Investigating the Spectral Energy Distribution within the Dwarf Irregular Galaxy IC 10

Transcription

1 Investigating the Spectral Energy Distribution within the Dwarf Irregular Galaxy IC 10 by TARA JILL PARKIN A thesis submitted to the Department of Physics, Engineering Physics, and Astronomy in conformity with the requirements for the degree of Master of Science Queen s University Kingston, Ontario, Canada September, 2008 Copyright c TARA JILL PARKIN, 2008

2 Abstract We present new submillimetre images of the dwarf irregular galaxy IC 10, taken with the Submillimeter Bolometer Common-User Array, mounted on the James Clerk Maxwell Telescope. Combining this new data with archival data from the 2MASS survey, ISO, Spitzer IRAC and MIPS, and the VLA, we plot the observed spectral energy distributions from 1.24 µm to 850 µm for two star forming regions within IC 10, namely IC 10 SE and IC 10 NW. The spectral energy distributions were subsequently modelled using a dust model with PAHs, and silicate and graphite dust grain components. This is the first time that well-constrained spectral energy distribution models of two individual regions within IC 10 have been presented. From our results, we find that IC 10 SE and IC 10 NW share the same physical characteristics in most cases, such as the gas-to-dust mass ratio, the mass fraction of PAHs comprising the total dust mass, and the fraction of PAHs that are ionised. The most significant difference is seen in the peak wavelengths of the SEDs, which are 70 µm and 45 µm for IC 10 SE and IC 10 NW, respectively. From this we conclude that the primary dust component within IC 10 NW is warmer than that of IC 10 SE, due to the hot young stars at the heart of the star forming region within IC 10 NW having a larger heating effect on the nearby dust than the interstellar radiation field. The similar environments of these two regions lead us to suggest that the star formation taking place i

3 within them was triggered by the same starburst, and that both stellar populations evolved together. We also find that IC 10 has physical conditions that are common amongst other low-metallicity, dwarf irregular galaxies, implying that IC 10 does not have an abnormal interstellar medium in these regions. ii

4 Acknowledgements First and foremost, I would like to thank my supervisors Dr. Judith Irwin and Dr. Suzanne Madden for their guidance and support throughout the course of my research, and for giving me the opportunity to work in France for part of my project. I would also like to thank Dr. Sacha Hony and Douglas Rubin for their patience and endless support, and for sharing their knowledge with me. I would like to thank Dr. Christine Wilson for providing me with the SCUBA data, and Dr. George Bendo for helping me with the Spitzer MIPS data and our Spitzer proposal. I also send a special thank you to Dr. Frédéric Galliano for allowing me to use his SED model for part of my thesis. Finally I would like to acknowledge and thank everyone from the Service d Astrophysique at the CEA Saclay, France, for inviting me to work with them for six months. Without their efforts I would not have had the experience of a lifetime. This research was funded in part by the R. S. McLaughlin Fellowship awarded by Queen s University. iii

5 Table of Contents Abstract i Acknowledgements iii Table of Contents iv List of Tables vii List of Figures ix Chapter 1: Introduction Dwarf galaxies The spectral energy distribution (SED) of a galaxy The interstellar medium (ISM) IC Chapter 2: Data Reduction and Analysis ISO data Spitzer data iv

6 2.3 JCMT data Supplementary data Background flux evaluation Convolution Flux evaluation Error analysis Chapter 3: Results Morphology of IC SED modelling Model fitting results Chapter 4: Analysis and discussion Spatial analysis of IC IC 10 SE IC 10 NW Comparing IC 10 SE and IC 10 NW A comparison with other galaxies Chapter 5: Summary and Conclusions Bibliography v

7 Appendix A: List of Acronyms Appendix B: Box Method IDL Code B.1 bkgrd box overplot.pro B.2 bkgrd box method.pro Appendix C: Gaussian Method IDL Code vi

8 List of Tables 1.1 Characteristics of the various components of the ISM Various parameters pertaining to IC 10 as published to date, adjusted to our adopted distance Characteristics of the original images Zero-point magnitude conversions for 2MASS data Background evaluation comparison Aperture characteristics Multiplicative factors for aperture correction Radio data points used to extract radio continuum equation Flux in apertures and associated error contributions for IC 10 SE Flux in apertures and associated error contributions for IC 10 NW Size ranges and mass densities for each dust component Dilution factors and temperatures of the four-component ISRF Model Parameters The values of the eight parameters determined by the best-fitting model, for both IC 10 SE and IC 10 NW A comparison between derived values for IC 10 SE and IC 10 NW vii

9 4.2 A comparison of the important physical parameters derived for IC 10 SE and IC 10 NW with those of several other galaxies viii

10 List of Figures 1.1 Dwarf elliptical galaxy IC Dwarf irregular galaxy IC An example of a dust SED from Galliano et al. (2003) A schematic of the classical PDR region A schematic of the various components of the ISM A single benzene molecule Examples of small PAHs Examples of large symmetrical PAHs The IR spectra for NGC7027 and the Orion Bar (H2S1) An optical image of IC 10 from the Digitized Sky Survey H i distribution in IC 10 with holes identified The best-fitting SED of IC 10 SE as determined by Bolatto et al. (2000) Raster mode schematic diagram The 850 µm image of IC 10 with the negative bowl left untreated The 850 µm image convolved to eliminate source structure µm image comprising data reduced with SURF Boxes used for background evaluation with the box method Background evaluation using Gaussian method ix

11 2.7 Point spread functions Apertures centred on IC 10 SE and IC 10 NW a J-band (1.24 µm) b H-band (1.66 µm) c K-band (2.16 µm) d 3.6 µm e 4.5 µm f 5.8 µm g 6.75 µm h 8 µm i 11.4 µm j 15 µm k 24 µm l 70 µm m 160 µm n 450 µm o 850 µm p 3.55 cm q 6.2 cm The model SED for IC 10 SE The model SED for IC 10 NW µm contours overlaid onto the 8 µm image µm contours overlaid onto the 850 µm image µm contours overlaid onto the 850 µm image x

12 4.4 Our model SED for IC 10 SE in units of L Hz Our model SED for IC 10 NW in units of L Hz The model SEDs for four different regions within the LMC (Bernard et al., 2008) The model SED of NGC Model SEDs for the plane and diffuse ISM of the Milky Way xi

13 Chapter 1 Introduction Nearby galaxies are of great interest to astronomers from all areas of observational astrophysical research, primarily because of their proximity to us in the Universe. The advanced telescopes and instruments that we have at our disposal today make it possible to study the various characteristics of galaxies with higher resolution than ever before. With this comes the possibility to study individual regions within a single galaxy and investigate how the various components making up each galaxy such as the stellar content and the interstellar medium (ISM) influence each other. The effects that star formation (SF) and the ISM have on each other are very important for us to understand. Detailed studies of current star formation within a galaxy, as well as the contents of the ISM can give us clues about the star formation history of the galaxy (Galliano et al., 2003). Aspects of the star formation history, such as the frequency of star forming episodes, the types of stars produced, and the star formation rate all play important roles in the star formation history of a galaxy and we can use this information to study the evolution of a galaxy as a whole. This in turn may lead us to advances in our understanding of how the Universe itself has 1

14 CHAPTER 1. INTRODUCTION 2 evolved. In particular, the present-day structure of the ISM of a galaxy is strongly dependent on the evolution of its star formation rate (SFR). Clues as to previous episodes of star formation rates can be found in the various stellar populations within the galaxy (Tielens, 1995). Low-mass stars with long lifetimes were formed in earlier star formation episodes and the ejected material from the outer layers of these stars (ejecta) contributes more hydrogen (H) gas to the ISM by mass than the more massive stars. On the other hand, high-mass stars are connected to more recent star forming rates, as they have relatively short lifetimes. During their lifetime they contribute to the ISM through stellar winds or supernovae, metals such as carbon (C), oxygen (O) or iron (Fe), large amounts of mechanical energy and a strong flux of high-energy photons. All stars enrich the ISM with their ejecta through this feedback effect, and impact how a galaxy evolves. Material (i.e. gas and dust) from the outer layers of evolved stars mixes with the contents of the ISM already present, changing the chemical make-up of the ISM over time. The abundance of metals 1 increases, therefore changing the metallicity, Z/Z, of the galaxy, where Z is the metallicity of the Sun. Note that sometimes the metallicity of a galaxy is characterised by the abundance of oxygen in the galaxy, which is given by A O = log(n O /N H ) , (1.1) where N O and N H are the number abundances per cm 2 of oxygen and hydrogen, respectively. For reference, the number abundance of hydrogen in the Sun is 12.00, and its oxygen abundance, A O,, is 8.83 (Grevesse & Sauval, 1998). Since virtually 1 In astronomy, all elements aside from hydrogen and helium are called metals.

15 CHAPTER 1. INTRODUCTION 3 all metals are produced through the evolution of stars, metallicity can also give us insight into the star formation history of a galaxy. A galaxy with a high metallicity would imply that several generations of stars are present, meaning it is at a later stage in its evolution. A galaxy with a low metallicity would be at a younger stage of evolution, with fewer generations of stars populating the galaxy. Another method of probing the ISM of a galaxy is to study its dust. Dust plays a key role in the overall heating and cooling processes throughout the galaxy, a direct result of dust being very efficient at blocking optical light. Through the absorption of stellar light and its subsequent re-emission at longer wavelengths, the dust reveals itself optimally at infrared (IR) wavelengths. In addition, important large molecules called Polycyclic Aromatic Hydrocarbons (PAHs; see Section 1.3.3) that may trace regions of star formation (Tielens et al., 2004), are thought to make themselves known through emission lines at mid-infrared (MIR) wavelengths. However, it is important to note that there is some uncertainty as to whether or not PAHs are, in fact, the source of these IR emission lines. By analysing the dust Spectral Energy Distribution (SED; see Section 1.2) of a galaxy, spanning from near-infrared (NIR) wavelengths to the submillimetre (submm), we can determine the values of certain parameters that govern the evolution of the galaxy itself. Examples of these characteristics include its metallicity, the age of the galaxy, the initial mass function (IMF) and even the types of stars in the galaxy (Galliano et al., 2003). The IMF of a particular group of stars measures how many stars of a specific mass are formed as a function of stellar mass. The definition of the IMF, ξ, is (Hunter, 2001) ξ(log m) = (ln 10)mf(m), (1.2)

16 CHAPTER 1. INTRODUCTION 4 where m is the stellar mass, and f(m) = Am Γ 1 is the stellar mass function (the number of stars per mass bin as observed) with A a constant. Empirically we determine the slope, Γ, of the IMF by plotting stars in a log-log plot of the number of stars within a given mass range versus the average stellar mass within that range. Substituting f(m) into Equation (1.2) we obtain ξ(log m) = Cm Γ, (1.3) where C = A(ln 10). Taking the log of both sides and differentiating with respect to log m we obtain the equation for Γ: Γ = [ ] (log ξ(log m)) log m m. (1.4) The results of the model SED will give us constraints on parameters such as the dust mass, stellar mass or PAH abundance, which we can then use to infer the characteristics of the galaxy s evolution. 1.1 Dwarf galaxies Local dwarf galaxies are of particular interest because they come in a wide variety of morphologies, surface brightnesses and masses. In a very broad sense, dwarf galaxies generally fall into two categories, dwarf ellipticals (de) and dwarf irregulars (di). Dwarf ellipticals, such as IC 225, shown in Figure 1.1, are small, ellipsoidal galaxies with masses ranging between 10 7 and 10 9 solar masses (M ) and diameters ranging

17 CHAPTER 1. INTRODUCTION 5 Figure 1.1: A Digitized Sky Survey optical image of dwarf elliptical galaxy IC 225. Image from between 1 and 10 kpc 2. They also differ from their larger counterparts, the normal elliptical galaxies, as they have lower surface brightnesses for a given absolute magnitude, and also lower metallicities. One other subcategory of dwarf galaxies is the blue compact dwarf (BCD) galaxy (Carrol & Ostlie, 1996). These galaxies are very blue and have relatively large amounts of gas (in comparison to other des, which are normally gas depleted), indicative of recent star formation and a young stellar population. They normally have diameters less than 3 kpc and a mass of 10 9 M, but their large luminosities lead to low mass-to-light ratios. A BCD galaxy is sometimes classified as a di galaxy, as their characteristics are very similar. Dwarf irregular galaxies on the other hand, such as IC 1613 in Figure 1.2, are very irregular in shape and possess a large abundance of gas and dust, as they are very blue in colour, especially in their nuclei. The blue colour is indicative of young, hot stars, meaning star formation is likely still ongoing in these galaxies, unlike the des where 2 One parsec (pc) is equal to cm or 3.26 light years.

