UVJ-5IR, red filled circles PACS-5IR at 0.3 z 1. The solid black line indicates one-to-one relation and dashed line indicate average deviation from one-to-one correlation of a factor of

83 star-formation timescale). This will vary for different types of galaxies, with different dust content, so to assume the same model for the whole sample may introduce biases. A better way would be to use different models depending on the type of galaxy, but to divide the sample in this way is not possible, due to the fact it is not possible to resolve individual stellar populations. While useful to do a sanity check with the L dust estimated SFR, in the following the SFR determined by MAGPHYS will be used exclusively. Galaxies evolve with time and by studying how galaxies have changed it is possible to get a better understanding of the growth of galaxies. Figure 5.3 shows how the SFR varies with redshift for the COSMOS-5IR sample, where only good fits have been selected (as discussed in section 4.2.3). The SFR is correlated with redshift with the trend, increasing redshift correspond to larger SFR. One reason for this is because there is an observational limit when observing high-redshift galaxies, namely that only bright source with high SFR will be detected since the other galaxies are to faint to observe, this effect is known as the Malmquist bias. This is the reason why the high-redshift source, with low SFR are missing in figure 5.3. On the other hand, the lack of low-redshift sources with high SFR is not due to observational constrains, but have a physical explanation. The reason why low-redshift galaxies have lower SFR may be because there is less gas available in these galaxy and thus less material to form stars out of. The decline in SFR with cosmic time found in this study is in agreement with literature which states that the peak of the SFR density occurs at z 2 (Madau and Dickinson, 2014). 5.2 Stellar mass estimates The stellar mass (M ) is determined by the SED fitting using MAG- PHYS. In order to determine how reliable the estimated M values are, one can compare the SED results with other mass estimate methods. Daddi et al. (2004) provide a stellar mass estimate for 73

84 SFR [M yr 1 ] redshift Figure 5.3: SFR vs redshift, COSMOS-5IR only source with reliable fits. 74

85 BzK (1.5 < z < 2.5) galaxies using K-band photometry. It is given by, log(m /10 11 M ) = 0.4(K tot K 11 ), (5.2) where K 11 is the K-band magnitude corresponding on average to a stellar mass of M and have a value of K 11 =19.51 (AB magnitude). The estimated K-band stellar mass is calculated using the K s -band 1 flux in AB magnitudes. The K-band estimated and SED fitted stellar masses are plotted in figure 5.4, where the solid black indicates a one-to-one relation. The stellar mass is overestimated by the SED fit for massive galaxies (M M and underestimated for M M compared to the K-band estimated stellar mass. The K-band estimated stellar mass is defined for only BzK galaxies and is not representative for the whole COSMOS sample studied in this thesis. The SED estimated stellar mass is on the other hand determined in a more sophisticated manner and is not simply dependent on the flux in a single photometric band. In the following the stellar mass used will be the SED estimated value by MAGPHYS. 5.3 Specific star formation rate The specific star formation rate is the SFR per unit stellar mass and is a inverse timescale. A high ssfr indicates that the galaxy is forming stars at a high rate relative to its mass and can be used to isolate starburst galaxies. Figure 5.5 shows the distribution of the ssfr for the COSMOS-5IR sample, divided into 6 redshift bins. In order to identify and study the most extreme star-forming galaxies in the sample, the top 2% objects with the highest ssfr from each of the 6 redshift bins are selected, a total of 29 source. The reason for choosing the limit at 2% is to make sure that only the most 1 The K and K s-band are very similar but slightly different and where the K s-band cut off at a lower wavelength, but the K s is used to be consistent with the date used for SED fitting. 75

86 1e12 5e11 Mass (K-band estimated) [M ] 2e11 1e11 5e10 2e10 1e10 5e9 2e9 2e9 5e9 1e10 2e10 5e10 1e11 2e11 5e11 1e12 Mass (MAGPHYS) [M ] Figure 5.4: Stellar mass estimated by K-band vs stellar mass determined by MAGPHYS. BzK-5IR sample, at 1.4 z 2.5. The black solid line correspond to a one-to-one relation. 76

87 redshift Total number of sources 2% of sample <ssfr [yr 1 ] 0.3 z < z < z < z < z < z Table 5.1: Total number of sources and number of the 2% most extreme ssfr (starbursts) for the 6 redshift bins and the lower limit of the ssfr for starbursts. extreme objects are included in this selection and lower values than 2% would result in to few sources to statistically make sense. This is a simple way of selecting the very most extreme sources without assuming any distribution and sources selected in this way will hence forth be classified as starburst galaxies. Table 5.1 shows the number of sources in each of the 6 redshift bins and the lower limit of most extreme ssfr. In figure 5.5 the starburst galaxies are shown in red and it is clear that the top 2% only falls at the very end of the tail of the distribution, hence, they are only the most extreme objects. For higher redshift the ssfr seems to be increasing and the lower limit is increasing with almost an order of magnitude different between the most extreme ssfr at 0.3 z 0.6 and 2 < z 2.5. Main sequence of star-forming galaxies The main sequence is a observed relation between SFR and stellar mass of galaxies. Figure 5.6 shows the correlation between SFR and stellar mass for the COSMOS-5IR sample split into 6 redshift bins. The correlation is not as pronounced for the low redshift bins where there is a much larger scatter. One explanation for this may be a observational limit that not all the sources selected at lower redshift are star-forming galaxies. If a few passive galaxies have been included then they would contribute to the scatter. For the 77

