Observations and models of high redshift Radio Galaxies and Quasars from the 3 rd Cambridge catalog. Dissertation

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1 Observations and models of high redshift Radio Galaxies and Quasars from the 3 rd Cambridge catalog Dissertation zur Erlangung des Grades Doktor der Naturwissenschaften an der Fakultät für Physik und Astronomie der Ruhr-Universität Bochum von Frank Heymann aus Leipzig Bochum 2010 b.w.

2 2 1. Gutachter Prof. Dr. Rolf Chini 2. Gutachter Priv.-Doz. Dr. Dominik Bomans Datum der Disputation

3 3 Abstract This thesis provides new observations of the most powerful high redshift Active Galactic Nuclei (AGN), namely the complete z > 1 3CR sample, and new dust radiative transfer modeling of their measured spectral energy distribution in the infrared. This work is separated into three main parts, two observational sections and one section containing the modeling. The first part shows observational results in the near (NIR) and mid (MIR) infrared obtained with the Spitzer Space Telescope, to extend the knowledge on high redshift sources. The main aspect of these observations is to study orientation dependence of the NIR and MIR emission and to confirm the unification scheme for the most powerful high redshift AGNs. The second part reports on a pilot study to detect galaxy clustering around high redshift radio sources using the Spitzer data. Because the radio AGN reside in massive host galaxies, they are expected to serve as signposts for cosmic mass peaks. These galaxy clusters are among the most distant known structures and therefore of particular cosmological interest. The third part explains a newly developed method to solve the radiative transfer equation in three dimensional configurations. This method makes use of the parallelization capabilities of modern vector computing units, like the graphics cards. The speed improvement is about a factor of 100. This enables us to model the close environment of AGN in so far unprecedented detail within reasonable computing time.

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5 Contents 1 Introduction Aim of this thesis Active Galactic Nuclei Seyfert Galaxies Quasars & Radio Galaxies Unification Galaxy Clustering Dust radiation The emissivity of dust The temperature of big grains Very small grains Basic Radiative transport Definitions The general transfer equation Analytical solutions Near- and mid infrared photometry Motivation Observations and Data Results and Discussion Radio galaxies as obscured quasars Evolutionary effects and non-thermal contributions Conclusions The cluster search Motivation

6 6 CONTENTS 3.2 Clustering around 3C Observations and data Results No evidence for clustering around 3C Observations and data Results Preliminary Conclusion Parallel 3D radiative transport Theory Monte Carlo method Parallelization Pseudo random number generator in parallel CUDA TM parallelization on graphic cards Imaging Solar system Benchmark test Spherical symmetry (1D) Disk geometry (2D) Dust properties Spiral expansion of disk structure (3D) Clumpy Torus geometry (3D) Modeling average spectra & SEDs Summary and Outlook 81 Bibliography 87 List of Figures 93 List of Acronyms 101

7 Chapter 1 Introduction 1.1 Aim of this thesis At the begin of my thesis, new unprecedented infrared observations of the complete high-redshift 3CR sample have been obtained with the Spitzer Space Telescope. Therefore, the aim of this thesis is to explore the near- and mid-infrared spectral energy distributions of this sample, comprising the most powerful radio-loud AGN, to test, how far it is possible with these data to detect galaxy clustering around these mass peaks of the early universe, and to develop a new proper 3D Monte Carlo radiative transfer code to model the spectral energy distributions. These three tasks represent new challenges and thus lead to new results. 1.2 Active Galactic Nuclei Active Galactic Nuclei (AGN) belong to the the most luminous objects in the universe. The luminosity of an AGN is provided by accretion of matter onto the central supermassive black hole: L AGN = ǫ Ṁ c L ǫ 0.1 Ṁ M /yr, (1.1) where ǫ is the efficiency of the mass to radiative energy transfer, L solar luminosity, M solar mass and Ṁ the mass accretion. This leads to a theoretical 7

8 ACTIVE GALACTIC NUCLEI upper limit for the central luminosity, named after Arthur Stanley Eddington: L max < L Edd = 4πGM BHm p c σ T L M BH 10 7 M, (1.2) where m p is the mass of the proton, σ T the Thomson cross section for interaction between electrons and protons. The wavelength spectral energy distribution of an AGN exhibits three characteristic features (Elvis et al. 1994) as shown in Fig 1.2: Figure 1.1: Sketch of an AGN continuum spectrum of the nuclear region, without stellar contribution. Three different bumps can be seen (Big Blue Bump in the middle, Infrared Bump on the left and X-ray Bump on the right). Figure from Manners (2002) Infrared Bump This feature consists of several components. Dominant is the emission from the hot and warm dust torus (red dashed line) and cooler dust from the host galaxy. The starburst activity in the host galaxy contributes to the far infrared (purple dotted line) followed by a steep decrease of the Infrared bump to the submillimeter (Chini et al. 1989). The local minimum at around 1µm is given by the sublimation temperature of the dust around 1500 K.

9 CHAPTER 1. INTRODUCTION 9 Big Blue Bump At shorter waverlengths the minimum turns into the Big Blue Bump (blue dashed-tripple-dotted line). This bump comes from the thermal emission of hot gas (5 000K K) heated by viscous processes, in the accretion disk. The gap in the bump results from absorption of neutral hydrogen and therefore missing data. This optical/uv radiation is efficiently transfered into infrared emission and therefore powers the Infrared Bump. (Miley et al. 1985) X-ray Bump The final feature in the AGN continuum is the high energy X-ray Bump. The radiation in this bump is produced by the hot corona above the accretion disk (green dashed line) and reflection of the disk (blue dashed-dotted line). The observational data of my thesis provide new constraints on the Infrared Bump (chapter 2), and the model part makes use of all three bumps (chapter 4) Seyfert Galaxies These galaxies, discovered by Seyfert (1943), contain a bright nucleus with strong emission lines from highly ionised gas (hydrogen, helium, nitrogen, oxygen). The Seyfert galaxies can be divided into two subclasses depending on the existence of broad and narrow emission lines (type 1) or only narrow lines (type 2) (Khachikian & Weedman 1974). The broad lines have velocities of km s 1, may vary on short timescales and can be explained by Doppler broadening. These high velocities can be explained by gas clouds, orbiting the black hole at small distances. It is also possible that these lines are emitted from the accretion disc itself. However due to the extremly high resolution which is neccessary to resolve the innermost part, it is difficult to observe the exact geometry of these objects. The narrow emission lines may by emitted by gas clouds further out. This is strengthened by the fact that the narrow lines are detected in all types of Seyfert galaxies, which implies that the emitting region is large. Breakthrough spectropolarimetric observations revealed, that some Seyfert 2 galaxies contain a hidden broad line region, leading to the AGN unification scheme (section 1.2.3).

10 ACTIVE GALACTIC NUCLEI Quasars & Radio Galaxies These two object classes are the powerful radio-loud cousins of the Seyfert galaxies. On early optical images quasars appeared starlike, which gave these objects the name quasi stellar radio source. In quasars and radio galaxies large structures, the radio lobes, are prominent. Depending on the morphology and power of the radio lobes, the radio sources are subdivided into the FR I and FR II classes (Fanaroff & Riley 1974). We here consider only the edge-brightened powerful FR II sources. The radio emission is powered by synchrotron radiation of outflowing material reaching 95% of the speed of light. Assuming an accretion disc and perpendicular to this disc an outflowing jet producing the synchrotron radiation, it is possible to explain these two objects with the orientation of the jet to the line of sight. The quasars, where the jet points to the observer, represent the type 1 AGN and the Radio Galaxies, where the jet is perpendicular to the line of sight, can be classified as type 2 (Barthel 1989; Orr & Browne 1982). The isotropic radiation at meter wavelengths enables to select objects with the same radio power and to study orientation dependent effects. While for the local universe (z < 1) orientation dependencies are fairly well studied, the knowledge is poor at high redshift (z > 1) Unification Figure 1.2 shows the actual picture of the unification model. It is believed that Type 1 and Type 2 galaxies are in essence the same, and they differ only by the angle at which they are observed. This unification scheme was proposed by Antonucci (1993). In Type 2 Seyferts the broad line component, which is produced by high velocity clouds in the vicinity of the black hole, is obscured by dust in a torus like structure. The obscuration then depends on the observing angle onto the dusty torus. The narrow emission lines are produced in clouds with lower rotation velocities and therefore larger distances to the black hole presumably in a bicone far outside the obscuring torus scale hight. Perpendicular to the accretion disk exists a jet of outflowing material, to remove the angular moment from the accretion disk. This outflow reaches speeds of up to 95% of the speed of light, and produces synchrotron radiation in the radio wavelength. In some Type 2 Seyfert galaxies the broad component can be observed in polarized light. In these objects the broad-line region is scattered by material in the bicone (dust or electrons), which allows us to see it indirectly. This effect was first discovered by Antonucci (1983) in the Type 2 Seyfert NGC 1068.