18 CHAPTER 1. INTRODUCTION 6 Figure 1.2: A Digitized Sky Survey optical image of dwarf irregular galaxy IC Image from star formation has, for the most part, ended. An important consequence of this young stellar population is that many di galaxies have low-metallicities, as evolved stars have not enriched the ISM with metals. IC 10, the focus of this project is normally classified as a di galaxy; however, some authors (e.g. Richer et al., 2001) classify it as a BCD galaxy. For this project we will use the more common classification, and assume IC 10 is a dwarf irregular galaxy. Dwarf irregular galaxies are ideal environments in which to study star formation and its impact on the ISM (and vice versa). Dwarf galaxies are too small in mass to promote the development of spiral arms, such as those we see in a spiral galaxy, yet in spite of this, dense regions still exist where stellar formation can occur (Hunter & Gallagher, 1989). Investigating the different processes of star formation between different types of galaxies can lead to a better understanding of the general properties of star formation that exist in all environments. Furthermore, dwarf irregular galaxies may, in fact, represent the primordial galaxies that existed during the early stages of the Universe (Madden et al., 2006), and became the progenitors of some of the

19 CHAPTER 1. INTRODUCTION 7 larger galaxies we see today (Hunter & Elmegreen, 2004). Many of them possess low-metallicities compared to the Solar value. This suggests there has not been much feedback from evolved stars, thereby insinuating that they could be at similar stages of chemical evolution as young (distant) galaxies in the early universe (Galliano et al., 2003). 1.2 The spectral energy distribution (SED) of a galaxy As already mentioned, star formation and evolution have a strong impact on the ISM, and likewise the composition of the ISM can affect the chemical structure and production of new stars. One way to conduct an in-depth study of the ISM in a galaxy is to plot its spectral energy distribution (SED). A SED is a plot of luminosity per unit frequency multiplied by frequency (νl ν ) versus wavelength for some ranges of wavelengths, making it an excellent tool to study the various components of the ISM. The focus of this thesis is on the dust SED, which spans roughly from the near-infrared (NIR) to mid-infrared (MIR) and through to submillimetre (submm) wavelengths (e.g. from the J, H and K NIR bands (see Table 2.1 and Section for details) through 450 µm or 850 µm). If there are enough data points such that the SED is well defined, then it can be modelled. The results of the model dust SED can tell us the relative abundances of the various components of the ISM such as hot and warm dust, PAHs and other molecules, and cold dust. An example of a dust SED is shown in Figure 1.3 (Galliano et al., 2003). This is a model of the dust SED of NGC 1569 which contains four main components: big grains, very small grains and

20 CHAPTER 1. INTRODUCTION 8 Figure 1.3: The modelled dust SED of NGC 1569 as presented in Galliano et al. (2003). The components contributing to the overall SED are big grains (dash-dotted line) of dust, very small grains (dotted line) of dust, polycyclic aromatic hydrocarbons (dashed line) and very cold dust grains (dash-dot-dot-dot line). Observed data are shown as crosses with error bars, where the horizontal error bars represent the filter bandwidth for a given wavelength, not physical error). very cold grains of dust, and PAHs. 1.3 The interstellar medium (ISM) The ISM of a galaxy comprises gas and dust found in wide variety of environments such as molecular clouds, ionised hydrogen (H ii) regions, and photodissociation regions 3 (PDRs). The gas content is far more abundant than the dust component, as 90 % of the ISM (and the gas in the Universe as well) is comprised of hydrogen. 3 These regions are sometimes known as photon-dominated regions.

21 CHAPTER 1. INTRODUCTION Figure 1.4: A schematic of the classical PDR region. The centre is an H ii region (dark grey region), usually in the vicinity of hot O and B stars. The outermost region is composed of molecular hydrogen, H 2 (white region). The region in the middle is the PDR region (pale grey region), where H 2 is dissociated by the FUV photons emitted by nearby stars. A PDR is simply defined as any region dominated by high energy far ultraviolet (FUV) photons, which can dissociate and even ionise molecules (primarily H 2 but other molecules as well, depending on the location of the PDR). Examples of these environments include the more classical definition of a PDR, the environment between regions of ionised and molecular hydrogen located in the proximity of luminous stars (see Figure 1.4), molecular clouds and even regions of neutral hydrogen in the ISM (Tielens, 2005). In Figure 1.5 we show a schematic from Kwok (2007) of the different components that make up a typical ISM. Also included in this diagram are some of the characteristics of each region, such as particle density and temperature. In Table 1.1 we present a summary of the different environments found in the ISM, along with their most important characteristics. Below we give a quick summary of each of the primary components of the ISM. For more details see Kwok (2007) and Tielens (2005). 9

22 Figure 1.5: A schematic of the various components of the ISM. Temperature, number density of atomic hydrogen and typical tracers of each medium are quoted. Image from Kwok (2007). CHAPTER 1. INTRODUCTION 10

23 CHAPTER 1. INTRODUCTION 11 Table 1.1: Characteristics of the various components of the ISM. Table information from Kwok (2007) ISM Common Temperature, Hydrogen Number State of Component Designation T (K) Density, n H (cm 3 ) a Hydrogen Hot ionised Coronal H ii b medium gas Warm ionised Diffuse 10 4 > 10 H ii medium ionised gas Warm neutral Intercloud H i c H i medium Atomic cold Diffuse H i + H 2 d neutral medium clouds Molecular cold Molecular clouds, H 2 neutral medium dark clouds Molecular hot Protostellar > 10 6 H 2 cores cores a The number density is of molecular hydrogen, n H2 for molecular clouds and cores. b Ionised hydrogen c Neutral hydrogen d Molecular hydrogen

24 CHAPTER 1. INTRODUCTION Gas There are three primary environments in which hydrogen is found: neutral hydrogen (H i), ionised hydrogen (H ii) and molecular hydrogen (H 2 ). The majority of the volume of the ISM is likely comprised of hot H ii regions. Regions of H ii have high temperatures due to their proximity to molecular clouds containing young, hot stars (see Figure 1.5). The density of these regions appears to correlate with size: more dense regions tend to be smaller and more compact than those of lower densities. Typical tracers of H ii include emission lines at optical, IR and UV wavelengths due to ions, and through the Hα recombination line. In addition they can also be traced with continuum radiation due to free electrons (see Section 2.8.3). Neutral hydrogen is found in both cold environments such as diffuse H i clouds, and warmer environments called intercloud regions. The most common tracer of H i is the 21 cm line; however, if there are bright stars located behind a H i region along its line of sight, optical and UV absorption lines can also reveal its presence (Tielens, 2005). Molecular hydrogen is most often found in giant molecular clouds. These regions are typically dense and very cold and with temperatures of 10 K and average particle densities of 200 cm 3 with core densities of up to 10 4 cm 3. They have a size of about 40 pc and a mass of order 10 5 M, although these numbers can vary with different clouds (Tielens, 2005). The bulk of the molecular hydrogen found in molecular clouds cannot be observed directly because it does not possess a net dipole moment, and therefore cannot radiate. Therefore, the presence of CO molecules is often used to trace H 2 via an empirically derived conversion factor of cm 2 (K km s 1 ) 1 for the Galaxy, though this number may not be valid for all environments (see Leroy

25 CHAPTER 1. INTRODUCTION 13 et al. (2006) for a discussion on this factor) Dust Dust manifests itself primarily in the infrared and the submillimetre wavebands. It is found in a variety of carbon, silicate or composite forms and in numerous types of environments (Tielens, 1998). It is observed in all three hydrogen dominated regions (i.e. H i, H ii, and H 2 ); however, dust can persevere the longest in molecular clouds where H 2 is the primary form of hydrogen. The high density and cold temperatures of these regions makes them ideal environments for dust to exist and interact with the gas. The inner regions of the cloud are protected from far-ultraviolet photons by the outer layers, and the cold temperatures allow for gaseous material to condense into a solid state. As a result, molecular clouds are the target of most studies of dust. Grains of dust form in the remnants of older stars in the giant phase of their evolution, as well as in novae and supernovae. In short, dust develops in environments where metals have condensed into a solid form and can potentially coagulate. There are several types of dust grains, with the majority possessing an amorphous structure, meaning that they do not have a very organised lattice structure between atoms. These grains can be either carbon-based or silicon-based, depending on the chemical composition of the parent star (Tielens et al., 2005). The temperature range of dust is very broad. Cold, large dust grains in radiative equilibrium with the interstellar radiation field have temperatures of 15 K and re-emit any absorbed stellar light at far-infrared (FIR) and submm wavelengths (i.e. > 60 µm). Hot dust emits in NIR and MIR bands 4 60 µm and can have temperatures upwards of 500 K (Tielens, 2005). The high temperatures are primarily

26 CHAPTER 1. INTRODUCTION 14 from PAHs (see Section 1.3.3) or very small grains which are heated by single photons up to extreme temperatures and then quickly radiate at NIR and MIR wavelengths, leading to large temperature fluctuations within the grains. In the context of star formation and our ability to observe star formation regions, dust can be a problem at optical wavelengths, as it can block the optical and UV light emitted by new stars at the core of a molecular cloud. However, it absorbs this light and re-emits it at infrared wavelengths, contributing to the processes of heating and cooling taking place in the ISM (Galliano et al., 2003). Depending on the size of the grain of dust, the wavelengths of emitted light will vary, allowing the identification of the different types of dust from a spectrum. Large dust grains may be able to reach a state of thermodynamic equilibrium with their surroundings after some time following the absorption of a photon, therefore self-radiating at a steady temperature (Kwok, 2007). On the other hand, small grains can increase their temperatures drastically with the absorption of a single photon, then cool off again upon re-emission via stochastic heating. As most environments where dust is found have relatively low particle densities, gas and dust temperatures are not often linked together, and can be significantly different due to a lack of collisional heating of particles (Kwok, 2007). Furthermore, if the dust grains are in a molecular cloud with a central source of stars the temperature of individual dust grains will vary with distance from the source. Therefore, an SED can be an excellent aid in determining the properties of the dust in the proximity of the source, as well as the radiation field of the source. Another important characteristic of a galaxy is its gas-to-dust mass ratio (where the gas mass here is the total mass of hydrogen). This ratio will give the relative