88 z < z < z < z < z < z ssfr [yr 1 ] Figure 5.5: The distribution of ssfr, COSMOS-5IR with only sources with reliable fits for 6 redshift bins. Red bins indicate the 2% sources with the highest ssfr. Note that the scales of the y-axis is varying for the three panel rows. 78

89 higher redshift bins the scatter is decreasing and the average SFR is increasing. This should be view upon with caution, since with increasing redshift only sources which have an intrinsic brightness above a certain limit can be detected. At high redshift, only bright star-forming sources are detected and low luminosity passive galaxies are not observed. This can explain why there is a much smaller scatter in the SFR-stellar mass plot for the high-redshift sources. In figure 5.6 it is clear that the main sequence is moving upwards in the SFR-stellar mass plot and the average SFR is increasing with redshift. What this tells you is that not necessarily that the true average SFR for galaxies at high-redshit is higher, since the low luminosity galaxies are missing from the analyse, but the SFR of the most extreme objects are higher. 5.4 Dust properties The presence of dust and gas in galaxies is important for the star formation and is one factor that regulates the SFR. To understand the evolution of galaxies one interesting thing to study is how the gas mass fraction change with redshift. The total gas mass is not provided as an output of MAGPHYS, but the M dust is given, which is instead used to compute the dust mass fraction, M dust /M. Figure 5.7 shows dust mass fraction versus redshift, and there is a large scatter over the whole redshift range 0.3 z 2.5. Starburst galaxies, the top 2% sources with the most extreme ssfr are shown as red stars and scatter at the higher end of the dust mass fraction. In order to quantify if there is an evolution with redshift, the sample is binned in z = 0.1. In figure 5.7 the median for each bin is plotted as yellow squares. The dust mass fraction is not changing much or low redshift, but at z 1.5 the median 2 dust mass fraction starts to increasing with redshift. In order to study how the dust mass fraction evolves with redshift, three curves have been fitted to the 2 Median is more robust against outliers then the mean and is calculated without having to assume any distribution. 79

90 z < z <1 SFR [M yr 1 ] z < z < z <2 2 z Stellar mass [M ] Figure 5.6: SFR vs Stellar mass for the COSMOS-5IR sample, split into 6 redshift bins. 80

91 M dust /M Redshift Figure 5.7: Dust mass fraction vs redshifit, blue filled circles show COSMOS-5IR only sources with reliable fits, red stars are starburst galaxies, magenta squared show the mean of the redshift bins and yellow triangles show the median value for each bin. 81

92 data. Figure 5.8 shows the median values as yellow squares and the tree fitted curves. A linear curve is fitted to the log 10 of the median and is shown as a magenta, solid line in figure 5.8, with the fitted function of the form log 10 ( Mdust M ) = k (1 + z) + m, with best fit values k = and m = A power-law is also fitted to the median values and shown as a dashed, red line, with fitted function log 10 ( Mdust M ) = log 10 (a) + log 10 (1 + z) 2, and best fit value a = Another power-law function is also fitted where power is not fixed to 2, but also a free parameter, ( ) Mdust log 10 = log 10 (b) + log 10 (1 + z) c M with corresponding best fit values b = and c = 2.379, and shown as a green, solid line in figure 5.8. All of the three best fit functions are very similar and it is not possible to excluded any of them. What is clear is that they are all indicating that the dust mass fraction is increasing with redshift, at least up to a z 2.5. How does the dust mass fraction relates to the dust properties? Top panel of figure 5.9 shows that the dust mass fraction is correlated to the temperature of the cold ISM, a high dust mass fraction corresponds to a lower ISM temperature. The starburst galaxies are again indicated by red stars and are all (except one) among the sources which have the highest dust mass faction for a given temperature with average < M dust /M > Bottom panel of figure 5.9 shows the dust mass fraction versus the temperature of the warm gas of stellar birth clouds. There is no correlation between these two parameters and one explanation is that the dust mass is a parameter which relates to the whole galaxy, whether the temperature of 82

93 10-2 M dust /M Redshift Figure 5.8: Dust mass fraction vs redshift, yellow squares shows the median values. Magenta solid line show best linear fit, red dashed line show best power of 2 function fit and green solid line show best fit power-law function with the power fitted to