11 CHAPTER 1. INTRODUCTION 11 Narrow Line Region Jet Broad Line Region Black Hole Accretion Disk Obscuring Torus Figure 1.2: This figure shows the unification scheme from Urry & Padovani (1995). In the center the black hole with its accreating disk is shown. Perpendicular to this disk is a jet of outflowing material. Close to the black hole are clouds which produce the broad emission line features with speeds of up to kms 1. The torus of dust shields the broad line region depending on the observing orientation. Further out is the narrow line region. 1.3 Galaxy Clustering Clusters of nebulae (Zwicky 1937) have been recognised later as cluster of galaxies. When observed visually, clusters appear to be collections of galaxies held together by mutual gravitational attraction. However, their velocities are too large for

12 DUST RADIATION them to remain gravitationally bound by their attractions, implying the presence of either an additional invisible mass component, or an additional force besides gravity. X-ray studies have revealed the presence of large amounts of intergalactic gas known as the intracluster medium (ICM). This gas is very hot, between 10 7 K and 10 8 K, and therefore emits X-rays in the form of bremsstrahlung and atomic line emission. The total mass of the gas is greater than that of the galaxies by roughly a factor of two. However, this is still not enough mass to keep the galaxies in the cluster. Since this gas is in approximate hydrostatic equilibrium with the overall cluster gravitational field, the total mass distribution can be determined. It turns out that the total mass deduced from this measurement is approximately six times larger than the mass of the galaxies or the hot gas. The missing component is called dark matter and its nature is unknown. In a typical cluster perhaps only 5% of the total mass is in the form of galaxies, maybe 10% in the form of hot X-ray emitting gas and the remainder is dark matter. Noteworthy, Brownstein & Moffat (2006) use a theory of modified gravity to explain X-ray cluster masses without dark matter. Clusters typically have the following properties: They contain 50 to 1,000 galaxies, hot X-ray emitting gas and large amounts of dark matter They have total masses of to M and sizes of 2 to 10 Mpc. The velocity distribution for the individual galaxies is about kms 1. Well known galaxy clusters in the relatively nearby universe include the Virgo cluster, Hercules Cluster, and the Coma Cluster. A very large aggregation of galaxies known as the Great Attractor, dominated by the Norma cluster, is massive enough to affect the local expansion of the universe (Hubble flow). However, at large distances (redshift z > 1) cluster knowledge is very sparse, because of observational difficulties to discern cluster member galaxies from foreground galaxies. In chapter 3 a new pilot study to find high redshift (z 1.5) clusters is presented. 1.4 Dust radiation This section describes the physical background of the dust emission. A single grain emits according to Kirchhoff s law, which can be derived from the assumption of harmonic oscillators in thermal equilibrium. In section we discuss

13 CHAPTER 1. INTRODUCTION 13 the thermal emission. The big grain radiation is discussed in section and the very small grains in section The emissivity of dust A dust particle in a radiation field has an equilibrium temperature derived from the condition that it absorbs as much energy as it emits. The ratio of the emission coefficient ǫ ν to the absorption coefficient Kν abs depends only on the frequency of the radiation field and temperature of the dust grain. Planck discovered that this dependence can be described by a function B ν (T), named the Planck function. Astrophysical grains are usually not in local thermal equilibrium (LTE). But whenever it is heated by a photon the energy is distributed among the energy states. The distribution of the energy states derived by the Boltzmann function depends only on the dust temperature T. Therefore the grain emission only depends on T and is given, as in LTE, by ǫ ν = K abs ν B ν (T). (1.3) Then the emission into all directions equals 4πǫ ν. The value ǫ ν is called emissivity and can reference to a single particle, a unit volume or a unit mass. The dimension for the emissivity per unit volume is expressed in erg s 1 cm 3 Hz 1 ster The temperature of big grains The dust temperature can be calculated with Equ. 1.3 and the dust temperature T from the balance between heating and cooling by Kν abs J ν dν = K abs ν B ν (T) dν (1.4) where the left side of this equation describes the heating and the right side the cooling. J ν is the average of the radiation intensity over all directions. Equ. 1.4 neglects other heating or cooling mechanisms like collisions with gas particles. To account for these additional heating (cooling) one has to add them to Equ It is obvious that not only the absolute value of Kν abs determines the dust temperature, which means the grain is at high temperature if Kν abs is high at the absorption wavelength and low at the emission wavelength. For the simple case of a black-body the absorption coefficient is K abs ν constant. This simplifies equation

14 DUST RADIATION 1.4 to J ν dν = σ π T 4, (1.5) with σ the radiation constant. The temperature of a dust black-body at distance r from a star with bolometric luminosity L is given by L 4πr 2 = 4σT 4. (1.6) For the astrophysical example of our solar system, where L = L, one finds: T = 279 ( r 1 AU ) 2 K, (1.7) which is close to the surface temperature of the earth. This holds even if our planet emits like a perfect black body and the atmosphere reflects nearly 30% of the radiation from the sun Very small grains When a very small dust particle absorbs an energetic photon its temperature fluctuates. To compute these time variable temperatures their optical and thermal properties are needed. The optical behavior depends on the particle shape and two dielectric functions ǫ 1 (ω) and ǫ 2 (ω) and the thermal behavior is determined from the specific heat (see Krügel 2008). For a large number of very small identical interstellar dust grains the chance for one of them to have a temperature in the interval T...T + dt is P(T)dT. Thus the normalised probability density is: 0 P(T)dT = 1. (1.8) For big interstellar grains the temperature fluctuations are small, so that the probability density P(T) approaches the δ-function δ(t). The balance between emission and absorption is therefore given in equation 1.4. For very small particles we assume that the radiation fulfills Kirchhoff s law at any time. In the case of spherical particles we can compute the average monochromatic emission per solid angle by ǫ ν = π a 2 Q abs ν B ν (T)P(T) dt (1.9) The emission of one of these grains is not constant over time, but the whole ensemble radiates at any frequency at a steady rate. The solution of 1.9 is reduced to find P(T). A more detailed view of the emission of very small particles is

15 CHAPTER 1. INTRODUCTION 15 Figure 1.3: Definition of the radiative intensity I ν described by Guhathakurta & Draine (1989); Draine & Li (2001); Krügel (2008). 1.5 Basic Radiative transport Definitions The intensity I ν describes the radiation field and is defined as shown in Fig. 1.3 A surface element dσ at location r receives the power dw = I ν ( r, e) cos (θ)dσdωdν, (1.10) where e is the direction, n is the unit vector of the surface element, θ is the angle between e and n, dω is the solid angle and dν is the width of the frequency interval. The dimension of the intensity is erg s 1 cm 2 Hz 1 ster 1. For extended astrophysical sources of solid angle Ω S the observed total flux received at the observer is given by where Fν obs = I ν (θ,φ)dω, Ω S (1.11) dω = sin(θ)dθdφ (1.12) is the element of solid angle. For constant I ν (θ,φ) over the source Equ simplifies to F obs ν = I ν Ω S. (1.13)

16 BASIC RADIATIVE TRANSPORT The mean intensity J is the average over the total solid angle, The net flux F through a unit surface is: F = 2π π 0 J = 1 I( e)dω. (1.14) 4π 4π 0 I(θ, φ) cos (θ) sin (φ)dθdφ. (1.15) The general transfer equation The light changes its intensity by traveling through a dusty medium. The intensity is reduced by absorption and increased by the thermal emission of the dust and the scattering of photons out of and into the line of sight. The intensity change at position r into direction e can be written as di ν ds = Kext ν I ν ( r, e) + ǫ ν, (1.16) where Kν ext = Kν abs + Kν sca is the extinction coefficient per unit volume including the absorption and scattering. The emission and scattering of the dust at temperature T can be written as ǫ ν = K abs ν B ν (T) + Ksca ν 4π 4π p ν ( e, e)i ν ( e )dω. (1.17) The factor p ν ( e, e) is the phase function for scattering from e into e. A fundamental quantity in the light propagation is the optical depth, τ ν. When the light travels from a point P 1 to P 2 the optical depth is given by τ ν = P2 P 1 K ext ν ds, (1.18) or dτ ν = K ext ν ds. (1.19) For dusty media the extinction coefficient is independent of temperature, which simplifies the integral in Equ With the column density N d, the number of grains in a cylinder with a base of 1 cm 2, the optical depth is given by τ ν = N d C ext ν, (1.20)