27 CHAPTER 1. INTRODUCTION 15 Figure 1.6: A single benzene molecule. Image taken from contributions of gas and dust to the total mass of a galaxy, and this can be used as an indicator for the evolutionary state of the galaxy. A higher gas-to-dust ratio would suggest a galaxy is at an earlier stage of evolution than one with a lower ratio, as a more evolved galaxy would be depleted of the gas locked up in stars and their remnants. Values for the gas-to-dust mass ratio for the Milky Way vary between local environments but the typical factor on a global scale is about 110 (Galliano et al., 2005) Polycyclic Aromatic Hydrocarbons (PAHs) Polycyclic aromatic hydrocarbons are molecules which have the simple benzene ring as their primary building blocks. Benzene is a planar (two-dimensional), hexagonal molecule comprising six carbon (C) atoms joined together to form a ring, with one hydrogen (H) atom attached to each carbon atom. Figure 1.6 shows a schematic of a benzene (C 6 H 6 ) molecule. Note that sometimes the H atoms are not shown on a benzene molecule or PAH but it is implied that they are present. PAHs are groups of benzene rings joined together with the hydrogen atoms attached only to

28 CHAPTER 1. INTRODUCTION 16 Figure 1.7: Examples of small PAHs. Image taken from chemical compound. Encyclopædia Britannica Encyclopædia Britannica Online. 25 June 2008 < Figure 1.8: Examples of large symmetrical PAHs (Bauschlicher et al., 2008). the outermost C atoms. Some examples of PAHs are shown in Figures 1.7 and 1.8. PAHs play an important role in numerous galactic environments. They are evident in most regions of the ISM, as well as various objects, such as young stellar objects, galactic nuclei, nebulae, or H ii regions (Peeters et al., 2004; Tielens et al., 2004). The PAH molecules are easily ionised by local FUV photons and as a result, gas in the region is heated by the free electrons via the photo-electric effect (Tielens et al., 2004). In their ionised form they contribute significantly to the overall balance of

29 CHAPTER 1. INTRODUCTION 17 charge in a photodissociation region (PDR) or a molecular cloud. In addition, their abundance can drastically change the degree of ionisation within a region. If there is a very low abundance of PAHs, then the degree of ionisation is primarily affected by the abundance of heavy metals within the host cloud. On the other hand, if the abundance of PAHs is high, the degree of ionisation is low in all circumstances, as free electrons can easily attach themselves to neutral PAHs creating negatively charged PAHs. These, in turn, will recombine with positively ionised PAHs or other molecules present. As a result of these interactions between PAHs and their environment, the chemical composition of the host region can be altered significantly (Tielens et al., 2004). The emission lines in the MIR thought to be from PAHs can be excellent tools to study the physical conditions in which they are situated. The most prominent occur at λ 3.3, 6.2, 7.7, 8.6 and 11.2 µm (Tielens et al., 2004), and are due to the relaxation of various stretching and bending vibrational modes of the molecule. In Figure 1.9 we show two examples of infrared spectra with the emission lines and their corresponding modes, from Peeters et al. (2004). These features are known to vary from source to source due to the local physical conditions (Peeters et al., 2004; Tielens et al., 2004). For example, the strength of the lines corresponding to compact H ii regions with significant amounts of dust are much weaker due to the absorption of the FUV photons by the dust. PAHs also trace conditions of star forming regions as they are excited or ionised by photons emitted by the hot stars in an H ii region; they can also be destroyed by the hard radiation near or within the H ii region (Haas et al., 2002). Therefore, observation and analysis of PAH emission can be very useful in examining the nature of a particular locale within a galaxy.

30 CHAPTER 1. INTRODUCTION 18 Figure 1.9: The IR spectra for NGC 7027 and the Orion Bar (H2S1), including the prominent PAH emission lines. The spectra are shaded to reveal detail. The vibrational modes corresponding to the PAH emission lines are also shown at the top, along with emission plateaux which may or may not be related to the PAH features. Image from Peeters et al. (2004).

31 CHAPTER 1. INTRODUCTION IC 10 From the earliest publications pertaining to IC 10 (e.g. Mayall, 1935, and references therein) in the early 20 th century, this galaxy has been a fascination to many astronomers. It is a member of the Local Group of galaxies 4, with a distance of approximately 0.82 Mpc (Wilson et al., 1996), although other recently published values range between 0.66 Mpc (Sakai et al., 1999) and 0.95 Mpc (Hunter, 2001). Determining an accurate distance to IC 10 has proven quite difficult, as it lies almost in the Galactic plane, with a Galactic latitude of 3. 3 (Hunter, 2001). Accurate determinations of the reddening due to the foreground dust in our galaxy must be made before the distance can be calculated. Current values of the total reddening, E(B V ) t tend to fall between magnitudes (mag), with a typical value of 0.77 (Massey & Armandroff, 1995; Hunter, 2001). In Table 1.2 we present a full list of parameters for IC 10, obtained from various publications. IC 10 (see Figure 1.10 for optical image from the Digitized Sky Survey 5 ) is generally classified as a dwarf irregular galaxy which has undergone a recent episode of star formation; however, Richer et al. (2001) have classified it as a Blue Compact Dwarf (BCD) galaxy (see Section 1.1 for the definitions of these galaxy types). Regardless of how IC 10 is classified, it remains that it has an unusually high star formation rate of 0.03 M yr 1 kpc 2 (Hunter, 2001), when compared to other irregular galaxies. Most irregular galaxies have star formation rates that fall in the range less than 4 The Local Group consists of about 35 galaxies dominated by our Milky Way and the Andromeda Galaxy (also known as M 31). The rest of the members are dwarf galaxies some of which are satellites of the Milky Way and Andromeda. 5 The Digitized Sky Surveys were produced at the Space Telescope Science Institute under United States Government grant NAG W The images of these surveys are based on photographic data obtained using the Oschin Schmidt Telescope on Palomar Mountain and the UK Schmidt Telescope. The plates were processed into the present compressed digital form with the permission of these institutions.

32 CHAPTER 1. INTRODUCTION 20 Table 1.2: Various parameters pertaining to IC 10 as published to date, adjusted to our adopted distance. Parameter Distance, D Galactic Latitude, b Inclination, i Absolute Magnitude, M B Star Formation Rate Total reddening, E(B V ) t H i Mass Molecular gas mass Metallicity, log(o/h) + 12; Z/Z a Wilson et al. (1996) b Hunter (2001) c Shostak (1974) d Wilcots & Miller (1998) e Leroy et al. (2006) f Lequeux et al. (1979) Value 0.82 Mpc a 3. 3 b (35 ± 5) c mag b 0.03 M yr 1 kpc 2, b 0.77 mag b (1 2) 10 8 d M e M 8.17; 1/6 f 0.01 M yr 1 kpc 2, and a large fraction of those have SFRs of 10 4 M yr 1 kpc 2 (Hunter, 1997). According to Massey & Armandroff (1995), there are 15 confirmed Wolf-Rayet 6 (WR) stars present in the galaxy, which implies the presence of a large number of young O- and B-type stars as well 7. More recently, Massey & Holmes (2002) conducted a deeper survey of IC 10 in search of more WR candidates. They conclude that there are approximately 100 WR stars throughout the extent of the galaxy. This means that IC 10 has a global surface density of WR stars that is much more dense than other galaxies in the Local Group, and approximately 20 times the density in the Large Magellanic Cloud (LMC; Massey & Holmes, 2002). The abundance of WR stars is important for the evolution and morphology of IC 10, as their 6 A Wolf-Rayet star is a very hot, massive young star that has very strong stellar winds. 7 Stars are classified by their temperature using a letter scheme, OBAFGKM. Each letter represents a different spectral type of star. O- and B-type stars are the hottest stars, while M-type stars are the coolest (Zeilik & Gregory, 1998).

33 CHAPTER 1. INTRODUCTION 21 Figure 1.10: An optical image of IC 10 from the Digitized Sky Survey. The two red X s mark the centres of our selected regions IC 10 SE (lower left) and IC 10 NW (upper right), while the circles show the apertures we use for this project. The radii of these apertures are and for IC 10 SE and IC 10 NW, respectively. Image from form?target=ic10&resolver=simbad. strong stellar winds and energy can blow out material surrounding them, significantly altering the structure of the galaxy. It is important to understand the properties of the stellar population of IC 10 in order to make conclusions about the evolution of the galaxy. A later study of IC 10 by Hunter (2001) also probed the stellar population. Using optical images from the Hubble Space Telescope (HST), they set out to determine the stellar initial mass function (IMF) of what they call the starburst region, which corresponds approximately to our IC 10 SE (see Figure 1.10). As stated in Equation (1.2), the definition the IMF is ξ(log m) = (ln 10)mf(m) where m is the stellar mass, f(m) = Am Γ 1, is the stellar mass function, and Γ is ( log ξ(log m))/( log m). As Hunter (2001) was unsure how

34 CHAPTER 1. INTRODUCTION 22 recent the starburst occurred, she has calculated the IMF for two extremes: a coeval stellar population, in which all stars formed within the hydrogen burning lifetime of the highest mass stars, and a stellar population with a constant star formation rate, which must account for those stars that are now dead, as well as the age of the region itself. They also considered two metallicities: Z = and Z = 0.008, assuming the true metallicity falls in between these extremes. For Z = they determined Γ = 1.9 ± 0.4 and Γ = 0.9 ± 0.3 for a coeval stellar population and constant star formation, respectively. For Z = 0.008, Γ = 2.1 ± 0.4 and Γ = 1.0 ± 0.4 for the coeval case and constant star formation case, respectively. The age of the starburst is less than 13 Myr for the coeval population and approximately 40 Myr for constant star formation. From these results they conclude that the IMF of stars with intermediate masses is not unusual for the starburst region because they expect the true slope to lie in between these extremes, and the slope of the Salpeter IMF, Γ = 1.3, lies between them as well. The slope of the Salpeter IMF is the classically, empirically determined value (Salpeter, 1955; Scalo, 1986). The WR stars discovered by Massey & Armandroff (1995) are thought to be associated with a small burst of star formation much more recent than the majority of stars in the starburst region, and small groups of OB stars appear to drive the star formation in IC 10. This theory has been supported by a more recent study of IC 10 with the Hubble Space Telescope (HST). According to Vacca et al. (2007), the older starburst occurred approximately Myr ago while a more recent starburst period occurred only about 10 Myr ago, the latter of which is in agreement with Hunter (2001). Equally important are the H i and H ii regions within IC 10. Studies of the H i content of IC 10 have been carried out for decades by groups such as Shostak

35 CHAPTER 1. INTRODUCTION 23 Figure 1.11: H i distribution in IC 10 with holes identified (Wilcots & Miller, 1998). West is to the right. (1974) Klein & Graeve (1986) using radio maps. More recently, Wilcots & Miller (1998) conducted an extensive survey of H i in IC 10. They detect holes in the H i emission throughout the galaxy, which are thought to be due to hot OB stars blowing off material with their strong stellar winds, or possible supernova explosions. In Figure 1.11 we show one of their H i maps which identifies seven independent holes. These holes lead to a strong deficit of H i in the western portion of IC 10 (Wilcots & Miller, 1998). The authors also note that only one to three of the holes were created by supernovae, based on the current radii of the holes and typical expansion rates.