94 the warm birth clouds is only localized to areas around very young stars. What is interesting is that most starburst galaxies (except 4) have a high T BC W > 45 K. This is consistent which what is expected from galaxies with intense star formation, since a galaxy with high SFR have many young bright O and B stars which heat the surrounding gas to high temperatures. A large part of the sample is clustering at high T BC W, not only the starburst galaxies, but this is also consistent since the sample is chosen to predominantly contain star-forming galaxies. Top panel of figure 5.10 shows clearly that there is no relation between the ssfr and T ISM C. The temperature vary in the allowed range K with no dependence on ssfr. The starburst galaxies are also uniformly distributed, which means that there is no correlation between T ISM C and the ssfr for starburst galaxies. Bottom panel of figure 5.10 shows that the ssfr is also not correlated to T BC W, the temperature of the warm stellar birth clouds. The temperature vary between the allowed values K over the whole range of ssfr. The distribution of starburst galaxies is on the other hand not uniform and they are clustering at higher temperatures. Only 4 out of 29 starburst galaxies have a T BC W < 45 K. 84

95 TC ISM [K] M dust /M TW BC [K] Figure 5.9: T op: Dust mass fraction vs temperature of the cold ISM, blue filled circles COSMOS-5IR only sources with reliable fits and red stars show starburst galaxies. Bottom: Dust mass fraction vs temperature of the warm birth clouds. 85

96 10-7 TC ISM [K] ssfr [yr 1 ] TW BC [K] Figure 5.10: T op : ssfr vs temperature of the cold ISM, Blue filled circles COSMOS-5IR only source with reliable fits and red stars show stardust galaxies. Bottom: ssfr vs temperature of the warm birth clouds. 86

97 Chapter 6 Discussion This is a study of a large sample of 1533 high-redshift galaxies at z = 0.3 to 2.5, where physical parameters have been estimated in a consistent way for the whole sample. The sources in the sample have been selected to have good photometric coverage from optical to far-ir, allowing both the stellar and dust properties of galaxies to be constrained. It has been found that both the SFR and dust mass fraction is increasing with redshift. Galaxies classified as starburst are among the most dusty sources in the sample. A very important part of this study is the model used and the uncertainty of the SED fitting code, which will be discussed and compared to other studies in the following section. In the following the obtained result for star-forming galaxies will be discussed and compared to literature results of the main sequence of star-forming galaxies and methods of isolating the starburst galaxies as outliers. Finally, the focus will be on the dust properties of our sample and how these results compare to previous studies, followed by a short discussion about the general evolution of galaxies found in this work. 87

98 6.1 The model and uncertainties The estimated physical properties of galaxies from SED fitting is highly dependent on the model used, and there is a variety of codes available in the literature, e.g. MAGPHYS (da Cunha et al., 2008, as used here), FAST 1 (Kriek et al., 2009), PEGASE.3 (Rocca-Volmerange et al., 2013) and GRASIL 2 (Silva et al., 1998). Some of the differences between SED fitting codes lies in the possible wavelength range they are able to fit, the underlying stellar populations codes, IMF, star formation histories, AGN component, and dust attenuation. Because of this, depending on which model is used, there can be systematic variations in the results, for example the case for the MAGPHYS estimated SFR (found to be a factor of 2.1 lower then L dust estimated SFR; section 5.1). A comparison study done by Michalowski et al. (2014) between 4 different SED fitting models (including MAGPHYS) have shown that there are differences between each model and that the chosen type of star formation history is especially important for obtaining a good estimate of the stellar mass. In the Michalowski et al. (2014) study, it was found that MAGPHYS overestimated the stellar mass by 0.1 dex compared to the input values used to create the simulated photometry. If the stellar mass is overestimated this can be part of the explanation that the literature definition of the main sequence is not recovered for the sample analysed in this thesis (along with the underestimated SFR) see section 6.2. In figure 6.2 the black line correspond to the main sequence of star-forming galaxies at the given redshift and it is clear that the line is not centred at the distribution of the sample in any of the redshift bins. In the work done by Rodighiero et al. (2014) it is shown that the slope and scaling of the main sequence is highly dependent on the sample and wavelength (UV or FIR) used to estimate the SFR. They find that the SFR(FIR) gives a higher estimate compared to SFR(UV) for starforming BzK galaxies from the COSMOS field. Due to these large 1 mariska/fast Download.html

99 difference in the estimated SFR it is not possible to do an exact comparison with the result found in Rodighiero et al. (2011) and it is therefore not surprising a large difference in the number of extreme outliers from the main sequence is observed. As mentioned earlier, the difference between models and simple, empirical estimates can have a large effect on the obtained physical parameters. What is also worth emphasising is that within the same SED fitting code, the underlying libraries and adopted prior settings can have a considerable impact on the constrained parameters. This has been explored for MAGPHYS and are described in more detail in Appendix A, where it was found that the standard version of MAGPHYS have some limits in fitting high-redshift galaxies. These limitations in the supplied library and with prior setting optimized for local galaxies caused some restrictions when fitting high-redshift galaxies. Figure A.3 shows the comparison between the fitting with MAGPHYS for the original and new high-redshift calibrated libraries and what a large impact a relative small change in the underlying libraries for the same model can have. 6.2 Star-forming galaxies In order to study the evolution of galaxies physical properties have been estimated for a large sample of galaxies. Two of the most important parameters, stellar mass and star formation rate, have been studied in more details to check that they are in agreement with other studies. It has been found that the model SED estimated SFR may be systematically underestimated by a factor 2 but is still reasonable. The estimated stellar mass is also in reasonable agreement with a simple K-band estimate. The most extreme star-forming sources are singled out from using ssfr, where the top 2% in each redshift bin have been classified as starburst galaxies. In Rodighiero et al. (2011) starburst galaxies are classified as outliers, that strongly deviates above the main sequence of star-forming galaxies (2.1), defined by (Tacconi et al., 2013). Thus by calculat- 89