17 CHAPTER 1. INTRODUCTION 17 where C ext ν is the extinction coefficient for one grain Analytical solutions It is possible to solve Equ for pure extinction without emission. This can be the light from an object, which is only decreased by the intervening dust. The solution is then I(τ ν ) = I(0)e τ, (1.21) where I(0) is the intensity of the source. The intensity I(τ) decreases exponential with the optical depth. The solution of Equ for pure emission without extinction (K ext = 0) is I(s) = I(0) + s 0 ǫ(s ) ds, (1.22) where s is the path length and ǫ the emission coefficient (Equ. 1.17). This situation is a good approximation for the sub-millimeter radiation of interstellar dust. The intensity for pure emission without a background source scales with the path length s. The more general case of background source emission plus dust emission and absorption is I ν (τ) = I(0)e τ + τ 0 S ν (τ )e τ τ dτ, (1.23) where S(τ) is the source function. For the simple case of a constant source function it is possible to solve the integral in equ We assume a dust cloud of uniform temperature T, the source function is given by the Planck function S ν (τ) = B ν (T). (1.24) This gives the solution for the intensity and flux of the absorption plus emission of dust on a background source by I ν (τ) = I(0) e τ + B ν (T) (1 e τ ) (1.25) and F ν (τ) = F(0) e τ + B ν (T) (1 e τ )Ω. (1.26) If the source emission is known or neglectable this equation gives the possibility to get the dust temperature and optical depth and therefore the dust mass. The

18 BASIC RADIATIVE TRANSPORT limiting solutions for Equ are B(T)τ + I(0) : for τ << 1 I ν (τ) = B(T) : for τ >> 1. (1.27) For low optical depth and no background source the radiation intensity received at the observer is directly proportional to the optical depth. The intensity of the received dust emission can be used to investigate dust properties like the dust mass or the grain frequency dependence. For very high optical depth the intensity is given by the black-body radiation. This means all information about the dust, with the exception of the dust temperature, is lost.

19 Chapter 2 Near- and mid infrared photometry This part represents one of the developed papers during my PhD thesis. My contribution in this large collaboration of many coauthors was mainly: Photometry of the entire high redshift 3C sample (Tables 5.1 and 5.2 in appendix 3C sources). Developing a pipeline to extract the photometry from the Spitzer mosaic images. Interactive inspection, verification and improvement of the spectral energy distribution of these faint sources. Abstract: The 178 MHz-selected sample is unbiased with respect to orientation and therefore suited to study orientation-dependent effects in the most powerful active galactic nuclei (AGN). Quasar and radio galaxy subsamples matched in isotropic radio luminosity are compared. The quasars all have similar spectral energy distributions (SEDs), nearly constant in ν F ν through the rest µm range, consistent with a centrally heated dust distribution which outshines the host galaxy contribution. The radio galaxy SEDs show larger dispersion, but the mean radio galaxy SED declines from rest 1.6 to 3µm and then rises from 3 to 8µm. The radio galaxies are on average a factor 3 10 less luminous in this spectral range than the quasars. These characteristics are consistent with composite emission from a heavily reddened AGN plus starlight from the host galaxy. The midinfrared colors and radio to mid-infrared spectral slopes of individual galaxies are also consistent with this picture. Individual galaxies show different amounts of extinction and host galaxy starlight, consistent with the orientation-dependent unified scheme. 19

20 MOTIVATION 2.1 Motivation When exploring the general evolution of galaxy populations across cosmic times, a particular challenge is to distinguish between black hole and star-forming activity. Star formation and obscuring dust go hand in hand, and black-hole-driven active galactic nuclei (AGN) are also surrounded by dust mainly distributed in a disk/torus-like geometry (Antonucci 1993). There is evidence that AGN mainly power the near- and mid-ir emission (NIR, 2µm; MIR, 10µm) from hot nuclear dust, while starbursts contribute mainly to the far-infrared (FIR, 60µm) luminosity (Rowan-Robinson 1995; Vernet et al. 2001; Schweitzer et al. 2006). Using the MIR/FIR luminosity ratio as an indicator for the relative AGN and starburst contributions, numerous studies have found an increase of AGN/starburst activity with total luminosity and redshift, but the validity of this trend is still under discussion because of selection effects on the various samples. More seriously, an unfavorable AGN orientation could cause MIR obscuration (e.g. Pier & Krolik 1993), leading to a fundamental observational degeneracy: a low MIR/- FIR luminosity ratio can be due to either a high star-forming contribution or to an AGN in which the hot dust is obscured. The spectral energy distribution (SED) of an obscured AGN may thus mimic that of a starburst-powered source. While this degeneracy has now been widely examined at low/intermediate luminosity and redshift (z < 1), it has still to be explored for the most luminous sources at high redshift (z > 1). In order to assess galaxy and AGN evolution in the universe, we therefore need to understand this AGN/starburst degeneracy for a population of luminous high-redshift sources. A crucial step towards this is to study the orientation dependence of the NIR and MIR emission of high-redshift AGN. Orientation-dependent effects can only be tested and quantified with AGN samples having type 1 (unobscured) and type 2 (obscured) subsamples matched in isotropic emission. The clean AGN tracers optical, [O III] λ5007å, NIR, and X-ray (<10 kev) all fail to fulfill this requirement. The [O II] λ3727å emission, while isotropic (Hes et al. 1993), is probably dominated by extended starbursts and shocks (Best 2000) rather than by the AGN. Therefore, the only feasible way is low-frequency (meter-wavelength) radio selection because the integrated emission from the radio lobes is optically thin and essentially isotropic. This makes radio-loud AGN particularly attractive for studying orientation-dependent properties at other wavelengths and, after sorting out the influence of radio jets/lobes on the emission, for generalizing conclusions about orientation-dependent effects

21 CHAPTER 2. NEAR- AND MID INFRARED PHOTOMETRY 21 to the much larger population of radio-quiet AGN. The brightest low-frequency-selected AGN sample is the 3CR compilation Spinrad et al. (1985). The powerful double-lobed radio galaxies (henceforth simply called radio galaxies) are supposed to be misaligned quasars Barthel (1989). Based on IRAS co-added scans and a few individual detections, Heckman et al. (1992, 1994) already noted an average MIR/FIR difference between 3CR quasars and radio galaxies. More comprehensive MIR and FIR spectrophotometry from ISO is in hand (as compiled by Siebenmorgen et al. (2004) and by Haas et al. (2004)) as well as from Spitzer (e.g., Shi et al. (2005); Haas et al. (2005); Ogle et al. (2006); Cleary et al. (2007), providing a basis to study the z < 1 3CR objects. These sources are, however, a factor of five less radio-luminous on average than the most powerful radio sources seen at higher redshift, and the lower indicated accretion power may reflect different source physics. The higher-luminosity population can be sampled by the 3CR sources at 1 < z < 2.5, which have radio luminosities similar to those of the most powerful radio sources at even higher redshift (2.5 < z < 6). However, with the exception of a few targets (Siebenmorgen et al. 2004; Seymour et al. 2007), the high-z 3CR sample has not been well observed in the rest frame NIR and MIR. The observations in this thesis are based on Spitzer observations of 62 of the 64 high-z 3CR sources. It focuses on the hot nuclear dust emission and its obscuration in the most luminous type-2 AGN. We use a ΛCDM cosmology with H 0 = 71 km/s/mpc, Ω m = 0.27 and Ω Λ = Observations and Data With the Spitzer Space Telescope (Werner et al. 2004), we have obtained the entire sample of 64 high-z 3CR sources using the instruments IRAC ( µm, Fazio et al. 2004), the IRS blue peak-up array (16µm, Houck et al. 2004) and MIPS (24 µm, Rieke et al. 2004). Most observations are performed in our guaranteed time program (PID 40072; PI G. Fazio) with on-source exposure times 4 30 s (each IRAC band), 4 14 s (IRS), and s (MIPS). A few sources have been observed in other programs, and we use the published photometry if available (e.g., PID 3329; PI D. Stern, Seymour et al. 2007). For IRAC, we used the basic calibrated data products (BCD, version S16) and co-added them to pixels using the latest version of IRACProc (Schuster et al. 2006). This optimally handles the slightly under-sampled IRAC PSF in order to assure accurate point-source photometry. For IRS, we used the post-

22 OBSERVATIONS AND DATA ν L ν ( rest 8µm, erg/s ) CR at z > ν L ν ( rest 178 MHz, erg/s ) quasars galaxies Si absorption Figure 2.1: Infrared versus radio luminosity of the 3CR sample at z > 1 prior to normalization. x symbols denote quasars; circles and squares denote radio galaxies. Superposed crosses indicate radio galaxies that show evidence of silicate absorption ( 2.3.1). The vertical long-dashed lines mark the range of our luminosity-matched quasar and radio galaxy subsamples. The dotted lines indicate L 8µm /L 178 MHz ratios of 1, 10, and 100. The radio galaxies are grouped into several SED classes in Fig. 2.3 and The color-coding and symbols are: green circle (A), red circle (B), red square (C), blue square (D), blue circle (E). The two low-excitation radio galaxies 3C 68.1 and 3C are labeled with their 3C numbers, as are sources outside the luminosity range of our analysis. BCD pipeline product, version S16. For MIPS, we used custom routines to modify the version S17 BCD files to remove instrumental artifacts (e.g., residual images) before shifting and co-adding to create the final mosaics. All sources are well seen on the images in all filters. The sources were extracted and matched using the SExtractor tool Bertin & Arnouts (1996). We used sufficiently large apertures so that aperture corrections are small (<5%). The photometric errors are typically smaller than 10% but increase for faint sources; exceptions are 3C 225A and 3C 294, where nearby bright stars make the photometry uncertain in the shorter IRAC bands. As of 2008 April, 24 quasars and 38 radio galaxies have been observed, covering