36 CHAPTER 1. INTRODUCTION 24 They take this to imply that the starburst episode that appears to be currently taking place is in its early stages, and that the majority of holes are created by the stellar winds of the stars in the centres. In accordance with Hunter (2001) and Vacca et al. (2007), Wilcots & Miller (1998) agree that a new starburst began only a few million years ago. The radio continuum emission of IC 10 has also been studied at length. Radio observations at 6, 20 and 49 cm were carried out by Yang & Skillman (1993) to study the continuum, with the conclusion that while most sources were associated with thermal emission, there were several nonthermal sources including one they call a superbubble, which was likely formed by approximately 10 supernovae. A later study by Thurow & Wilcots (2005) agreed that there was enough energy in the region to suggest numerous supernovae; however, just recently Lozinskaya & Moiseev (2007) suggested that the nonthermal superbubble is the result of a hypernova 8 explosion rather than several supernovae. The large amounts of energy deposited into the ISM, as well as the enriched metals formed during the explosion(s) can have a significant impact on the characteristics of the ISM. The continuum regions showing thermal emission match well with the H ii structure described in Hodge & Lee (1990), which traces free-free emission from free electrons. These authors report that the H ii content has been resolved into 144 individual regions using a narrow-band Hα filter, and the variety of morphologies amongst these regions is quite broad. The overall distribution may say something about the mode of star formation taking place within the galaxy. Thurow & Wilcots (2005) conducted a study of the kinetics of the ionised gas, and they found that the velocity field of 8 A hypernova is the explosion of an extremely massive star. Basically it is a very large supernova explosion.

37 CHAPTER 1. INTRODUCTION 25 the H ii closely matched that of the H i content, as determined by Wilcots & Miller (1998). In addition, these regions are all in the central starbursting region of the galaxy. IC 10 has also been observed extensively at infrared and submillimetre wavelengths. Early far-infrared (FIR) and submillimetre observations were carried out by Thronson et al. (1990) using the Kuiper Airborne Observatory (KAO). Images at 95 µm and 155 µm reveal a spatial correlation with infrared emission in the two concentrated areas of star formation of IC 10 SE and IC 10 NW. The authors concluded that there is only a small amount of cold dust, with temperatures less than 25 Kelvin (K), as they measured average dust temperatures of 35 K over the 60 µm to 160 µm waveband and 27 K using just the 155 µm KAO data. With the latter temperature the authors derived a dust mass of M d M, and a gas mass of M gas M for these regions, assuming a gas to dust mass ratio of 750 (we have scaled their values from a distance of 1.3 Mpc to our adopted distance of 0.82 Mpc; Thronson et al., 1990). Mid-infrared (MIR) studies of IC 10 were carried out by Dale et al. (1999) and Dale et al. (2000) using the Infrared Space Observatory s ISOCAM (see Section 2.1 for details). Our ISO data are those initially published in these papers. In the 6.75 µm image PAHs are the main contribution, as the band picks up the λ 6.2, 7.7 and 8.6 µm emission features, while the 15 µm image reveals faint 12.5 µm PAH emission, as well as emission from a dust continuum between 13 and 18 µm. (see Section 1.3.3). In Dale et al. (1999) they conclude that IC 10 has a large abundance of high energy UV photons and a low abundance of PAHs, based on the trends they find in the total surface brightness ratios I ν (6.75 µm)/i ν (15 µm), of the galaxies in their survey. A

38 CHAPTER 1. INTRODUCTION 26 ratio less than 1 indicates regions with strong heating, as the continuum intensity detected by the 15 µm filter increases while the abundance of PAHs decreases (shown in the 6.75 µm image), as they are thought to be destroyed by FUV photons. A continuation of this study was carried out by Dale et al. (2000), and they concluded that the material emitting strongly at MIR wavelengths in IC 10 has a characteristic temperature between 100 K to 200 K, implying an interstellar radiation field (ISRF) of 10 4 times that of in the Solar vicinity. They also associate this emission with H ii regions, found either within these regions or in their outskirts. Images of IC 10 taken with the Spitzer Space Telescope s Infrared Array Camera (IRAC; see Section 2.2) have also been previously published within a survey of 18 irregular galaxies by Hunter et al. (2006). They used the 3.6 µm image to study the older stellar population of these galaxies and observed that a dust lane, which blocks optical light, runs through the western portion of IC 10 and is transparent at 3.6 µm revealing otherwise hidden stellar content. The strongest emission at this wavelength correlates with the dust lane and to regions strong in Hα (λ µm), while revealing more finely detailed structure in the south-eastern part of the galaxy. These strongly emitting NIR regions are thought to reveal luminous clusters of stars hidden in optical wavelengths. Hot dust, often associated with H ii regions, was studied with the 4.5 µm images. The authors determined a small correlation between hot dust and the star formation rate. As the SFR increases, the amount of hot dust also increases, though slowly. Using the 5.8 µm and 8.0 µm images, Hunter et al. (2006) studied PAHs and observed that as the SFR increases with respect to stellar emission, the strength of the PAH emission also increases. For IC 10 in particular, the authors also studied the spatial

39 CHAPTER 1. INTRODUCTION 27 morphology of the PAH emission, and conclude that the PAHs are heated by stars embedded in clusters as the PAHs are found in the edges of shells of matter. IC 10 SE, the most massive giant molecular cloud complex in IC 10, has already been studied at 850 µm with the Submillimeter Common-User Bolometer Array (SCUBA; see Section 2.3 for details) by Bolatto et al. (2000), concurrently with spectral observations of CO transitions. In addition to 850 µm, the authors also carried out observations at 1350 µm and 450 µm with SCUBA, though they were unable to make any significant detections at 450 µm. They produced the SED for the FIR dust continuum shown in Figure 1.12 and found that it was a shallow continuum reflecting the general form of a greybody (modified blackbody). If a greybody has an opacity τ = (λ 0 /λ) β where λ 0 is the wavelength at which the emission becomes optically thick and β is the greybody emissivity exponent, then its observed thermal emission flux density is given by (Bolatto et al., 2000) F ν = ΩB ν (T)(1 e (λ 0/λ) β ), (1.5) with Ω being the solid angle over which the source is emitting, and B ν (T) is the equation for a blackbody (see Equation (3.6)). The authors conclude that the graybody emissivity for IC 10 is low, as β 0.3 from their best-fit model and it is expected that 1 < β < 2 for most materials depending on their composition. They suggest that the low grain emissivity is due to IC 10 s low metallicity and strong UV radiation field, which can destroy small dust grains. Following in the footsteps of the above paper, a complete survey of the CO (J = 1 0) transition was carried out by Leroy et al. (2006) in search of giant molecular clouds within IC 10. They detected 16 individual clouds and concluded that despite

40 CHAPTER 1. INTRODUCTION 28 Figure 1.12: The best-fitting SED of IC 10 SE as determined by Bolatto et al. (2000). Black dots show the original observational measurements, black circles represent the model fits to the photometry at these wavelengths and the boxes are 3σ error limits. The solid black, solid grey and dashed lines represent models with β equal to 0.5, and β fixed at 1.0 and 1.5, respectively.

41 CHAPTER 1. INTRODUCTION 29 the low metallicity of IC 10 in comparison to our Galaxy, the characteristics of these regions such as mass, luminosity, and CO spectral line widths are comparable to those of the Galaxy. It is generally agreed that IC 10 has undergone a recent episode of star formation within the past 10 Myr or so, and that it has an unusually high star formation rate and surface density of Wolf-Rayet stars. The mechanical energy from these and other hot O and B stars is observed to have strongly modified the morphology of IC 10, especially at H i wavelengths where distinct holes have been identified. It is unclear as to whether or not any of these holes can be attributed to supernova events, although several groups believe a small number of supernovae may be the source of certain holes. It is also generally agreed that IC 10 is bathed in a strong interstellar radiation field, which may be the cause of a lower observed abundance of PAHs. The objective of this thesis is to investigate the interstellar radiation field within two different star forming regions of IC 10. We plan to obtain images of the galaxy at as many wavelengths that we can between approximately one micron and 1000 microns, in order to create a well-constrained SED that can be modelled. The model will be used to obtain information about some of the physical characteristics of these two regions, and then we will examine our results. In this thesis we present archival data from the Infrared Space Observatory (ISO), and Spitzer Space Telescope. We also present new 450 µm and 850 µm data from the SCUBA instrument mounted on the James Clerk Maxwell Telescope (JCMT). These data, combined with supplementary data from the 2MASS survey and the Very Large Array (VLA), are used to analyse the dust Spectral Energy Distribution (SED) for two distinct regions within IC 10 (see Figure 1.10). We chose these regions as they

42 CHAPTER 1. INTRODUCTION 30 are the two primary star forming regions of IC 10 and they show strong emission through out the majority of our data set; in fact, we are limited to these regions as they were the only regions with a signal-to-noise ratio sufficiently high for us to study in detail, but they are still important regions to study. Star forming regions are ideal environments to study the characteristics of a galaxy, especially the dust, as it is illuminated by the hot stars at the centre of these regions. Theoretical SED models are then fit to our observational SEDs using a new model, and the results are subsequently analysed to obtain important information regarding the parameters solved for with the code. From this we want to ascertain the extent and types of activity taking place within this galaxy, and then make useful comparisons with previous work on IC 10, as well as comparisons to both our own Milky Way as well as other local dwarf irregular galaxies. In Chapter 2 we present the data sets and discuss the treatment applied to them. In Chapter 3 the model and results are presented, with our analysis and discussion following in Chapter 4. We conclude in Chapter 5.