100 redshift Total number of sources MS > 3 MS > 5 MS > z (0.8%) 1 (0.4%) 0 (0.0%) 0.6 < z (2.0%) 3 (0.5%) 1(0.2%) 1 < z (1.1%) 0 (0.0%) 0 (0%) 1.4 < z (0.8%) 0 (0.0%) 0 (0.0%) 1.7 < z (1.0%) 0 (0.0%) 0 (0.0%) 2 < z (0.0%) 0 (0.0%) 0 (0.0%) Table 6.1: Total number of sources and percentage of sample for the 6 redshift bins with MS calculated from the SFR determined by MAGPHYS. ing the deviation MS, from the literature definition of the main sequence, it is possible to localise these extreme objects. The deviation will be defined as, MS SFR obs a (M / M ) p+1 ((1 + z)/2.2) q M, (6.1) where a, p, and q are defined as in section In this way, the deviation MS, is calculated individually for each source at the given redshifts and stellar mass. This opens up the possibility to obtain a way to quantify how strongly a source deviates from the main sequence for the redshift of each source and can thus be applied to the whole sample. In order to compare the distribution of MS with the ssfr distribution from section 5.3, we use the same redshift bins. Figure 6.1 shows the MS distributions and a pronounced feature is that the distribution is not centred at 1 for any of the redshift bins. We remind, the main sequence implies MS = 1. Table 6.1 shows the number of sources with MS > 3, 5 and 10 for each of the redshift bins. In Rodighiero et al. (2011) starburst galaxies are defined as sources deviating more than 10 times MS > 10, above the main sequence and represent 2% of their sample. Due to the low number of outliers found in this study, with only 1 source having MS > 10 our result is not comparable to Rodighiero et al. (2011). One explanation may be connected with the SFR estimated by MAGPHYS which is 90

101 z < z < z < z < z < z MS Figure 6.1: Distribution of MS for 6 redshift bins. Note the scale of the y-axis is varying for the three different panel rows. 91

102 underestimate compared to literature Rodighiero et al. (2011) value. The small scatter and low number of outliers with high MS means that the main sequence is recovered to a high degree for this sample of galaxies over the whole redshift range, 0.3 z 2.5, studied in this thesis. Figure 6.2 shows the galaxies scatter along the main sequence defined as in (Tacconi et al., 2013), indicated as a solid, black line, calculated by Eq. 2.1 for the median redshift value of respective redshift bin. The higher the redshift bin, the less scatter for the main sequence. For the lowest redshift bin, 0.3 z < 0.6, the scatter is very large, and one reason for this may be the low redshift UV J selected sample containing galaxies that are not star-forming. The UV J selection is not very strict, which means that some passive galaxies may be included in the selected sample. The lower redshift bins may be more affected by this since at higher redshift, it is only possible to detect bright star-forming galaxies, which leads to that the main sequence is recovered with a smaller scatter. Since the studied galaxies are required to be detected in the IR, this also limits the sample to objects that are bright in the IR, which corresponds to galaxies with a large amount of heated gas/dust. Again, we remind that,it is not necessary that there are less star-forming galaxies with low SFR at high redshift, but relates to the detection limit of the IR data. The main sequence of star-forming galaxies have been recovered with a relative small scatter and few number of outliers compared to similar studies done by Rodighiero et al. (2011, 2014). The small scatter seen in this sample can maybe be explained by the fact that the SFR have been determined in a consistent way on an coherent sample, where the relative uncertainties of each source small and effected by the same systematic errors. The small scatter seen put rather tight constrains on the relationship between the SFR and stellar mass and that it is possibly very similar mechanism that cover the galaxies. 92

103 SFR [M yr 1 ] z < z < z < z < z < Stellar mass [M ] z log 10 ( MS ) Figure 6.2: SFR vs stellar mass, COSMOS-5IR sample split into 6 redshift bins. Solid black line represents the main sequence at corresponding median redshift value for each bin. Colors indicate the log 10 ( MS ) value. 93