23 CHAPTER 2. NEAR- AND MID INFRARED PHOTOMETRY 23 the complete high-z 3CR sample with the exception of the quasar 3C 245 and the radio galaxy 3C 325. All 62 sources have IRAC measurements and are observed in at least one of the 16 and 24µm bands (54 sources at 16µm and 60 sources at 24µm). For the analysis it is desirable to compare rest frame SEDs with the same wavelength sampling. Depending on the redshift of our sources (1 < z < 2.5) the observations sample different rest wavelengths between 1.6 and 10 µm. Before resampling and interpolating the SEDs, we checked that spectral features do not affect the interpolation. In principle, prominent spectral features in this wavelength range could be PAH emission bands around 7.7µm and the 9.7µm silicate absorption feature. IRS spectra of several low-z and even a few high-z FR II radio sources are available. PAH features are weak and usually undetected, and the continua are generally smooth. Strong silicate absorption is, however, present in some objects (Haas et al. 2005; Ogle et al. 2006; Cleary et al. 2007; Seymour et al. 2007). Our broadband SEDs therefore represent the smooth continua for λ 8 µm but are uncertain at rest wavelengths near 10 µm. In practice, the de-redshifted SEDs were interpolated in log-log space at 12 wavelengths between rest 1.6 and 10µm to produce the figures shown. The quasar and radio galaxy samples match reasonably well in redshift and rest-frame 178 MHz flux density. Rest-frame 178 MHz radio flux densities were derived from data listed in the NASA Extragalactic Database, NED. The quasars have mean redshift z = 1.44±0.31 and mean flux density S 178 = 27.8±15.1 Jy, while the values for the radio galaxies are z = 1.42±0.31, S 178 = 22.2±6.2 Jy. Thus over the whole sample, quasars are about 30% more luminous than radio galaxies as shown in Figure 2.1. In order to improve the luminosity match, we have excluded the sources at the low and high ends of the distribution, L 178 < erg/s and L 178 > erg/s, respectively. We have also excluded the quasar 3C 418 because of its flat radio spectrum (low-frequency spectral index α 178 0), while all other sources have steep radio spectra ( 1.1 α ). The resulting mean radio luminosities of the sample galaxies are L 178 = (5.35 ± 2.53) erg/s for quasars and (5.55 ± 2.34) for radio galaxies. While the quasar and radio galaxy distributions match very well in L 178, a proper analysis of orientation-dependent effects requires also that the individual SEDs are normalized by the radio luminosity, which serves as a tracer for the intrinsic AGN strength. Therefore, we have normalized each SED to the sample mean 178 MHz luminosity; after normalization each object has L 178 = erg/s. Because of the good L 178 match of the samples, it turned out that

24 RESULTS AND DISCUSSION the net effect of this normalization on the results is small. 2.3 Results and Discussion Radio galaxies as obscured quasars The NIR MIR SEDs of quasars are all very similar in shape, as shown in Figure 2.2. The SEDs can be described by a single power law L ν ν 1, consistent with previous results for lower-redshift objects (e.g., Elvis et al. (1994)). The dispersion of the SEDs is essentially caused by differing ratios of MIR to radio luminosity. Some quasars exhibit small (10-20%) bumps around 5 µm explainable by distinct hot dust components. 1 The power law shape of the quasar SED can naturally be explained by the superposition of centrally-heated dust components with a radial temperature gradient (1500 K > T > 300 K) as has been found also in lower luminosity type-1 AGN (Ward et al. 1987; Barvainis 1987; Rowan- Robinson 1980). Any contribution of the quasar host galaxies to the NIR MIR SEDs appears to be outshone (factor 5 10) by the AGN dust emission. In contrast to quasars, radio galaxies display a diversity of SED shapes leading to a 50% larger dispersion around their mean SED (Fig. 2.2). Despite the dispersion, nearly all radio galaxy SEDs show a decline from rest 1.6µm to 3µm and a rise from 3µm to 8µm (L ν ν 1.9 ). In addition, the average radio galaxy SED is fainter by a factor of three at 8µm and a factor of eight at 2µm relative to the quasar SED. Unlike the quasars, hot (T > 750 K) dust emission is not seen in the radio galaxy SEDs. Its absence can be explained by absorption (screen A V 50) 2 of the central dust emission. The short wavelength (λ < 3 µm) component can then be explained by emission from stars in the host galaxy. Extrapolation of the mean 3 8 µm SED slopes towards longer wavelengths suggests that the radio galaxy and quasar SEDs meet each other at about 25 40µm, and beyond these wavelengths extinction may be no longer relevant. As noted above, the quasar NIR MIR SED shapes are extremely homogeneous. This is reflected in the narrow range of the quasars NIR and MIR colors. 1 Despite the similarity of the infrared SEDs, the quasar population is not homogeneous at optical wavelengths: there are quasars like 3C 186 with blue optical SED and 3C 68.1 with red optical SED, as listed in NED. In the orientation-based unified scheme, 3C 68.1 could be borderline so that the broad lines are detected, but most of the UV-optical continuum is absorbed. 2 The reddening curve used is a compromise between the latest results for Milky Way reddening and earlier data (summarized by Indebetouw et al. (2005)): A V / A H / A 3µm / A 5µm / A 8µm / A 10µm = 1 / / / / /

25 CHAPTER 2. NEAR- AND MID INFRARED PHOTOMETRY 25 The color-color diagram shown in Figure 2.3 illustrates the differing SED types. In this diagram, quasars populate a distinct locus ( E ), while radio galaxies show wider dispersion as mentioned above. According to their location in the color-color diagram, we have grouped the radio galaxies into five classes described below. Their SED shapes are illustrated in Figure 2.4. A) Four sources at the high end of the 3µm/1.6µm ratio (above the dotted and dashed boxes in Fig. 2.3): basically, they have quasar-like SEDs, but the hottest dust emission at about 1 2µm appears to be absorbed (screen A V 5) leading to a redder 3µm/1.6µm color compared to quasars. B) The bulk of radio galaxies (20 sources) have declining 1 3µm SEDs with a steep 3 8µm rise. Their colors can be explained by a heavily reddened AGN plus an added component of starlight contributing at 1.6µm. If this explanation is correct, the direction of the extinction vector A V becomes meaningless because host galaxy starlight will not be affected by extinction near the nucleus. Instead, vertical position in the plot is determined by the relative contributions of starlight and AGN light, while horizontal position measures the amount of extinction (to the extent the underlying AGN SEDs are the same). As noted above, A V 50 mag is required to explain the colors. C) Three sources at the low end of the 3µm/1.6µm ratio (below the long dashed line in Fig. 2.3): Their SEDs show a very strong host galaxy contribution at 1.6 µm, and starlight exceeds the dust luminosity even at wavelengths as long as 3.5µm. In principle, class C is similar to class B but with stronger host galaxy contribution. D) Three sources immediately below the dotted box in Fig. 2.3: Their SEDs can be explained by a slightly reddened AGN (similar to class A) plus an added component of starlight contributing significantly at 1.6 3µm. E) Three sources with quasar-like SED colors (inside the dotted box in Fig. 2.3): Their SEDs overlap with the low luminosity end of the quasar SEDs. In the orientation-based unified scheme, these sources could be borderline so that most of the dust torus is visible but the broad line region and the UV-optical continuum are obscured. While the rest 8 10 µm range is poorly sampled, eight galaxies show declines in this range that could be caused by silicate absorption. (The MIPS-24 filter,