43 Chapter 2 Data Reduction and Analysis We have obtained images of IC 10 at 17 wavelengths that were reduced from data collected at several different observatories. The three key sources of our infrared and submillimetre data are the Infrared Space Observatory 1 (ISO; Kessler et al., 1996), the Spitzer Space Telescope 2 (Werner et al., 2004), and the Submillimeter Common- User Bolometer Array (SCUBA; Holland et al., 1999) installed on the James Clerk Maxwell Telescope (JCMT) 3. We have also gathered supplementary data from the archives of the Two Micron All-Sky Survey 4 (2MASS; Skrutskie et al., 2006) and 1 ISO is a European Space Agency (ESA) project with instruments funded by ESA Member States (especially the principle investigator (PI) countries: France, Germany, the Netherlands and the United Kingdom) and with the participation of Japan s Institute of Space and Astronautical Science (ISAS) and the United States of America s National Aeronautics and Space Administration (NASA). 2 The Spitzer Space Telescope is operated by the Jet Propulsion Laboratory (JPL), California Institute of Technology (Caltech) under a contract with NASA. 3 The James Clerk Maxwell Telescope is operated by The Joint Astronomy Centre on behalf of the Science and Technology Facilities Council of the United Kingdom, the Netherlands Organisation for Scientific Research, and the National Research Council of Canada. 4 The Two Micron All Sky Survey is a joint project of the University of Massachusetts and the Infrared Processing and Analysis Center/California Institute of Technology, funded by the National Aeronautics and Space Administration and the National Science Foundation. 31

44 CHAPTER 2. DATA REDUCTION AND ANALYSIS 32 the National Radio Astronomy Observatory s Very Large Array 5 (NRAO; VLA). All of our data are in the Flexible Image Transport System (FITS) format, which is a common file format for astronomical images. In this chapter, details about each data set acquired will be described as well as the methods used to reduce the data for our analysis purposes. A summary of our observational data is presented in Table ISO data We have three images of IC 10 taken by the ISO Camera (ISOCAM; Cesarsky et al., 1996), at 6.75 microns (µm), 11.4 µm, and 15 µm that were retrieved from the NASA Extragalactic Database 6 (NED) archives. The observations and treatment of these images were carried out by Dale et al. (2000). They used ISOCAM s long wavelength (LW) array with the broadband filters LW2 and LW3 to observe at 6.75 µm and 15 µm respectively, during two different observing runs. In addition, they also observed IC 10 at 11.4 µm with the LW8 filter only during the second observing run. All of the observations were done in raster mode 7, the mode used for observing extended objects that do not fit within one instrument field of view (see Figure 2.1 for a schematic diagram of the spatial coverage resulting from using this mode). For these data, 16 observations were made to create a grid of four images by four images, slightly overlapping one another to ensure the region is fully sampled. They were 5 The National Radio Astronomy Observatory is a facility of the National Science Foundation operated under cooperative agreement by Associated Universities, Inc. 6 The NASA/IPAC Extragalactic Database (NED) is operated by the Jet Propulsion Laboratory, California Institute of Technology, under contract with the National Aeronautics and Space Administration. 7 This mode is sometimes called scan-map mode for other telescopes or instruments.

45 CHAPTER 2. DATA REDUCTION AND ANALYSIS 33 Figure 2.1: Schematic diagram of the raster mode coverage. The four different coloured large squares each represent the field of view of the instrument during one observation, and the various shades of colour represent the regions covered by multiple observations. The ellipse and stars are shown to represent a galaxy which is larger than one field of view of the telescope. later combined to form one mosaic image of IC 10 and the surrounding sky. Reduction of the data for IC 10 was carried out by Dale et al. (2000), completed with the CAM Interactive Analysis (CIA) software (see Table 2.1 for the details about each image). Due to the disconnection of column 24 of the LW array (Blommaert et al., 2003), there is a strip of dead pixels in each map where the pixel values have been set to Not a Number (NaN). In addition, the pixels covering the first raster position (located in the southeast corner of each image) were also set to NaN (masked out) by the authors since the maps still show low-level residuals due to transients from the sources (Dale et al., 2000). Transients are residual flux from photons already detected by the detector that decay over a time period slower than the time between photon detections. This leads to a build up of flux in the detector from previous photons which can lead to an overestimate of the true flux from the source. Note that all pixels set to NaN are excluded from all of our analyses.

46 CHAPTER 2. DATA REDUCTION AND ANALYSIS Spitzer data We have obtained images of IC 10 created with data from Spitzer s Infrared Array Camera (IRAC; Fazio et al., 2004) at 3.6 µm, 4.5 µm, 5.8 µm, and 8.0 µm. In addition, we have three images from the Multiband Imaging Photometer for Spitzer (MIPS; Rieke et al., 2004) at 24 µm, 70 µm, and 160 µm. All of the IRAC maps were downloaded from the Spitzer data archive in the Post-BCD format, where BCD means basic calibrated data (Reach et al., 2006). These data sets were reduced using the standard processing pipeline developed for Spitzer, and initially all of our MIPS data were obtained from the archive in this format as well. However, upon close examination of the MIPS data, and after consulting the Spitzer Science Centre s MIPS data handbook 8 we deemed these MIPS post-bdc data not fit for our analyses, as the standard MIPS processing pipeline was unable to reduce the data to the quality we need for this study. As a result, we contacted Dr. George Bendo from the Imperial College in London, England, who is an expert at dealing with the reduction of MIPS band data, and he agreed to help us. To improve the quality of the images, he used the raw data from the Spitzer archives, and reduced the MIPS data himself using the Spitzer data analysis software package, MIPS Data Analysis Tools (DAT). This software gives the observer better control over artifact removal and general data processing (Dr. Bendo; private communication). The key benefits of using raw data and this software package for our images are that Dr. Bendo was able to adjust the processing to compensate for the lack of sky coverage in these data, and more accurately determine the background flux. He determined the median surface brightnesses in the off-target images (not always 8 From

47 available in the Post-BCD format) relating to IC 10, and subsequently subtracted these values from each of the target images before sending the results to us. The MIPS images from Dr. Bendo are still not excellent maps though, as all three maps were generated with data collected during observations made using Spitzer s photometry mode, which is a pointed mapping mode and does not have a large enough field of view for the size of IC 10 (Dr. Bendo; private communication). As a result, the 70 µm and 160 µm images are missing about 50% and 20% of the extent of IC 10, respectively. Ideally, these observations should have been carried out using the scan map mode, which is the best mode for observing large extended sources (see Kennicutt et al., 2003). Thus, in November of 2007 we submitted a proposal for Spitzer s fifth and final observation cycle, to collect new and properly generated MIPS maps for IC 10. However, in February of 2008 our proposal was unfortunately rejected, so we proceed with the MIPS maps generated by Dr. George Bendo, ensuring to carefully consider and incorporate all errors associated with them where necessary.

48 Table 2.1: Characteristics of the original images. Telescope, Wavelength, Bandwidth, Beam Plate- Field Original Conversion Background Pointing Instrument λ 0 λ Size a scale b Size Units Factor Flux Uncertainty (µm) ( µm) ( ) ( /pix) ( ) to Jy/sr (MJy/sr) ( ) 2MASS c DN d ± 0.2 < DN ± 0.3 < DN ± 0.4 < 7 Spitzer, IRAC e MJy/sr ± MJy/sr ± MJy/sr ± ISO, ISOCAM f mjy/pix ± Spitzer, IRAC e MJy/sr ± ISO, ISOCAM f mjy/pix ± mjy/pix ± Spitzer, MIPS g MJy/sr ± 0.1 h MJy/sr ± 2 h MJy/sr ± 7 h < 3.9 JCMT, SCUBA i Jy/beam ± Jy/beam ± 2 6 NRAO, VLA j Jy/beam Jy/beam a See Section 2.6 for a description of the beam size. Units are arcseconds. b All platescale values are contained within the headers of the images themselves. Units are arcseconds per pixel. c Central wavelengths, bandwidths and positional uncertainty are from Cutri et al. (2003). Beam sizes are from the Interactive 2MASS Image Service ( d Data-Number unit e Central wavelengths, bandwidths and beam sizes are from Fazio et al. (2004). Positional uncertainty is from f Beam sizes are from Okumura (2000). Central wavelengths, bandwidths and positional uncertainty are from Blommaert et al. (2003). g Bandwidths and beam sizes are from Rieke et al. (2004). Central wavelengths and positional uncertainties are from h (George Bendo; private communication) i Central wavelengths, bandwidths and beam widths are from Holland et al. (1999). See Section 2.3 for details about positional uncertainty. j Central wavelengths, bandwidths and positional uncertainty are from Ulvestad et al. (2007). Beam sizes are from the NRAO Data Archive System Image Retrieval Tool ( CHAPTER 2. DATA REDUCTION AND ANALYSIS 36

49 CHAPTER 2. DATA REDUCTION AND ANALYSIS JCMT data We have two images of IC 10 derived from the SCUBA instrument on the JCMT, one at 450 µm and the other at 850 µm. These data were obtained by Dr. Christine Wilson during two different observing runs in 1999 and 2000, both with excellent observing conditions. Under normal operating conditions, the secondary mirror on the JCMT should chop both in the azimuthal and declination directions with up to three different chop throw values in each direction, for a total of six maps. Each image shows the flux difference between the source and the off-target sky in the chopping direction, and these images are combined in Fourier space, then transformed back into real space to obtain the final image (Johnstone & Bally, 1999). However, during the observation run in 1999, the secondary mirror on the JCMT was not operating correctly due to technical difficulties, and therefore only chopped in the azimuthal direction (Dr. Wilson; private communication). Since chopping only occurred in the one direction, the chop direction constantly changed with respect to the sky. As a consequence, the usual method of reconstructing chopping mode data used by the standard SCUBA data reduction software, the SCUBA User Reduction Facility (SURF; Holland et al., 1999, and references therein) could not be used. With SURF, the reconstruction method requires that the chop throws remain fixed on the sky. Therefore, the SURF package could be used for certain steps, but not for the image reconstruction. Instead, an alternative method which permits a varied chop throw pointing was used. For details, see Johnstone et al. (2000a). The observations in 2000 were carried out with a fully operational secondary mirror, using all six chop throws. In addition, the filter at 450 µm was replaced by a more sensitive one between the 1999 and 2000 observations, improving the sensitivity

50 CHAPTER 2. DATA REDUCTION AND ANALYSIS 38 Figure 2.2: The 850 µm image of IC 10 with the negative bowl left untreated. The scale is in units of Jy/sr. of the 450 µm map from In the end, only the data collected in 2000 were used to produce the image at 450 µm as they were of higher quality, while the 850 µm image was produced using a combination of data collected both in 1999 and 2000 (Dr. Wilson; private communication). All of the reduction steps were carried out by a summer student of Dr. Wilson. Reduced to the state the images were in when we received them, the maps revealed some structure in IC 10; however, they were not optimal for any analyses because of several remaining issues. The biggest problem was an artificial negative bowl around the central emission peaks in both images. In Figure 2.2 we show the original 850 µm data. An effect intrinsic to SCUBA scan maps, these negative bowls occur because the background cannot be quantified by SCUBA when in chopping mode and therefore the average flux in the final map is set to zero. As a result, the total negative

51 CHAPTER 2. DATA REDUCTION AND ANALYSIS 39 Figure 2.3: The 850 µm image convolved to 240, in order to eliminate source structure. Note that coordinates are not on this image as they haven t been assigned to it; however the spatial scale is the same as that shown in Figure 2.2. Greyscale units are Jy/sr. flux balances out the strong positive source emission and cannot be eliminated by simply adding a constant flux to each pixel (Johnstone et al., 2000b; Reid & Wilson, 2005). Instead, we followed the method discussed by Johnstone et al. (2000b) and Reid & Wilson (2005), where a masked version of the original image is convolved (see Section 2.6) using a Gaussian kernel at least twice as wide as the largest chop throw to eliminate most, if not all of the source structure in the original image. The largest chop throw was 65, and we convolved the image to 240. The convolved 850 µm image is shown in Figure 2.3. In our case, we masked out pixels with an absolute flux greater than twice the standard deviation of the pixel distribution (2σ) before convolution, by copying the original image to a new file and setting the values of the pixels to be masked to NaN. This process helps prevent new negative regions from

52 CHAPTER 2. DATA REDUCTION AND ANALYSIS 40 forming as a result of the next step, which is to subtract the convolved image from the original, unmasked image (Reid & Wilson, 2005). This new image is then the image used to determine the background flux contribution requiring removal. The background flux removal process is discussed further in Section The other main issue with the SCUBA maps was that the headers of the FITS files that contained the images were missing some crucial information, such as the coordinates of IC 10 and information pertaining to the reference pixels, which are required to conduct any astrometry on the images. To correct this problem, we set out to rewrite the headers ourselves given the information we had. We first chose a reference pixel in each image and determined their corresponding positions in right ascension (RA) and declination (DEC). The best method we had to assign an accurate position to our reference pixels required the use of 450 µm and 850 µm images created by Dr. Wilson with only the data set taken in 2000, but using SURF to fully reduce the data. We carefully examined these images and visually determined the approximate centres of each of the three main peaks of emission visible in the galaxy, then noted their x and y pixel coordinates, as well as their positions in RA and DEC. Concluding that the southernmost peak of IC 10 SE (see Figure 2.4) was the most reliable peak in each map, we assigned the centre position of this peak in RA and DEC from both alternate images to the pixel in each of our images that marked the approximate centre of the same peak. In general, the pointing uncertainty (how accurately a telescope is able to point at a set of given coordinates) for the JCMT is 1.5 ; however, as we set the coordinate grid ourselves this uncertainty will be larger. We conservatively estimate that the uncertainty in correctly determining the coordinates of the 450 µm and 850 µm is 6. See Table 2.1 for the pointing uncertainties of all instruments.