104 6.3 Dust properties The obtained dust mass fraction is found to be increasing with redshift, for the studied range 0.3 z 2.5. In the Milky Way the dust and gas mass is found to be related in such a way that M dust /M gas 0.01 (e.g. Draine, 2011). Assuming that this rough estimate holds for the whole sample and also that the fraction of hydrogen to the total gas mass is relatively fixed, it allows one to convert the dust mass fraction into a gas mass fraction and vice versa. In Geach et al. (2011) and Carilli and Walter (2013) the gas mass fraction has been studied out to z 4, where the gas mass included the molecular component. They have found that for their combined sample, the gas mass fraction follows M gas /M = 0.1 (1+z) 2. In figure 6.3 the gas mass fraction curve of Carilli and Walter (2013) has been converted to a dust mass fraction curve using the relation from (Draine, 2011) and plotted as a black, solid line (M gas /M dust 100). Figure 6.3 shows the same fitted curves as in figure 5.8 plotted over the whole COSMOS-5IR and where the median values (yellow squares) have been included. The magenta, solid line shows the linear fit, and the red, dashed and solid green line is the fitted power-laws, for a power of 2 and 2.4, respectively. The relation found in this thesis is in good agreement with previous result, where the curve found in Carilli and Walter (2013) fits relatively well with the result, considering the very crude assumptions made in order to compare the gas mass fraction to the dust mass fraction. The three fitted functions are very similar and all are within the scatter of the sample. What is clear is that the dust mass fraction of the sample studied in this thesis follows the same trend as the gas mass fraction found in Carilli and Walter (2013). This also indicates that the assumption of a fixed M gas /M dust is consistent for this sample but with a conversion factor of M gas /M dust = 270, a factor of 2.7 higher than in (Draine, 2011) (by scaling the black line on the fitted lines i.e. M dust /M as a free parameter). This is consistent with the result found by Seko et al. (2014) in a study of 3 star-forming galaxies at z 1.4 detected in MIPS 24µm and SPIRE 250 µm and 94

105 350 µm, with a gas-to-dust ratio at of The gas mass fraction has also been studied by Scoville et al. (2014), where the M ISM /(M ISM + M ) have been determined for a sample of 101 galaxies in the range 0.2 < z < 2.5. They have divided there sample in 3 redshift bins, low z = , mid z = and high z = leading to a gas mass fraction 2 ± 0.5%, 12 ± 3% and 14 ± 2% respectively. They also include a sample of 6 IR bright sources at z = giving a gas mass fraction of 53 ± 3%. In order to compare with these result the corresponding gas mass fraction for the sample in this thesis have been calculated as ( ) Mgas M dust M dust ( ), Mgas M dust M dust + M with the use of the M gas /M dust = 270 to approximate the gas (ISM) mass since the total gas mass is not provided as an output. The COSMOS-5IR sample in divided in three redshift bins 0.3 z < 0.65, 0.65 z < 1.4 and 1.4 z 2.5 with the average gas mass fraction 26%, 31% and 43% respectively. A fourth sample is made containing the starburst galaxies selected as the 2% most extreme source and these objects have an average gas mass fraction of 68%. The gas mass fraction is increasing with redshift from 26% at low redshift up to 43% at the highest redshift and the starburst galaxies have an even higher average gas mass fraction. The percentage of the gas mass fraction found in this work is higher then in (Scoville et al., 2014). However, the values obtained in this study are of comparable size to that of the bright IR sample with a gas mass fraction of 53%. Since all sources in our COSMOS-5IR sample have detections in 5 IR bands, they are more comparable to the IR bright sample in (Scoville et al., 2014) and for that sample our result are more similar to what they find. However, very crude approximations have been made in order to calculate a gas mass fraction of the COSMOS-5IR sample so it is not surprising the there 95

106 10-1 M dust /M Redshift Figure 6.3: Dust mass fraction vs redshift for the COSMOS-5IR sample. 3 fitted curves, magenta solid line linear fit, red dashed line power-law function of power 2, green solid line power-law fit best fit power of 2.4 and black solid line indicate the curve of the gas mass fraction found in (Carilli and Walter, 2013) of form M gas /M = 0.1 (1 + z) 2. 96

107 are some differences. Also in Scoville et al. (2014) they have use a calibration to calculate the M ISM from the far-ir continuum to avoiding the need to assume a dust-to-gas ratio. In Scoville et al. (2015) the ISM mass evolution of 180 starforming galaxies at z = 1 to 6.4 have been studied with ALMA cycle 2 observation of the long wavelength dust emission. Similar to the previous study by (Scoville et al., 2014) the gas mass (M ISM ) have determined form the far-ir continuum using an empirical calibrations. They find that the galaxies with the highest ssfr have a gas mass fraction (M ISM /(M ISM + M )) of 50 to 80% at z 2. These results are in better agreement with the gas mass fraction found in this work, of average value 43% at 1.4 z 2.5. Figure 6.4 shows the ssfr versus dust mass fraction (M dust /M ) for the COSMOS-5IR sample (blue filled circles) and starburst galaxies (red stars) selected by their extreme ssfr value. Is is clear from figure 6.4 that the galaxies with highest ssfr also have the highest dust mass fraction and thus also indicating that these objects also have the highest gas mass fraction, in agreement with Scoville et al. (2015). 6.4 Evolution of galaxies The sample of 1533 star-forming galaxies in the redshift range 0.3 z 2.5 studied in this thesis have allowed a large part of the the history of the Universe (from 2.6 to 10 Gyr) to be studied. The sample is large enough in order to enable good statistics of highredshift galaxies. The galaxies have a large photometric coverage from optical to far-ir. The later is very important in order to constrain the physical properties of the galaxies. They availability of Herschel data has really opened up the possibility to probe the dust emission of high-redshift galaxies, which is a very important complement to UV and optical observations. This high quality data have been analysed with a the state-of-the-art SED fitting code MAG- PHYS and physical properties have been constrained in a consistent 97