26 RESULTS AND DISCUSSION 50% transmission at µm, requires z 1.8 for the silicate feature to fall into its range.) One of these sources (3C 469.1, z = 1.336) has an IRS spectrum available. It shows a broad silicate absorption with optical depth τ corresponding to A V 10, consistent with its position in Fig This supports the view that the SED declines in the other radio galaxies are due to silicate absorption, too. The photometric silicate absorption sources show a wide range of colors (Fig. 2.3), but only one galaxy (3C 469.1) is on the blue (right) side, where low-extinction sources reside. The important conclusion is that the silicate feature requires considerable extinction to be present in at least some of the radio galaxies, and this is largely independent of the SED class. 3 If radio galaxies are misaligned quasars, as proposed in the unified scheme, reddening of individual galaxies should be correlated with their extinction. Figure 2.5 shows that this is indeed the case. Quasars populate a distinct region of this diagram characterized by high MIR/radio and blue NIR MIR colors. Most radio galaxies spread towards fainter MIR/radio and redder NIR/MIR. Under the reasonable assumption that the emission at 5 8µm is not affected by the host galaxy, de-reddening along the direction of the extinction vector can place each radio galaxy inside the region populated by quasars. Thus individual radio galaxies can be explained as reddened quasars, consistent with the orientationdependent unified scheme. The typical amount of radio galaxy reddening, A V 50 for an obscuring screen (Fig. 2.5), corresponds to a hydrogen column density N H cm 2 (for A V = mag/cm 2 Seward (1999). This is close to but below the Compton-thick limit (N H = cm 2 ). Screen extinction is a simplification, and one may expect a more complex geometry. If emitting dust particles are spatially mixed with the absorbing ones, the amount of dust has to be higher for the same observed reddening, typically by a factor Thus there could very 3 The photometric silicate absorption sources are 3C 68.2, 3C 222, 3C 249, 3C 250, 3C 266, 3C 305.1, 3C 324, and 3C These galaxies lie in the redshift range 1.08 < z < 1.83, suggesting that in this range the broad band 16µm/24 µm filter combination is able to register silicate absorption, if strong enough. For comparison, this redshift range contains 20 more radio galaxies with 16 and 24 µm photometry available but without silicate absorption signatures in their broadband SEDs. Four of these sources (3C 13, 3C 266, 3C 267, 3C 356) have IRS spectra available, but significant silicate absorption is detected only in one of them (3C 267, z = 1.14, τ ). At low redshift, the detection of silicate absorption appears not to be directly correlated with other absorption signatures, perhaps because of complex geometry and/or varying silicate dust abundance (Haas et al. 2005; Ogle et al. 2006; Cleary et al. 2007). A detailed analysis of the high-z 3CR spectra and the photometric detectability of silicate features will be presented elsewhere (Leipski et al. submitted.). 4 The transmission factors are exp( τ λ ) and (1 exp( τ λ ))/τ λ for the screen and the mixed case, respectively, with τ λ = A λ (Disney et al. 1989).

27 CHAPTER 2. NEAR- AND MID INFRARED PHOTOMETRY 27 well be enough gas present to render the AGN Compton-thick. There is, unfortunately, no independent measurement of reddening for individual galaxies, nor is it certain that a Galactic reddening curve applies to AGN. Thus it is still an open question whether after de-reddening there will remain a difference in the 8 µm/178 MHz ratio between radio galaxies and quasars. If such a difference remains, with quasars having a higher 8 µm/178 MHz ratio than radio galaxies, then either our screen extinction premise is too simple or the MIR luminosity of quasars is enhanced by an additional potentially non-thermal contribution. Our Chandra X-ray observations and IRS spectra of the Si feature (Leipski et al. submitted) of a subset of the sample will provide independent estimates of the extinction towards the nuclei (Wilkes et al., in prep.). To summarize, while quasars exhibit a uniform SED shape which can be explained by a centrally heated dust distribution, radio galaxies show a diversity of SED shapes. In all cases, however, the radio galaxy SEDs are consistent with being intrinsically a quasar modified by absorption of the dust emission and addition of some amount of host galaxy starlight Evolutionary effects and non-thermal contributions Studying powerful 3CR sources at z < 1, Ogle et al. (2006) found evidence for a population of accretion-inefficient radio galaxies, in which the jet/lobe may be powered by extraction of rotational black hole energy. These sources, mainly optically-classified low-excitation radio galaxies (LERGs), have a 15 µm luminosity below erg/s and a luminosity ratio L 15µm / L 178MHz < 10. In contrast, with the reasonable assumption that L 8µm L 15µm, all our z > 1 sources have observed MIR luminosity L 8µm > erg/s, which is expected to be even higher after de-reddening. Also, the two LERGs (3C 68.2, 3C 469.1) in our sample show a high luminosity ratio L 8µm / L 178MHz > 10 comparable to that of quasars (Fig. 2.1). From this, our data do not support the existence of an accretioninefficient population among the powerful 3CR sources at z > 1. A possible explanation for the deficit of optical high-excitation line luminosity (for instance [O III] λ5007å) in our two LERGs may be extinction of the narrow-line region. On the other hand, some of our radio galaxies with very strong host contribution (plotted as squares in Fig. 2.5) are expected after de-reddening to lie at the low end of the L 8µm /L 178MHz distribution. Hence compared with the strength of both the host and the radio lobes, these galaxies are relatively weak in the MIR and may represent a population at the beginning of a different evolutionary state.

28 CONCLUSIONS Some authors have attributed the excess emission of quasars compared to radio galaxies to non-thermal emission from synchrotron jets. For example, Cleary et al. (2007) fitted the SEDs and spectra of 3CR sources at 0.5 < z < 1 with a combination of a spherically symmetric dust model and a jet+lobe synchrotron component. They attributed half of the factor of four excess in the 15µm luminosities of steep-spectrum quasars relative to radio galaxies to a non-thermal component. If such a non-thermal component were present in our 3CR sources at z > 1, it would show up in Fig. 2.5 as an offset by about a factor of two between de-reddened radio galaxies and quasars. This conclusion is, however, dependent on both the reddening law and on the radiative transfer and thus the geometry of the emitting region. In order to draw definite conclusions about any MIR luminosity excess, detailed radiative transfer modeling is required (see chapter 4). Spherically symmetric models are wholly inadequate for this purpose. In an inclined disk-like system, some fraction of the MIR emission is likely to have very little obscuration while the bulk of the MIR emission is heavily obscured, and no spherical model can properly account for this geometry. All we can say at the moment is that our data appear consistent with a simple thermal interpretation and show no evidence for a non-thermal component. 2.4 Conclusions The 3CR sample at 1 < z < 2.5 represents the most luminous steep-spectrum quasars (type 1 AGN) and powerful double-lobed radio galaxies (type 2 AGN). This sample is nearly unbiased by orientation. We have defined subsamples of 19 quasars and 33 radio galaxies matched in isotropic rest 178 MHz luminosity and have obtained Spitzer µm photometry. The main results are: 1) Quasars all have similar energy distributions in the rest frame µm range, and their ratio of MIR to radio luminosity is also nearly constant. This is consistent with results seen previously in lower-redshift samples. 2) The rest frame µm SEDs of radio galaxies can be explained as reddened quasars, consistent with orientation-dependent unification. Various amounts of extinction of the AGN emission combined with addition of host galaxy starlight can explain the diversity of radio galaxy SEDs. 3) If the extinction is sufficiently large, there is no need to invoke a beamed synchrotron contribution to explain the MIR luminosity difference between

29 CHAPTER 2. NEAR- AND MID INFRARED PHOTOMETRY 29 quasars and radio galaxies. The actual amount of extinction has to be derived from additional observations. 4) The above results hold also for splitting our sample in redshift and luminosity; within our sample we do not find any trends with redshift or luminosity. 5) At rest frame 8µm, quasars are 3 times more luminous than radio galaxies. If this difference applies also to high-redshift, radio-quiet AGN, then MIR (24µm) surveys are strongly biased in favor of type-1 and against type-2 AGN. This will make it very difficult to resolve the AGN/starburst degeneracy with only broadband SEDs, and spectral line diagnosis will be required. While our near-mid-ir SEDs provide a fundamental set of high luminosity AGN templates, we expect to derive more stringent conclusions from proper twodimensional radiative transfer modeling in combination with Spitzer MIR spectra, Chandra X-ray observations, and Herschel far-ir/sub-mm data.

30 CONCLUSIONS Mean SEDs ν L ν [ erg/s ] host galaxy quasar radio galaxy quasar reddened by A V = λ rest [ µm ] Figure 2.2: Rest frame quasar and radio galaxy SEDs normalized by rest 178 MHz luminosity ( L 178 = erg/s). Symbols connected with thick blue and red lines show the mean SEDs for quasars and radio galaxies, respectively. The thin dotted blue and red lines indicate the dispersion (upper and lower quartiles) around the mean SEDs; the mean ratio of upper/lower quartiles are 2.3 (quasars) and 3.4 (radio galaxies). The radio galaxy SED can be explained by the sum (black long-dashed line) of a reddened quasar (blue long-dashed line) and starlight from the host galaxy (thin black solid line). The long-dashed lines have been shifted slightly to make them visible in the plot. The difference between radio galaxies and reddened quasars at 10µm may be due to the silicate absorption feature which may escape detection in our broad band photometry.