53 CHAPTER 2. DATA REDUCTION AND ANALYSIS 41 Figure 2.4: 850 µm image comprising data reduced with SURF. The circled region highlights the peak we used to determine the coordinates of our images.

54 CHAPTER 2. DATA REDUCTION AND ANALYSIS 42 Lastly, we converted the units of the original images from Janskys (Jy) per beam (see Section 2.6 for the definition of a beam) to Janskys per steradian 9 (sr), taking into account the Gaussian response function of the instrument beam which is a factor of ln In the end we had two SCUBA maps that were ready for background subtraction (see Section 2.5). 2.4 Supplementary data MASS data To help constrain our results, we obtained near-infrared images from the 2MASS survey archives in the J (1.25 µm), H (1.65 µm), and K (2.17 µm) bands, as well as radio maps from the VLA archives at 3.6 cm and 6.2 cm. The 2MASS images are All-Sky Release Survey Atlas Images and were retrieved using the Interactive 2MASS Image Service, run by the NASA/IPAC Infrared Science Archive 11. To prepare these 2MASS images for background subtraction and convolution, it was necessary to convert the units from the default 2MASS Data-Number (DN) units to units of Jy/sr (see Table 2.1 for the specific conversion factors). The headers of these images contain the zero point magnitudes (m z ) for each band, and with that information we can use the following equation to convert the 9 A steradian is a unit of angular area called a solid angle, with the entire surface of a sphere covering 4π sr (Zeilik & Gregory, 1998). 10 Beam response functions are often Gaussian in shape, and therefore not uniform. 11 The NASA/IPAC Infrared Science Archive is operated by the Jet Propulsion Laboratory, California Institute of Technology, under contract with the National Aeronautics and Space Administration.

55 CHAPTER 2. DATA REDUCTION AND ANALYSIS 43 Table 2.2: Flux values for the J, H and K bands for a zero-magnitude zero-point conversion (Cohen et al., 2003). Wavelength (µm) Flux at 0-mag, F 0 (Jy) Zero point magnitude, m z (mag) ± ± ± flux in each pixel from data-number units to a magnitude (mag; Cutri et al., 2003): m 1 = m z 2.5 log 10 (S), (2.1) where m 1 is the calibrated magnitude, m z is the zero point magnitude listed in the 2MASS image header under the keyword MAGZP, and S is the flux in a given pixel, in DN units. We also have the standard equation relating flux to magnitude, ( ) F1 m 1 m 0 = 2.5 log 10, (2.2) F 0 where in this case, m 0 is set to zero and F 0 is the flux of the object when its magnitude is zero. These fluxes are listed in Table 2.2, as well as the zero point magnitudes. What we ultimately want to solve for is F 1 in terms of S. Combining Equations (2.1) and (2.2) we find F 1 = F 0 S10 mz/2.5, (2.3) where we have set m 0 = 0. Since the given DN values apply to any single pixel our

56 CHAPTER 2. DATA REDUCTION AND ANALYSIS 44 last step is to convert these values to units of Jy/sr. The solid angle of a pixel is Ω pix = (d pix π ) 2 sr (2.4) 180 where d pix is the diameter of one pixel, taken to be the image platescale in arcseconds per pixel. We find for a platescale of 1 /pix Ω pix = sr, (2.5) and thus obtain F 1 = F 0S10 mz/ , (2.6) where S is the image array of pixels. The calculated values of the conversion factors F J, F H and F K are listed in Table VLA radio data The VLA is an interferometer comprised of metre diameter antennae which can be positioned in five different arrangements to vary resolution 12. The VLA radio images we have were obtained from the NRAO s Data Archive System. Background continuum information is not detected by the interferometer during observations with the VLA, as the largest angular scale it can detect is λ/d, where λ is the observing wavelength and D is the closest distance between any two antennae in the array. In our case, observations were taken with the interferometer in the D configuration, so the largest angular scale that can be detected is approximately 180 for the 3.6 cm image and 300 s for the 6.2 cm image. For details see Ulvestad et al. (2007). Therefore, we 12

57 CHAPTER 2. DATA REDUCTION AND ANALYSIS 45 did not have to carry out a background subtraction step on these data. The units of these images were in Jy/beam, so we converted these units to Jy/sr following the same method as for the SCUBA images. The beam sizes and calculated conversion factors are listed in Table Background flux evaluation The main reduction step that we had to correctly apply to the majority of the maps (MIPS and VLA maps excluded) was the subtraction of any background and/or foreground flux present in the maps we obtained. As we cannot fully discern between foreground and background emission we will refer to their combined contribution as the background flux. We emphasise that only the diffuse background emission is evaluated here, as the low Galactic latitude of IC 10 prevents us from discerning emission from other objects between us and the galaxy along the line of sight. To accurately determine the background fluxes, we decided to test two different methods and then proceed with the better of the two. The first method we deemed the Box Method and the second method we called the Gaussian Method. Both methods are described below The Box Method This method was the simpler of the two methods we used to evaluate any background contribution to the total flux in the maps. We carefully examined each map for regions that contained only distinct background flux, and visually selected three boxes or regions that we were confident were located beyond the extent of the galaxy itself. In

58 CHAPTER 2. DATA REDUCTION AND ANALYSIS 46 Figure 2.5: Regions used to calculate the background flux with the box method highlighted in red and superimposed on the Spitzer 8 µm image. Figure 2.5 we show as an example, our 8 µm image with the three boxes highlighted in red that we selected to carry out our calculations. For each box, the mean flux value inside was calculated using two programs written in the Interactive Data Language (IDL; see Appendix B). The results from each box were then averaged to obtain one final value for the background flux. We took the standard deviation of the mean to be the error for each box, then propagated these errors to determine the error for the final value of the background contribution. The program also overlays the boxes chosen onto the image so that it is clear where the boxes are located with respect to the galaxy. The background flux values obtained with this method for the Spitzer 8 µm image are shown alongside those obtained with the Gaussian method (see next subsection) in Table 2.3.

59 CHAPTER 2. DATA REDUCTION AND ANALYSIS The Gaussian Method The basic idea behind this method is that if we plot a histogram of all of the pixels in a map of IC 10 including those containing source emission, and assume that the resulting background flux distribution should be a Gaussian distribution, then all of the pixels falling within flux bins located to the left of the central peak contain background flux (see Figure 2.6). Therefore, the left-hand side of the histogram should represent the actual shape of the distribution of the background flux, while the right-hand side of the histogram contains not only background flux, but also all of the source flux superimposed on top. The flux value corresponding to the central peak of the distribution then represents the average value of the background flux. To quantify this idea, we begin by creating a histogram of pixel count vs. flux for all of the pixels (excluding those pixels with a value of NaN) in an image. We adjust some of the key parameters of the histogram (i.e. the number of bins, and the minimum and maximum values of the flux) until a satisfactory histogram which has a clear underlying Gaussian shape due to the background flux is produced. The final histogram is then examined to see if it is asymmetrical, with the right tail falling off much more gradually than the left tail. The reason we look for this asymmetry is that all of the source flux is going to be especially high in comparison with the majority of pixels in the image, and will fall off gradually, creating an asymmetry in the histogram. For all of our images, the histogram was asymmetric, with the tail of increasing flux decreasing more gradually than the tail to the left of the peak. An example is shown in Figure 2.6, where we show the original histogram in black. Our next step is to create a symmetrical histogram using only the left-hand side of the original histogram including the peak itself. We select this part of the histogram

60 CHAPTER 2. DATA REDUCTION AND ANALYSIS 48 Figure 2.6: The original histogram (black), symmetrical histogram (red), and Gaussian fit to the symmetrical histogram (blue) for the Spitzer 8 µm image. and create a mirror image of it (excluding the peak which remains untouched) to represent the right-hand side of the background flux distribution. Concatenating both halves together results in a fully symmetric histogram representing the assumed Gaussian distribution of background flux. In Figure 2.6, the red line shows the symmetric histogram for our 8 µm image. Lastly, we fit a Gaussian function to our symmetric histogram using a predefined program in IDL. We chose to fit only three parameters in the function, which results in ( ) 2 x A1, (2.7) f (x) = A 0 exp 1 2 A 2 where A 0 is the height of the Gaussian function, A 1 is the centre value of the Gaussian fit, and A 2 is the standard deviation (σ) of the Gaussian. The fit to our histogram

61 CHAPTER 2. DATA REDUCTION AND ANALYSIS 49 Table 2.3: A comparison between background contributions obtained with the Box Method and those obtained with the Gaussian Method for a few select wavelengths. Wavelength (µm) Background Flux (MJy/sr) Box Method Gaussian Method ± ± ± ± ± ± 0.5 for the 8 µm data is shown in blue in Figure 2.6. Once we have a Gaussian fit to the symmetrical histogram, it is simple to obtain the background flux. We take the centre flux value (A 1 ) of this fit to be the background flux value, and the standard deviation of the distribution (A 2 ) is considered to be the error in the background flux value. While the background value was actually set as soon as we finished adjusting the original histogram, making all subsequent steps seem redundant, our motivation for carrying out these extra steps was to enable us to easily read off the background flux and determine its associated error. The program written to carry out this task can be found in Appendix C. We compared the results of the Box method and the Gaussian method to see how they performed, and all results agreed within error, as shown in Table 2.3. In the end we chose to use the Gaussian method because the magnitude of the error was marginally smaller for one wavelength than that obtained with the Box method, and because the values we determined with the Box method depended on where we chose to place our boxes. The Gaussian method makes use of all pixels in each map thereby eliminating any potential selection biases. In Table 2.1 we summarise the background contribution subtracted from each image, with its corresponding error. For the Spitzer MIPS images, which were already