108 ssfr [yr 1 ] M dust /M Figure 6.4: ssfr vs dust mass fraction, blue filled circles COSMOS- 5IR sample and red stars indicate starburst galaxies selected as the 2% most extreme sources. 98

109 way for the whole sample. Figure 6.5 shows that the ssfr is increasing with redshift up to z = 2.5, with yellow squares indicating the median value of the whole sample in redshift bins of z = 0.1. This is consistent with the peak of the star formation density at z = 2 3 (Madau and Dickinson, 2014). The evolution of the ssfr has been studied in literature up to z 7 where the ssfr is increasing up to z 2 and reach a plateau of constant ssfr 2 Gyr 1 at z = 2 to 7 (Weinmann et al., 2011; Lehnert et al., 2015). Figure 6.6 from (Lehnert et al., 2015) shows how the ssfr evolves with redshift up to z 7 for galaxies of a similar mass range of M M and have been included in order to make comparisons with the result found in this work. The COSMOS-5IR sample have an average mass of M M, considerable higher than in the studies (Weinmann et al., 2011; Lehnert et al., 2015) and in order to compare with these studies, the median have also been calculated for a sub-sample (from the COSMOS-5IR sample) of 103 galaxies with stellar mass in the range M = (0.1 2) M, shown in figure 6.5 as magenta triangles. In figure 6.6 the best-fit relation from Elbaz et al. (2011) fits the collected measurement from literature (ssfr=26 t 2.2 cosmic, where t cosmic is the cosmic time elapsed since the Big Bang in Gyr 1 ). In figure 6.5 the same best-fit relation is shown as a black, solid line and is also in good agreement with the results found in this work, with the median values of the M sub-sample lying just above the best-fit of Elbaz et al. (2011). The dust mass fraction has a considerable scatter but by studying the median value is have also been found to be increasing with redshift. The fitted curve of the gas mass fraction of (Guo et al., 2013) (black solid line in figure 6.3) have a very similar shape compared to the best fitted curves of the dust mass fraction found in this work, see figure 6.3. This might indicate that there is a fix dustto-gas ratio over the whole redshift range for this sample and that the gas mass fraction probably follows a similar dependence as the dust mass fraction. This gives that galaxies at high redshift have higher ssfr and gas mass fraction, possibly explaining the higher 99

110 ssfr [yr 1 ] redshift Figure 6.5: ssfr vs redshift, blue filled circles COSMOS-5IR sample, yellow squares are the median valued of the whole sample, magenta triangles are the median value of galaxies with M = (0.1 2) M and black solid line is the best-fit ssfr=26 t 2.2 cosmic found in Elbaz et al. (2011). 100

111 Figure 6.6: ssfr (in Gyr 1 ) as a function of redshift with points showing various measurements from literature of galaxies at M M, blur line is the best-fit from (Elbaz et al., 2011) in the redshift rage 0 to 2 and red line shows a simple relation of ssfr(z) = (1 + z) 3 /t H0 where t H0 is the Hubble time at z = 0. Credit Lehnert et al. (2015). 101

112 SFR seen at high redshift. The starburst galaxies are among the dustiest objects in this sample, with a very high average dust mass fraction of 2.7% compared to 0.29, 0.31 and 1.0% for the redshift bins low z = , mid z = and high z = respectively. This also indicates that these objects have a higher gas mass fraction which can explain why these galaxies are forming stars at such a high rate. 102

113 Chapter 7 Conclusion This thesis present a statistical study of 1533 high-redshift galaxies and a bench mark of the newest version of the SED fitting code MAGPHYS. With this specific SED fitting code it is possible to analyse the spectrum of galaxies from the ultraviolet to far-infrared in a self consistent way, making it possible to study both the stellar and dust emission. A very important conclusion is that the original version of MAGPHYS is not adapted for high-redshift galaxies and is to some extent not able recover meaningful result for these types of sources. It is there for strongly recommended to use the newest version, calibrated for high-redshift galaxies, for fitting high-redshift sources z 0.3 With the new version of MAGPHYS with stellar libraries of Bruzual and Charlot (2003), it is possible to fit high redshift galaxies in a meaningful way, but special care have to be taken into considerations in evaluating the reliability of the fitted sources. The first important requirement is that it is necessary to have a good infrared photometry coverage. Without, it is not possible to constrain the dust properties of the galaxies. Even with good photometry coverage there are still constraints in the data and the fitting code which 103