31 CHAPTER 2. NEAR- AND MID INFRARED PHOTOMETRY 31 F 3 µm / F 1.6 µm (rest) 4 1 B C F 5 µm / F 8 µm (rest) A 68.2 V AV = 5 D E quasars galaxies Si absorption Figure 2.3: NIR/MIR color-color diagram. The radio galaxies are grouped into five classes labeled A E as explained in Symbol color-coding is the same as in Figure 2.1. Radio galaxies with a photometric signature for silicate absorption are additionally marked with an underlying plus. The two sources 3C 267 and 3C with spectroscopically-detected silicate absorption are labeled, as are the two low-excitation radio galaxies 3C 68.1 and 3C The error bar in the upper left corner represents a color rms of 15%. The A V arrow indicates screen extinction with the reddening law given in

32 CONCLUSIONS Diversity of SEDs ν L ν [ erg/s ] quasar E D host galaxy A B Si absorption C 1 10 λ rest [ µm ] Figure 2.4: Mean SED of each radio galaxy class as identified in The SEDs have been normalized to the mean 178 MHz luminosity. The mean quasar SED is also shown for comparison. The dispersion around each SED (measured as mean ratio of upper/lower quartile) is: 2.3 (quasars), 2.0 (A), 3.2 (B), 2.4 (C), 1.7 (D), 1.5 (E), and 4.8 (silicate absorption).

33 CHAPTER 2. NEAR- AND MID INFRARED PHOTOMETRY 33 F 8 µm / F 178 MHz (rest) V A V = quasars galaxies Si absorption F 5 µm / F 8 µm (rest) Figure 2.5: Infrared-radio color-color diagram. The dotted line marks the region occupied by quasars. The color-coding and symbols of radio galaxies correspond to those in Figures 2.1 and 2.3. The two sources 3C 267 and 3C with spectroscopically-detected silicate absorption are labeled as well as the two lowexcitation radio galaxies 3C 68.1 and 3C The error bar in the upper left corner represents an rms of 15%. The A V arrow indicates screen extinction with the reddening law given in

34 CONCLUSIONS

35 Chapter 3 The cluster search This part represents one of the developed papers during my PhD thesis. My contribution in this large collaboration of many coauthors was mainly: Developing a pipeline to extract the photometry of all objects in the Spitzer mosaic images. Develop an interactive data tool to provide photometric redshifts for all objects in the center and control fields (Fig 3.1). Interactive inspection, verification and improvements of the SEDs of the cluster candiates by eye. Technical data analysis (cluster properties, plots). Abstract: Observations of the z = 1.53 quasar 3C with the Spitzer Space Telescope at µm and with the 6.5-m MMT in the z - and Y -bands allow detection of potential cluster members via photometric redshifts. Compared with nearby control fields, there is an excess of 11 extremely red objects (EROs) at 1.33 z phot 1.73, consistent with a proto-cluster around the quasar. The spectral energy distributions (SEDs) of 3/4 of the EROs are better fitted with passive elliptical galaxies than with dust-reddened starbursts, and of four sources welldetected on an archival HST snapshot image, all have undisturbed morphologies. 3.1 Motivation Galaxy groups and clusters are important for the understanding of large scale structures and their evolution. As a few thousands of clusters are known in the 35

36 MOTIVATION Figure 3.1: Outcome of the photometric fitting routine. The left column shows the two dimensinal fitting space of redshift and luminsosity. This helps to check the quality of the fitting procedure. The right column shows the logarithmic plot of the data, with the wavelenth on the x-axis and νl ν on the y-axis. The top left small box contains information like the redshift, χ 2, position and distance to the 3C source. The small pictures on the left shows a small region around the object to check for double sources or other peculiarities.

37 CHAPTER 3. THE CLUSTER SEARCH 37 local universe up to redshift one, the main problems are that the flux which reaches the earth is dramatically weaker for distant clusters at redshift higher than one. The other fact is that it is challenging to discriminate the distant cluster members from the foreground galaxies. The most distant clusters till now have been found via X-Ray observations (Mullis et al. 2005; Bremer et al. 2006; Stanford et al. 2006; Fang et al. 2007). The second approach to find clusters at high redshift is to search for over densities in wide area optical-infrared surveys. A few of these surveys are the Spitzer-IRAC shallow survey, the UKIDSS ultra deep survey, the HIRCOCS/COSMOS, and the Spitzer First look Survey. A different way is to look around high redshift radio sources. For the local universe it is known, that these objects exist in massive environments. Therefore they should trace over density regions, in other words galaxy groups or clusters. The advantage of this method is that the redshift is known and this limits the observational effort. For very high redshifted radio sources (2 < z < 5) this method has revealed an excess of Lyman break galaxies (Kurk et al. 2000). These Lyman galaxies show no real concentration, which is consistent with a cluster that is forming but not yet virialised (Intema et al. 2006). There is evidence that the high redshift radio sources have an over density of EROs (Best (2000)). But it is not known if these objects are passive ellipticals or dust reddened star forming galaxies. So the question, if these giant ellipticals are already formed or still in evolution, is not answered till now. If these ellipticals already exist, the formation must be at larger redshifts. On the other hand if these EROs are star-forming Galaxies we probe the epoch in which they form. With our large Spitzer maps (> 5 ) we should be able to detect the whole cluster with sizes of about 1-2 at that redshift range and our MIPS images should in principle identify the dusty star-forming galaxies. 3.2 Clustering around 3C Observations and data We analyse Spitzer maps of 3C taken with the Infrared Array Camera (IRAC) and Multiband Imaging Photometer (MIPS) on the Spitzer space telescope. The exposure times on the sources were 4 30s for each IRAC band, and 10 10s for the MIPS bands. The size of the maps is about 4 4 for the IRAC camera. In addition we also obtain a comparison field northeast of the quasar for the 3.6µm and 5.8µm band. For the 4.5µm and 8.0 µm we get the data for a field

38 CLUSTERING AROUND 3C Southwest of the quasar. The center of the comparison fields are shifted of about 6 from the quasar. The 3σ detection limit of the maps is 4µJy for the first two IRAC bands (3.6µm,4.5µm),10 µjy for the last two IRAC bands (5.8µm,8.0µm) and 100µJy for the 24µm MIPS band. We also obtain z and Y-band images at the 6.8m MMT using MegaCam (30 FOV). These filters encompasses the 4000 A break at the redshift of the quasar. The sensitivity for the both MMT filter is better than 1 µjy with exposure times of 40 minutes for the z filter and 90 minutes for the Y-Band. The reduction of the images was achieved with interactive analysis tools. IRAC mosaics were corrected for residual images from previous observations by making object masked, median-stacked co-adds with all the science frames and the subtract them from the science frames before final co-add Results Cluster galaxy candidates Cluster galaxies around 3C should lie at the redshift of the quasar. We determined photometric redshifts z phot by fitting the observed spectral energy distributions (SEDs) of the 184 galaxies detected at z with two basic templates, an elliptical galaxy and a dusty starburst galaxy. 1 For the elliptical galaxy, we used NGC 221, which has a strong 4000Å break. The NGC 221 spectral template from Kinney et al. (1996), covering the rest-frame wavelength range Å, was smoothly extrapolated to longer wavelengths by a 4000 K black-body. For the starburst galaxy template, we used the ultra-luminous infrared galaxy Arp 220 with photometry from the NASA Extragalactic Database (NED) and the Sloan Digital Sky Survey (SDSS, DR6) and mid-ir spectra from Spitzer/IRS. We also tested the dust-reddened star-forming galaxy template M 82, but the results were similar to those for Arp 220 (differences in z phot less than z = 0.1), and therefore we present only the Arp 220 results. The accuracy of photometric redshifts can be estimated, for instance, from the Spitzer Wide-Area Infrared Extragalactic Survey (SWIRE), which has spectroscopic redshifts available for many of its sources. In SWIRE, 7 filters (5 optical and 2 IRAC) were sufficient to discriminate between 8 templates (ranging from blue to red galaxy types) and to determine z phot with an rms of z/(1+z) = 3.5% (Rowan-Robinson et al. 2008). We have fewer filters than SWIRE, but our task 1 We also tried type-1/-2 AGN templates, but none of the SEDs is consistent with such templates at redshift z 1.5 except 3C itself.