62 CHAPTER 2. DATA REDUCTION AND ANALYSIS 50 background subtracted, we include the background flux values that had already been subtracted off. The resulting images are presented and analysed in Chapter 3, Figures 3.1a through 3.1q. In Section 2.6 we will describe the process of convolution as we applied it to these images. This is a crucial step in our analysis, as it allows us to carry out photometry on each image at the same resolution. 2.6 Convolution In order to conduct numerical and spatial analyses on all of the images simultaneously, and plot a spectral energy distribution (SED), each image should have the same platescale and must have the same beam size (or resolution). The beam size is related to the point spread function (PSF) of the telescope on which the observations were made. The PSF is the pattern that light from a point source (i.e. a star or distant galaxy) makes on the telescope s detector. When the light passes through all of the optics of a telescope it is diffracted (spread out) slightly so that when it is detected by the charge-coupled device (CCD) or other type of detector, a point source object appears smeared out. An example of this is shown in Figure 2.7, which is a comparison between the observed and theoretical PSF s for the MIPS instrument on Spitzer. Theoretically, the PSF is a circular diffraction pattern which is determined by the diameter of the telescope s objective (lens or mirror) as well as the wavelength of the light being observed (Zeilik & Gregory, 1998). For a given wavelength λ, and circular

63 CHAPTER 2. DATA REDUCTION AND ANALYSIS 51 Figure 2.7: A comparison between the observational and theoretical point spread functions (PSFs) of the MIPS instrument on Spitzer. Image from Rieke et al. (2004) aperture diameter D, we have θ 1.22( λ ), (2.8) D where λ and D must be in the same units. The value of θ is referred to as the angular resolution of the telescope in units of arcseconds 13. It is also the angular radius of the first dark ring of the diffraction pattern, called the Airy Disk ; it contains about 85% of the light from the source, with the rest of the light contained within the surrounding concentric rings (Young & Freedman, 2000). The beam size is considered to be the full width at half-maximum (FWHM) of the PSF. In Table 2.1 we present the beam size in arcseconds related to each wavelength, in addition to the platescale of each image. With this information, we can proceed with convolution. Convolution is a mathematical procedure that lowers an image s resolution using a convolving function. In order to compare all of our images, we need to convolve 13 For an ideal telescope this would be the diffraction limit of the aperture; however, the instrumentation on many telescopes cannot achieve this limit.

64 CHAPTER 2. DATA REDUCTION AND ANALYSIS 52 each image to the same resolution as the image with the poorest resolution. In our case, the image of IC 10 at 160 µm (shown in Figure 3.1m) has the poorest resolution at 40, so we convolved all of our images to this resolution. We carried out the convolution using a routine written in IDL that makes use of the native IDL function convol. The routine assumes that the initial FWHM of the convolving function is shaped like a Gaussian function with a FWHM ( ) of FWHM = (40 ) 2 θ 2 0, (2.9) where θ 0 is the initial resolution of the image we are convolving. A PSF with this condition is then generated and used within the convol program to convolve our initial image and create an image with the new resolution. The last step of this process is to regrid all of the convolved images so that they have the same platescale of 9 /pix as the MIPS 160 µm image. This is carried out with the program hastrom found in the astronomy library of IDL routines 14. Once this is done, the images are ready to for us to extract the flux in the regions IC 10 SE and IC 10 NW and create the observed SEDs. 2.7 Flux evaluation The main focus of our study are the regions known as IC10 SE and IC 10 NW that we introduced in Chapter 1, where SE and NW stand for south-east and northwest, respectively (see Figure 2.8 for the locations of these regions). To create their IR spectral energy distributions (SEDs) we need to extract the flux density within 14 Astrolib is a library of IDL routines specifically written for astronomy purposes. The home of this library is < >.

65 CHAPTER 2. DATA REDUCTION AND ANALYSIS 53 Figure 2.8: Apertures centred on IC 10 SE (lower left) and IC 10 NW (upper right) of the convolved MIPS 24 µm image. The red circles show our apertures while the region between each set of green circles highlights the regions used to by the program to evaluate the local background. The blue crosses mark the centres of our apertures which correspond to the centres of the emission peaks. apertures centred on these two regions. We chose aperture sizes that are large enough to encompass as much of the region as possible, without including too much local flux from the galaxy surrounding these regions. We also chose two surrounding annuli that determine local background, as the program we used to evaluate the flux required us to do so. This is because the program is set up for point source photometry (i.e. for stars); however, the rings were ignored during actual calculations as we already determined the background contribution. Figure 2.8 shows the two regions in which we calculated the integrated flux density (red), as well the rings used to calculate the local background (green). In Table 2.4 we present the specific details about the apertures we chose, and their respective rings.

66 CHAPTER 2. DATA REDUCTION AND ANALYSIS 54 Table 2.4: Aperture characteristics. Region Centre of Radius of Radii of Local Number of Pixels Aperture Aperture Background Rings in Aperture (RA, DEC) a ( ) ( ) IC 10 SE 0 h 20 m 28.5 s, IC 10 NW 0 h 20 m s, a right ascension, declination To calculate the integrated intensities within the two apertures, we used a modified IDL routine from the Astrolib library, that takes the aperture radii, coordinates and background rings as the main inputs and returns the flux contained within the given aperture with error, as well as the mean background in the ring with error. We do not use the error calculated by the program, but rather a combination of uncertainties we evaluated, which are described in the next section. The values we obtained for the integrated flux are listed in Tables 2.7 and 2.8 along with all associated uncertainties. Next we will discuss the other uncertainties we must take into account when determining the total error for each aperture. 2.8 Error analysis There are several steps required in order to properly evaluate the errors that are present in our images. There is an error associated with our background evaluation as mentioned above in Section 2.5, which is the noise in the image. There is also a calibration error associated with each instrument itself, and for some wavelengths an aperture correction was also necessary. Lastly, we evaluate the non-dust contributions to the submillimetre bands, as well as the radio continuum contributions at each

67 CHAPTER 2. DATA REDUCTION AND ANALYSIS 55 waveband. We will discuss each source of error or contribution, and our methods to deduce them below Background noise The noise in each image is due to the small fluctuations in the background value. Recall in Section 2.5 we measured the background values of the original images using the Gaussian method, which demonstrated that these small fluctuations exist through the distribution itself. To evaluate the noise present in our convolved images, we need to propagate the noise errors in our background subtracted images. To do this we first create artificial noise maps containing a Gaussian distribution of pixel values with a standard deviation equal to that of the noise in the original image. This is accomplished by scaling the distribution (created with the Randomn function in IDL) by the standard deviation. Each noise image generated has the same dimensions as the corresponding original, and maintains the overall background characteristics of the original map. The next step is to convolve the noise maps following the same procedure as for the original images (see Section 2.6). This is required as it is the only way to properly propagate the noise errors and correctly measure the noise in the convolved images. After convolving the images, we regrid each image as was done for the original convolved images, and finally obtain a noise map for each wavelength treated identically as its corresponding original image. From these noise images it is now a simple matter of evaluating the standard deviation of the convolved noise maps, which gives us the noise present in one pixel. These errors must be adjusted by multiplying the noise per pixel by the square root of the number of pixels contained within each aperture.

68 CHAPTER 2. DATA REDUCTION AND ANALYSIS 56 One caveat with the 160 µm, 450 µm, and 850 µm images is an increase in the magnitude of the noise in the vicinity of IC 10 NW (see Figures 3.1m, 3.1n and 3.1o). As we felt that the noise level calculated using the method just described was not sufficiently large enough to accommodate this increase, we estimated the noise based on a comparison of the source flux of IC 10 NW to the background flux in the immediate vicinity. As a result we conservatively set the noise at 40 % of the total flux within IC 10 NW for the 160 µm, and 50 % of the total flux for the 450 µm and 850 µm images Calibration errors and aperture corrections The calibration errors are related to the telescope and observing instrument used during observations. Most of the calibration corrections were applied to the data before we obtained them; however, two further corrections were necessary. The first requires an aperture correction by applying a multiplicative factor to the aperture fluxes from the 450 µm image to correct for the flux from the error beams (Dr. Wilson; private communication). The error beams are the side lobes of the light diffraction pattern seen by the detector. Most of the light falls within the main beam (recall in Section 2.6 we defined the beam size as the FWHM of the PSF); however, some light is detected on either side of the main beam due to the constructive interference of the light (see Figure 2.7). If an object has an angular size that is larger than the width of the main beam, these side lobes will measure flux external to the main beam, which is then added to the flux measured by the main beam, overestimating the true flux as a result. The response of the detectors can be studied to model this pattern and corrections can be applied to the data to account for the flux in the side

69 CHAPTER 2. DATA REDUCTION AND ANALYSIS 57 Table 2.5: Multiplicative factors for aperture correction. a Reach et al. (2005) b Dr. Wilson; private communication Wavelength Aperture Correction (µm) Multiplication Factor a a a a (IC 10 SE) b 0.95 (IC 10 NW) b lobes. In addition, Reach et al. (2005) suggest applying multiplication factors for aperture corrections to the IRAC data as well. The multiplication factors we used are presented in Table 2.5. The other calibration correction we applied to our data dealt with absolute calibration errors, since we are using observations taken with six different instruments. Publications such as Rieke et al. (2008) and Reach et al. (2005) give some comparisons between a few instruments, but it is difficult to extend this to all of the instruments we are using. Given the information we have from these two papers, as well as information from the various instrument manuals we have estimated the calibration errors. Note that these errors are necessary in order to compare data from numerous telescopes but they do not affect the variations from pixel to pixel in the images. These values are shown in Tables 2.7 and 2.8 and comparing them to the other sources of error, they are the dominant source in the majority of cases.

70 CHAPTER 2. DATA REDUCTION AND ANALYSIS Waveband contamination As we are specifically studying the dust SED of IC 10 rather than the entire spectrum, we have to remove any contribution to our images that is not accounted for by the SED model to be used to fit our observations (see Chapter 3). More specifically, at 850 µm there is radiation present due to the J = 3 2 rotational transition of carbon monoxide (CO). CO emission contributes to a large fraction of the total molecular mass within a galaxy, and is often used as a tracer of H 2, the dominant source of molecular mass. It has been detected in IC 10 through several emission lines such as J = 1 0 transition or C ii, and numerous groups have mapped out the distribution of CO within the galaxy (e.g. Wilson & Reid, 1991; Madden et al., 1997; Leroy et al., 2006). As a result of its strong presence within IC 10, we need to determine the contribution from the CO(J = 3 2) transition since it falls within the 850 µm band, and adjust our flux values if necessary (i.e. we need to compare the energy of the CO(J = 3 2) line with the total energy within the 850 µm band). To estimate the CO(J = 3 2) contribution to the 850 µm waveband emission, we refer to studies of CO carried out by Bayet et al. (2006) and Leroy et al. (2006). From the survey of Bayet et al. (2006) we were able to determine the ratio of the intensities of to the J = 3 2 to J = 1 0 transitions, finding that I 3 2 /I 1 0 = Next, we use the intensity distribution map for the CO(J = 1 0) transition from Leroy et al. (2006) to determine the intensity of that line within IC 10 SE and IC 10 NW. Since the beam size of the telescope used in this survey (the Arizona Radio Observatory (ARO)) is 55, it is approximately the diameter of the aperture we use for IC 10 NW and we were able to use the intensity at this location directly. However, the aperture we use for IC 10 SE is larger than the area of the beam by