114 give rise to some catastrophic cases for which the fitted SED is not in agreement with the observed data. This is manly due to blending of sources and limitations in the model libraries. It is possible to select good fits by requiring that the goodness of the fit χ 2 /(number of bands)< 0.4 and that the probability distributions of the fitted free parameters must have a physical shape, that is a 1σ > 0. With these limits, 12% of the sample are considered as non-reliable and not included in the analyse. 29 starburst galaxies have been single out as the 2% sources with most extreme ssfr. They have been found to have a high average dust mass fraction and high temperature of the stellar birth clouds > 45 K. The main sequence of star-forming galaxies is recovered with a small scatter. There are very few galaxies that deviates strongly above the main sequence as defined in literature. This results in very few galaxies classified as starburst in this way. The SFR recovered by MAGPHYS is a factor of 2 lower than model estimated from the IR luminosity. The low SFR can explain why the main sequence recovered by MAGPHYS is lower compared to literature. Dust emission are constrained by the available Herschel photometry, which make it possible to study the dust properties of the galaxies. The dust mass fraction is found to evolve with redshift, and is deceasing with cosmological time. There is a large scattering for the entire redshift range and by binning the result in bins of z = 0.1 and calculating the median values it is shown that the M dust /M is increasing with redshift. This is in good agreement with previous studies of the gas mass fraction. The dust-to-mass is found to be 270 for this sample of galaxies which is within the range found in previous studies. 104

115 Chapter 8 Outlook SED fitting is a powerful tool to interpret the observed photometric data of galaxy surveys. MAGPHYS is an SED fitting code developed to analyse the spectrum of galaxies from UV to far-ir, which allows for both star and dust emission to be studied in a consistent way. Even though SED fitting is one of the best ways to analyse the data of high redshift galaxies it should be used with caution. The estimated physical parameters are extremely model dependent which have been investigate in Appendix A where different model libraries can greatly change best-fitted result. It would therefore be interesting to do further comparison between MAGPHYS and other SED fitting codes in order to investigate how well the physical parameters are constrained for different models for a high quality sample of galaxies and preferably over a wide redshift range. Other possible codes could be for example FAST (Kriek et al., 2009), PEGASE.3 (Rocca-Volmerange et al., 2013) or GRASIL (Silva et al., 1998). Since SED fitting is a commonly used technique, it is important to understand the limitations of different models in order to obtain a meaningful result, and it would therefore be interesting to do a comparison between different codes. Another way to improve the work presented in this thesis would 105

116 be to increase the sample size, especially at high redshift. There are quite few high-redshift source in the sample which results in poor statistics. One way to improve this would be to add the data from GOODS- South and North which are very deep surveys and included many high-redshift sources. One disadvantage is that, not so many sources in these surveys are detected with Herschel (far-ir) which means that in order to increase the number of high-redshift sources, the dust emission will not be constrained during the fit. An important parameter in the study of galaxy evolution is the redshift. Depending on the redshift, the same measured flux from a galaxy will yield very different properties, since for example the required mass of a galaxy in the local Universe to produce the same amount of light is much less compared to a galaxy at high redshift. This is just one example to highlight the importance of determining the correct redshift of a source. Another reason is to be sure the observed source is placed at the right time in history which is important when trying to understanding the evolution of galaxies. Because of this, and the fact that spectroscopic redshifts are only available for a small number of high-redshift galaxies, it would be interesting to study the photometric redshift vs the spectroscopic redshift. Both to look at how large the difference is between the two redshift estimates for the same source and also to study which effect this will have on the constrained parameters from SED fitting. 106

117 Appendix A Evaluating the different versions of MAGPHYS In this thesis two different versions of MAGPHYS were used and refereed to as v1 and v2. The main difference between those (as described in section and 3.3.4) is in the model libraries and not in the actual algorithm. The pre-made libraries are created form different stellar populations synthesis models. The prior distribution of some of the parameters have also been changed between the two versions, resulting in different parameter space. The version MAG- PHYS v1 is calibrate for local galaxies, but has been used to fit SEDs of galaxies at higher ( 0.1 1) redshift galaxies too (Berta et al., 2013; Annunziatella et al., 2014; Hayward and Smith, 2015). A.1 BzK and PACS galaxies in GOODS- S In this thesis, the sample GOODS-S BzK and PACS at redshift 1.4 < z < 2.5, was fitted using MAGPHYS v1. The results of the 107

118 BzK PACS SFR[M yr 1 ] :5 0:2 0:1 0:05 2e8 5e8 1e9 2e9 5e9 1e10 2e10 5e10 1e11 2e11 5e11 1e12 stellarmass[m ] Figure A.1: M -SFR relation fitted with MAGPHYS v1 at 1.4 z 2.5, BzK-GOODS sample (blue triangles) and GOODS-PACS sources (red dots) detected sources. The dashed indicate the upper limit of ssfr. estimated SFR and M are shown in A.1 where the black dashed line have only been added to highlight the sharp upper limit in ssfr. This limit in ssfr is surprising since it is expected that star-forming galaxies should scatter around the main sequence. To have a sharp cut in the ssfr as seen in figure A.1 may indicate that the first result obtained might not be robust. It could also be some bias introduce when making the sample selections. In Rodighiero et al. (2011) they use COSMOS and GOODS-S data and implement the same BzK and PACS selection to define 4 sample of sources, and shows the result in a SFR-M plot figure 1 of 108