39 CHAPTER 3. THE CLUSTER SEARCH 39 is easier because we only have to determine whether the photometry is consistent with z = 1.53 or not. Furthermore, we consider only extremely red sources and only two templates and therefore suggest that we can reach accuracy similar to that of SWIRE. At z = 1.5, an accuracy (rms) of about 4% corresponds to z = 0.1. This is much larger than the expected redshift dispersion within a cluster ( z < 0.01). While the SEDs can provide cluster galaxy candidates within appropriate redshift bins around z QSO, confirmation of a cluster will require spectroscopic redshifts of at least a sample of the candidates. (We have been granted NIR multiobject spectroscopy with MMIRS at the MMT to look for redshifted H α emission, but observation have still to be carried out) Figures 3.2 and 3.3 show examples of SEDs and template fits to cluster galaxy candidates. The most striking SED features are the steep rise from z - to Y -band and beyond and the decline between 3.6 and 8.0µm. The slopes of these features determine the redshift. We performed the template fits over a grid in redshift (dz = 0.01) and intensity. For a red SED with F(3.6)/F(z ) 30, the chi square contour plots exhibit a sharp minimum, suggesting that for a given template the achievable accuracy of z phot is about z = 0.1. As illustrated in Fig. 3.3, even for SEDs with fewer than six data points the accuracy of z phot should be not worse than z < Therefore, we have chosen as cluster galaxy candidates the 29 objects for which either the elliptical or the starburst template yields 1.33 < z phot < The basic conclusions on the clustering of EROs around 3C270.1 remain unchanged for other ranges z between 0.15 and Usually the µm SEDs can be fitted very well with both the elliptical and the starburst template. Both templates are extremely red, and therefore no other galaxy type is likely to fit the SEDs. The examples in Figures 3.2 and 3.3 illustrate the ability to identify the two galaxy types at z The resulting fit parameters for the 29 cluster candidates are listed in Table 3.1. The photometric redshifts differ systematically for the elliptical and starburst templates. On average, z ell is higher by 0.27 ± 0.1 than z SB. Among the 29 cluster candidates, the elliptical template yields better fits (smaller chi square values) for 22 sources (75%, Table 3.1). All of these sources have lie in the redshift bin 1.33 < z SB < Among the remaining 7 cluster candidates, those 3 sources which favor the starburst fit lie in the redshift bin 1.33 < z SB < 1.73, too. These sources have χ 2 SB < 0.3 χ2 ell (object numbers 3, 5 and 29 in Table 3.1). The remaining 4 sources (object numbers 11, 14, 17 and 28) are not detected at 5.8 and 8.0 µm, and they do not clearly favor the starburst template, so that as ellipticals they are cluster galaxy candidates.

40 CLUSTERING AROUND 3C elliptical ν F ν [ W / m 2 ] dusty starburst ν F ν [ W / m 2 ] Wavelength [ µm ] Figure 3.2: Observed spectral energy distributions for two good examples of the 29 cluster candidates. The IRAC and MMT data are marked with filled circles and 1σ error bars. The MIPS 24µm data point is marked with a filled circle, too; in the upper panel (elliptical source) it is a 3σ upper limit, in the lower panel (dusty starburst source) it is a 2σ detection also visible on the map. The horizontal bar indicates the 24 µm pass band for comparison with the silicate absorption feature. HST photometry is marked with an open circle; it is not available for the dusty starburst source. Solid lines show the elliptical galaxy (NGC 221) fit for the source in the upper panel and the dusty starburst (Arp 220) fit for the galaxy in the lower panel. Dotted lines show the alternative template for each galaxy. The upper and lower panel shows object numbers 23 and 3, respectively, as listed in Table 3.1).

41 CHAPTER 3. THE CLUSTER SEARCH 41 ν F ν [ W / m 2 ] z = 1.73 χ 2 = 0.16 z = 1.53 χ 2 = 0.07 z = 1.33 χ 2 = 0.16 ν F ν [ W / m 2 ] z = 1.73 χ 2 = 0.28 z = 1.53 χ 2 = 0.04 z = 1.33 χ 2 = Wavelength [ µm ] Figure 3.3: Observed spectral energy distributions for two cluster candidates with poorer data quality. The IRAC and MMT data are marked with filled circles and 1σ error bars. The sources are detected only in the z -band (in the lower panel also in the Y -band) and at 3.6 and 4.5 µm, but the upper limits at 5.8 and 8.0 µm help to constrain the redshift fits. As in Fig. 3.2, the upper panel shows a source (object 11) which is preferably fit by the elliptical template and the lower panel one (29) fit by the starburst template. The dotted and dashed lines show the preferred templates at different redshifts (indicated in the figure), showing that the accuracy of the photometric redshifts should be dz 0.2.

42 CLUSTERING AROUND 3C Quasar field F 3.6 / F z color cand SED cand. other 10 2 Control field 1 F 3.6 / F z color cand SED cand. other F 3.6 [ µjy ] Figure 3.4: Color-magnitude diagram F(3.6)/F(z ) versus F(3.6), for the quasar field (upper panel) and control field 1 (lower panel). Sources with z phot = 1.53 ± 0.20 (SED determined candidates marked with circles) concentrate in a distinct color range 15 < F(3.6)/F(z ) < 52 indicated by the horizontal dotted lines. The number of color determined candidates is also given.

43 CHAPTER 3. THE CLUSTER SEARCH 43 The cluster galaxy candidates occupy a limited range of z [3.6] color as shown in Figure 3.4. While the candidates were determined from multi-band SED fitting (usually 4 6 bands), selecting a color range of 15 < F(3.6)/F(z ) < 52 would have given nearly the same sample. The sources redder than this color range are probably at larger redshift (z > 1.8), while bluer sources are consistent with being either foreground objects or unreddened star-forming galaxies at the quasar redshift. Our EROs with F(3.6)/F(z ) > 15 (corresponding to z [3.6] > 5 mag in the Vega system) are likely to also obey the standard ERO definition of R K > 5 mag (Wilson et al. (2004)). Sky distribution and surface density Figure 3.5 shows the sky distribution of the cluster candidates. Spreading over a diameter of more than 2, they form at most a loose concentration around the quasar, indicating a proto-cluster rather than a virialised, concentrated system. The peak density appears to lie 30 to the east of 3C 270.1, but it is difficult to be precise with so few galaxies. The 3C proto-cluster is larger in angular size than the X-ray clusters at z = 1.45 and z = 1.22 found by Mullis et al. (2005) and Bremer et al. (2006). These clusters have an extent of less than 1 in both their galaxy distributions and their X-ray sizes. We have also analyzed the two comparison fields taken with IRAC and covered by the MegaCam z image (but not the SWIRC Y image). Details of the analysis are described in Appendix The surface density of possible z = 1.53 ± 0.20 galaxies is less than about 18 objects in each control field of area 15.2 arcmin 2 (1.2 per arcmin 2 ). Thus, in the redshift range z = 1.53 ± 0.20, the central field surrounding the quasar shows an excess of at least about = 11 sources, i.e., 60% over the comparison fields. Figure 3.6 shows the radial surface density plot of the 29 EROs in the redshift range z = 1.53 ± The surface density peaks inside the central 50 radius and declines steadily with increasing distance down to the surface density of the control fields. This provides further evidence that there is an excess of z phot 1.53 galaxies near 3C The radial overdensity is also present when using circles around the centroid east of the quasar position. Existing X-ray data on 3C are inconclusive about the presence of a cluster. The quasar itself is clearly detected as a point source in our 10 ks Chandra data (Wilkes et al., in preparation). Weak extended X-ray emission (<20 ) is also present, but most of the counts come from the position of the southern radio lobe

44 CLUSTERING AROUND 3C DEC J o 41 30" 33 o 43 30" 33 o 45 30" IRAC HST 12 h 20 m 35 s 12 h 20 m 45 s RA J h 20 m 25 s Figure 3.5: Sky distribution of the 29 candidate cluster galaxies around the quasar 3C (marked with a cross). The triangle shows the apparent centroid of the cluster galaxy distribution. The star in the southwest marks the starburst candidate whose SED is shown in Fig The solid lines surround the areas covered by IRAC and HST frames. The dotted circles of radius 50, 100, and 150 outline the areas considered in Fig. 3.6, they are centered around the quasar. At z = 1.53, 50 corresponds to 427 co-moving kpc.

45 CHAPTER 3. THE CLUSTER SEARCH 45 5 Number / arcmin C270.1 field 1 control fields Distance [arcsec] Figure 3.6: Surface density of the 29 cluster galaxy candidates versus projected distance from the quasar 3C270.1 (solid line through fat dots with Poisson error bars). The radial bins centered around the quasar are outlined in Fig. 3.5, and the surface density of the outermost annulus has been corrected for the area not covered by IRAC. For comparison, the long-dashed histogram shows the surface density of cluster galaxy candidates versus projected distance from centroid (the triangle in Fig. 3.5). The dotted line indicates the mean surface density in the two control fields.