71 CHAPTER 2. DATA REDUCTION AND ANALYSIS 59 a factor of 3.6, so we need to increase the intensity measured within the telescope s beam accordingly. We find that the intensity of the CO(J = 1 0) emission at the position of IC 10 SE is approximately 8.1 K km s 1 for the ARO beam size, which corresponds to an intensity of 4.5 K km s 1 for the CO(J = 3 2) emission. This translates to an intensity of approximately 16.3 K km s 1 within the aperture for IC 10 SE, assuming a uniform intensity distribution. For IC 10 NW we find an intensity of 1.0 K km s 1 for the CO(J = 1 0) emission, corresponding to an intensity of 0.56 K km s 1 for the CO(J = 3 2) emission. Next we convert these intensities to units of Jy km s 1 using a conversion factor of 25 Jy/K determined specifically for the ARO, finding intensities of 408 Jy km s 1 and 14 Jy km s 1 for IC 10 SE and IC 10 NW, respectively. To make a comparison of these values to our measured flux at 850 µm we have to multiply our flux values by the width of the 850 µm filter in units of km s 1. From Table 2.1 we find that the width of this filter is 70 µm, which is equivalent to km s 1 using the relations ν = λc/λ 2 and v = νc/ν, where λ, ν and v are the filter bandwidth in terms of wavelength, frequency and velocity, respectively. Using the flux measured within IC 10 SE and IC 10 NW from Tables 2.7 and 2.8 and the bandwidth in terms of velocity, we find intensities of Jy km s 1 and Jy km s 1 for IC 10 SE and IC 10 NW, respectively. Comparing these values to the emission contribution of the CO(J = 3 2) transition, we find that the CO(J = 3 2) emission is negligible, contributing only 3 % and 1 % to the total flux at 850 µm for IC 10 SE and IC 10 NW, respectively. We also have to remove any radio continuum that has extended into our submillimetre images. In order to quantify the flux in these images from the radio continuum,

72 CHAPTER 2. DATA REDUCTION AND ANALYSIS 60 we evaluated it first assuming that the continuum included both non-thermal and thermal emission, but was dominated by non-thermal emission, and then assuming only thermal emission. Non-thermal emission is generally in the form of synchrotron radiation. This radiation mostly stems from free electrons travelling at very high speeds in the presence of a magnetic field. The electrons spiral around the field lines, and are accelerated as a result. Eventually, the accelerated electron will emit a photon. Thermal emission tends to occur most often in regions where a plasma of free electrons and positive ions exists, such as an H ii region. This radiation is called Bremsstrahlung radiation. When an electron approaches an ion it is accelerated into a new orbit around the ion, subsequently emitting a photon to compensate for the change in energy required to alter its orbit. The energy of the electrons involved in Bremsstrahlung radiation reflects the temperature of the gas it resides in, which is why it is considered to be thermal emission. For the combined continuum we needed to determine the spectral index associated with it. The radiation follows the relation F = Cλ α, (2.10) where F is the flux from the radio continuum, and α is the spectral index that includes both thermal and non-thermal emission. It is assumed to be described by the slope between 3.55 cm and 6.2 cm, where we know that non-thermal emission dominates the total. The variable C is a constant. Since we have two different fluxes for two different radio wavelengths we can solve for C and α. In Table 2.6 we present the data used to determine Equation (2.10) for both regions and the resulting equations

73 CHAPTER 2. DATA REDUCTION AND ANALYSIS 61 Table 2.6: Radio data points used to extract radio continuum equation. Wavelength, log(λ) IC 10 SE IC 10 NW λ (µm) (µm) Flux, F (Jy) log(f) (Jy) Flux, F (Jy) log(f) (Jy) are F SE = ( )λ (2.11) for IC 10 SE and F NW = ( )λ (2.12) for IC 10 NW. Wielding these two equations, we can now calculate the non-thermal radio continuum contribution for all wavelengths. The non-thermal radio continuum contributions are shown in Tables 2.7 and 2.8. The continuum due to solely thermal emission can be approximated by Equation (2.10) with α = 0.1. Using data only from the µm radio image, we can determine the value of C for each aperture. As already stated we know that at 3.55 cm the emission is dominated by non-thermal radiation which falls off more rapidly than for the thermal emission. Therefore, extrapolating the thermal emission from this point will give us the upper limit on the total emission at shorter wavelengths. Thus, we obtain F SE = ( )λ 0.1 (2.13) for IC 10 SE and F NW = ( )λ 0.1 (2.14)

74 CHAPTER 2. DATA REDUCTION AND ANALYSIS for IC 10 NW. The contribution due to the thermal continuum is also shown in Tables 2.7 and 2.8. Comparing these values at each wavelength with those obtained from the combined continuum we see that the contribution from a solely thermal continuum is higher because we extrapolated it to shorter wavelengths, whereas the value of α for the total emission assumes that the thermal continuum falls off rapidly and the non-thermal radiation dominates at all wavelengths. The important thing to note here is that regardless of the type of continuum, the total contribution due to radio continuum is negligible to the total flux in the apertures. Tables 2.7 and 2.8 present the integrated flux as measured by our routine, all individual error contributions, and finally the net flux with associated error. In Chapter 3 we will introduce the program we used to model the dust SEDs of each region, along with our best-fitting models. 62

75 Table 2.7: Flux in apertures and associated error contributions for IC 10 SE. Wavelength Flux Noise Calibration Error from Local Non-thermal Radio Thermal Radio Net Flux Error Background Contribution Contribution (µm) (Jy) (Jy) (%; Jy) (Jy) (Jy) (Jy) (Jy) %; ± %; ± %; ± %; ± %; ± %; ± %; ± %; ± %; ± %; ± %; ± %; ± %; ± %; ± %; ± %; ± %; ± CHAPTER 2. DATA REDUCTION AND ANALYSIS 63

76 Table 2.8: Flux in apertures and associated error contributions for IC 10 NW. Wavelength Flux Noise Calibration Error from Local Non-thermal Radio Thermal Radio Net Flux Error Background Contribution Contribution (µm) (Jy) (Jy) (%; Jy) (Jy) (Jy) (Jy) (Jy) %; ± %; ± %; ± %; ± %; ± %; ± %; ± %; ± %; ± %; ± %; ± %; ± %; ± %; ± %; ± %; ± %; ± CHAPTER 2. DATA REDUCTION AND ANALYSIS 64

77 Chapter 3 Results In this chapter, we present the 17 images of IC 10 at their highest resolution and describe the various morphological characteristics of each image and how they change with wavelength. The Spectral Energy Distribution (SED) model we used to fit our observational data is introduced, and the results for various models are presented. 3.1 Morphology of IC 10 The background subtracted images at their original resolution are presented in Figures 3.1a to 3.1q. They are presented in order of increasing wavelength from 1.24 µm to 6.2 cm to emphasise the change in morphology IC 10 undergoes as we look from the near infrared (NIR) to radio wavelengths. At near-infrared (NIR) wavelengths IC 10 reveals its older, cool stellar population. Note that point sources in these images are foreground stars from our own Galaxy. In the J-band (1.24 µm), H-band (1.66 µm) and K-band (2.16 µm) images, IC 10 looks like a compact elliptical galaxy, 65

78 CHAPTER 3. RESULTS 66 surrounded by extended emission which extends farther out with increasing wavelength. At 3.6 µm we are looking at primarily the older stellar population of the galaxy (Hunter et al., 2006), and it is in this image we begin to see faint, extended structure with emission likely from very hot dust, which is in close proximity to the hot young stars at the hearts of these star forming regions (SFRs). In addition, the bandwidth of the 3.6 µm filter also detects emission from the 3.3 µm PAH line. Moving to the 4.5 µm image, emission here corresponds to hot dust as well, in addition to some contribution from stars. IC 10 SE is now resolved into two separate smaller regions, while IC 10 NW is also starting to take shape in the arc to the northwest (see Figure 1.10 for positions of IC 10 SE and IC 10 NW). A second, much fainter arc is barely visible extending far up to the north, above the first arc. In the mid-infrared (MIR) images 5.8, 6.75, 8.0 and 11.4 µm we are seeing emission dominated by polycyclic aromatic hydrocarbons (PAHs). There is significant structure in these images, as IC 10 SE and IC 10 NW are now very well defined, with more diffuse emission in between. The arcs extending up and to the northeast have now almost completely closed to form loops of material. It has been observed that in the regions within these loops are very hot O and B-type stars, and an unusually high density of Wolf-Rayet (WR) stars which all possess stellar winds capable of blowing out material and causing it to build up around the edges (Hunter, 2001). The 15 µm image primarily picks up continuum due to the warm dust, with the strongest diffuse structure located in between IC 10 SE and IC 10 NW, which are both still very prominent. The MIPS 24 µm image shows an extensive distribution of warm dust. In this image we see smaller regions of strong emission, most distinctly in the upper northwest

79 CHAPTER 3. RESULTS 67 Figure 3.1a: Background subtracted images with original resolution, in order of increasing wavelength. Contours are overlaid for clarity with the lowest contour in each image corresponding to the 3σ level. For all images, North is upwards and East is to the left, and the greyscale and contours are in units of MJy/sr. The blue circles and crosses mark the aperture sizes and centres of IC 10 SE and IC 10 NW. J-band (1.24 µm): Contours are from 0.6 MJy/sr to 3.0 MJy/sr in increments of 0.6 MJy/sr (3σ).

80 CHAPTER 3. RESULTS 68 Figure 3.1b: H-band (1.66 µm): Contours are from 0.9 to 5.4 MJy/sr in increments of 0.9 MJy/sr (3σ).

81 CHAPTER 3. RESULTS 69 Figure 3.1c: K-band (2.16 µm): Contours are from 1.2 to 5.2 MJy/sr in increments of 2.5 MJy/sr.

82 CHAPTER 3. RESULTS 70 Figure 3.1d: 3.6 µm: Contours are from 0.06 to 2.46 MJy/sr in increments of 0.3 MJy/sr.

83 CHAPTER 3. RESULTS 71 Figure 3.1e: 4.5 µm: Contours are from 0.06 to 3.06 MJy/sr in increments of 0.5 MJy/sr.

84 CHAPTER 3. RESULTS 72 Figure 3.1f: 5.8 µm: Contours are 0.3, 1.3, 3.3, 5.3, 7.3 and 9.3 MJy/sr.

85 CHAPTER 3. RESULTS 73 Figure 3.1g: 6.75 µm: Contours are from 0.9 to 9.9 MJy/sr in increments of 1.5 MJy/sr.

86 CHAPTER 3. RESULTS 74 Figure 3.1h: 8 µm: Contours are from 0.6 to 20.6 MJy/sr in increments of 4.0 MJy/sr.

87 CHAPTER 3. RESULTS 75 Figure 3.1i: 11.4 µm: Contours are from 1.5 to 21.5 MJy/sr in increments of 4.0 MJy/sr.

88 CHAPTER 3. RESULTS 76 Figure 3.1j: 15 µm: Contours are from 1.5 to 26.5 MJy/sr in increments of 6.0 MJy/sr.

89 CHAPTER 3. RESULTS 77 Figure 3.1k: 24 µm: Contours are 0.3, 5.3, 15.3, 30.3, 50.3, 100.3, and MJy/sr.

90 CHAPTER 3. RESULTS 78 Figure 3.1l: 70 µm: Contours are 6.0, 12.0, 24.0, 48.0, 96.0, 192.0, and MJy/sr.