119 SFR[M yr 1 ] :5 0:2 0:1 0:05 0:02 0:01 0: redshift 1e6 1e7 1e8 1e9 1e10 1e11 stellarmass[m ] Figure A.2: SFR vs Stellar mass, 400 galaxies from the GOODS- S catalogue, randomly chosen. Colour indicate the redshift of the individual sources. Rodighiero et al. (2011). In this plot there is no sharp edge in either of the samples. This is one indication that the feature seen in figure A.1 are due to the fitting procedure of MAGPHYS. To investigate if this effect are due to any bias introduced by the selection techniques, 400 galaxies at random where chosen (first and last 200 galaxies in the catalogue) from the GOODS-S catalogue, irrespective of redshift. No additional criteria was made and the galaxies were fitted with MAGPHYS v1. The result is shown in figure A.2 which shows the same sharp upper limit in ssfr. The conclusion from this is that this behaviour is not due to a selection effect, but rather connected with the fitting procedure. 109

120 To investigate the underlying cause of this in the code, the galaxies located near the edge where studied in more details. One aspect that was considered was if this is due to some redshift effect. The redshifts of the random sample of 400 sources are shown in figure A.2 in a colour scale. There is no correlation between the redshift of the sources near the edge, which excludes this as the cause of the problem. The SFR is determined as a function of time as in da Cunha et al. (2008) and are described by an underlying continuous model, ψ(t) e γt (A.1) characterized by the age t form and the star formation time-scale parameter γ. The limit in ssfr are closely related to the SFR since it is determined as, t t t φ s (t) 8 dt ψ(t ) (A.2) t 8 M the ratio of the SFR avarage over the past t 8 = 10 8 yr and the curren stellar mass. The limit in ssfr are thus probably partly due to restrictions in t form and/or γ, which determined the SFR. Both these parameters has a prior distribution which determine the allowed values they can take in this model. The code is calibrated for galaxies in the nearby universe and the prior distribution of the parameters will thus be optimized for these type of galaxies. The galaxies in the sample used in this thesis are high-redshift galaxies which differ from local galaxies. The limit in ssfr is thus likely due to restrictions in the parameter space. Since galaxies at high redshift are usually younger and have higher SFR, the code may have problems fitting these galaxies properly since the appropriate values are not covered in the prior distribution of t form and γ A.2 Comparison at lower redshift (z 1) The edge seen in figure A.1 and A.2 showing the results for galaxies from GOODS-S fitted with MAGPHYS v1, is probably due to limita- 110

121 tion in parameter space that arise when fitting high-redshift galaxies. One possible way to overcome this limitation, without changing the distribution of the prior settings within the code, is to study galaxies at lower redshift. As the GOODS-S catalogue is much deeper and smaller, the COSMOS catalogue is used instead as it contains more low-redshift sources. The UVJ and PACS detected samples where fitted with MAGPHYS v1 and the result of this can be seen in figure top panel A.3. The main motivation for studying galaxies at lower redshift are to reduce the problem in the fitting procedure, but as seen in figure A.3 the same upper limit in the SFR still exist. In fact, now there are several lines in the SFR-M plane. It thus seems likely that the same restriction in the code have a considerable effect at redshifts 0.3 z 1. At a closer look, the galaxies that form the lines seen in the top panel in figure A.3, have the same ssfr. The value of the ssfr vary between the different lines, but are fixed. An explanation for this is that during the fitting procedure one parameter gets stuck in a local minima which it cannot get out of and then all other parameters connected to this one will have a very tight constrain and all get the same value. The code is somewhat static, where the user is not able to change the number of free parameters and all the libraries are already constructed from a fix set of priors. The code is made to be user-friendly and the only thing that is required by the user is to provide an input. The fitting is then done by running the provided scripts and the result are printed in two different output files for each galaxy. The user do not have a lot of control over the fitting and it is difficult to supervise the fitting process and determine if something is not working as intended. The exact same sources (2000 UVJ and PACS detected samples of COSMOS at 0.3 z 1) were fitted with MAGPHYS v2, bottom panel of figure A.3. This version contains a new set of library files and the prior distributions have been change, optimized to fit SEDs of star-forming galaxies at z > 1 in the optical-to-radio. The result of this fitting can be seen in figure A.3, and a pronounced feature now seen is that the galaxies are scattered in the SFR-M plot. The 111

122 UVJ PACS SFR[M yr 1 ] :5 0:2 0:1 0:05 0: e8 1e9 2e9 5e9 1e10 2e10 5e10 1e11 2e11 5e11 1e12 2e12 5e12 1e13 stellarmass[m ] UVJ PACS SFR[M yr 1 ] 2 1 0:5 0:2 0:1 0:05 0:02 0:01 0:005 1e9 2e9 5e9 1e10 2e10 5e10 1e11 2e11 5e11 1e12 2e12 5e12 1e13 stellarmass[m ] Figure A.3: SFR vs stellar mass, blue triangles 2000 COSMOS-UVJ and ref filled circles show COSMOS-PACS at 0.3 z 1. Black solid line indicate main sequence at z = 0.65 T op: Sample of galaxies fitted with MAGPHYS v1. Bottom: Galaxies fitted with MAGPHYS v2.