46 CLUSTERING AROUND 3C Table 3.1: Fit parameters for the 29 cluster galaxy candidates in the quasar field. Object RA J2000 Dec J2000 n [1] z [2] ell χ 2 ell z [3] SB χ 2 SB [1] Number of data points (detections) used to calculate χ 2 of the fit. [2] Redshift of the fit using the elliptical template NGC 221 [3] Redshift of the fit using the starburst template Arp 220 or from between the northern radio lobe and the quasar. ROSAT data (19.3 ks, 1993 May) show a strong detection of 3C 270.1, but at ROSAT resolution not only is there no way to separate cluster gas emission from quasar emission, but the emission is also heavily blended with that of unrelated QSO B B located 0. 8 away. 2 We found no XMM observations. Longer Chandra exposures 2 Because of its lower redshift, z = 1.038, this QSO does not affect the clustering evidence

47 CHAPTER 3. THE CLUSTER SEARCH 47 will be needed for a definitive detection of any X-ray emission from cluster gas. Comparison with the control fields The quasar field has photometry in six filters, two MMT and four IRAC bands. But the two control fields have photometry in only three filters, in z and two IRAC bands: 3.6 and 5.8 µm in control field 1, 4.5 and 8.0 µm in control field 2. This makes the uncertainties on the photometric redshifts larger, and therefore comparison within the same redshift bin appears not to be straightforward. Furthermore, in the central field, the source detection rate at 4.5 µm is lower than at 3.6 µm, so that for a fair comparison the number of candidates in control field 2 has to be corrected for. In order to facilitate the comparison, we used the colors z [3.6] and z [4.5] and counted the number of candidates in the respective color bins. Control field 1: The colors of the elliptical and starburst template, when redshifted to z = 1.53, are F(3.6)/F(z ) = 25 and 39, respectively. In the color range 15 < F(3.6)/F(z ) < 52 we found 28 and 17 sources for the quasar and the control field, respectively (Fig. 3.4). With regard to the low number statistics, these values are consistent with the 29 and 16 sources in the quasar and the control field, having 1.33 < z phot < 1.73 (denoted as SED candidates in Fig. 3.4). This leaves an excess of 11 colour and 13 SED selected sources in the quasar field. Control field 2: The colors of the elliptical and starburst template, redshifted to z = 1.53, are F(4.5)/F(z ) = 24 and 43, respectively. Guided by these expectation values and the actual z [4.5] distribution of the candidates in the quasar field, we selected a color range of 13 < F(4.5)/F(z ) < 54 (Fig. 3.9, top). Now we considered the fields, where the 4.5 µm source list was created using the single-mode SExtractor option, i.e., not making use of the 3.6 µm information in the quasar field. We found 24 and 14 color-selected candidates for the quasar and the control field, respectively (Fig. 3.9, middle and bottom). These values are consistent with the corresponding numbers for SED-selected candidates of 23 and 12 in the quasar and the control field, respectively. Because the quasar field contains more cluster galaxy candidates, 29 via color and 29 via z phot (Fig. 3.9, top), we have scaled up the number of candidates in the control field by the factors 29/24 for the color-candidates and 29/23 for the SED-candidates. This results in 17 colour- and 15 SED-selected candidates. This leaves an excess of 12 (= 29 17) colour- and 14 (= 29 15) SED-selected sources in the quasar field.

48 CLUSTERING AROUND 3C Quasar field F 4.5 / F z color cand SED cand. other 10 2 Quasar field 4.5µm confined F 4.5 / F z color cand SED cand. other 10 2 Control field 2 F 4.5 / F z color cand SED cand. other F 4.5 [ µjy ] Figure 3.7: Color-magnitude diagram F(4.5)/F(z ) versus F(4.5) of the quasar field and control field 2. top: quasar field with all sources detected at 4.5 µm using SExtractor in double-image mode at 3.6 & 4.5 µm. middle: quasar field restricted to those sources detected at 4.5 µm using SExtractor in single-image mode, as was done for the control field 2. bottom: control field 2. Sources with z phot = 1.53 ±0.20 (SED determined candidates marked with circles) concentrate in a distinct color range 13 < F(4.5)/F(z ) < 54 indicated by the horizontal dotted lines. The number of color determined candidates is also given.

49 CHAPTER 3. THE CLUSTER SEARCH 49 Combining the control field counts we estimate an excess of colour- and SED-selected candidate cluster galaxies in the quasar field. Nature of cluster galaxy candidates The cluster galaxy candidates have an absolute rest-frame magnitude H K < 23.8 (AB system). An L galaxy at z 1.5 has H K = 23.6 (as determined from observations at 1 < z < 1.3 by De Propris et al. (2007)). Thus only the most luminous cluster galaxy candidates are detected on our maps. The galaxies are either giant ellipticals or dust-reddened starbursts. If giant ellipticals, most of their stellar mass has been formed at even higher redshift, but they may harbor some ongoing star-formation it would be relatively weak with respect to the already existing stellar mass but could show up at rest UV wavelengths. If dusty starbursts, they would have to be at least three times more luminous in the nearinfrared than the ULIRG Arp 220, indicating that a large stellar mass has already formed during an earlier episode. The distinction between ellipticals and dusty starbursts is difficult to make at rest wavelengths shorter than 1 µm using photometry only, albeit possible with adequate wavelength coverage (Pozzetti & Mannucci 2000). At z = 1.5, the six data points of our SEDs cover about rest wavelength 0.3 3µm. As mentioned in Sect , 75% of the SEDs are better fitted by the elliptical than the starburst template. In principle, additional sensitive photometry at rest wavelengths longer than 10 µm could improve the starburst elliptical distinction (Stern et al. (2006)). At z = 1.53, unfortunately, the 9.7µm silicate absorption enters the 24µm band, reducing the potential to detect the powerful MIR emission of starbursts. Apart from 3C itself, there is only one cluster candidate with a marginal (2σ) detection at 24 µm (Fig. 3.3). Its SED provides evidence for a dust-enshrouded starburst. While only elongated on the Y -band image, on the z -band image this galaxy appears as a double source with 0. 8 ( 7 kpc) separation; for determining z phot we have used the combined z -band photometry. This source is not covered by the HST image (Fig. 3.5). Because the z -band image is limited in spatial resolution, we have also inspected the morphology of the sources seen on the HST snapshot image. The HST frame covers 15 potential cluster galaxies (Fig. 3.5), 9 of which are detected. All 9 are extended, ruling out the (remote) possibility that they might have been brown dwarfs. Figure 3.8 shows the four sources detected in the F702W filter with photometric accuracy better than 5σ. The SEDs of these galaxies are best fit

50 CLUSTERING AROUND 3C RA/Dec=12 h 20 m s /33 o " RA/Dec=12 h 20 m s /33 o " arcsec V E V E V N RA/Dec=12 h 20 m s /33 o " V N RA/Dec=12 h 20 m s /33 o " arcsec V E V E V N arcsec arcsec V N Figure 3.8: HST F702W images of four cluster galaxy candidates (object numbers 9, 12, 14 and 15 in Table 3.1). The contours are linearly spaced in steps of 10% of the peak flux value (0%, 10%,... 90%). Arrows indicate the orientation of each panel. with the elliptical template. They have a regular shape, and none of them shows a peculiar morphology. This argues against starburst galaxy pairs and in favor of an evolved elliptical population. The remaining five of the 9 HST-detected sources are too faint to draw stringent morphological conclusions. The HST F702W photometry is of limited use because of the short exposure time, but six of the nine detected sources appear to show a rest-frame UV excess above the elliptical template (Fig. 3.3). This indicates some level of ongoing star formation activity. However, at rest wavelengths around 1 µm this activity appears to be outshone by large numbers of already-evolved stars.

51 CHAPTER 3. THE CLUSTER SEARCH 51 Having so many as 11 galaxies brighter than L would be very high for a local cluster, but passive evolution will cause these galaxies to become fainter by something like 3 mag 3 by z = 0 to become comparable to today s cluster ellipticals. Thus the photometric data are compatible with the existence of a cluster. 3.3 No evidence for clustering around 3C 437 In addition to the published results on 3C 270.1, I have analysed 3C 437 yielding interesting results Observations and data We obtained Spitzer maps of 3C 437, a radio galaxy at redshift z = 1.48 using IRAC and MIPS on the Spitzer Space Telescope. The exposure times on the sources are the same as for the Quasar 3C (120s for each IRAC band. and 100s for the MIPS bands). The image distribution of sources on the sky is similar to the image distribution of 3C We got IRAC (3.6µm,4.5µm,5.8µm,8.0µm) images of 4 4 on the Radio Galaxy and two other control fields south west (IRAC 3.6µm,5.8µm) and north east (IRAC 4.5µm,8.0µm). The 3σ detection limit of the maps is 4µJy for the first two IRAC bands (3.6µm,4.5µm),10 µjy for the last two IRAC bands (5.8µm,8.0µm) and 100µJy for the 24µm MIPS band. We also obtained HAWKI H band images at the VLT 4 and MEGACAM z band images at the MMT. The sensitivity for the VLT HAWKI image is better than 5 µjy with an exposure time of 60 minutes. This is sufficient, because at redshift z 1.5 the ERO SEDs peak in the H-band. The sensitivity for the MMT MEGACAM z band image is better than 1 µjy with an exposure time of 60 minutes. The reduction of the images Spitzer Images was achieved identical to that of 3C 270.1, while the HAWKI reduction was done with the ESO pipeline Results We found no evidence for clustering aroung the Radio Galaxy 3C 437. This is demonstrated in the color-magnitude diagram in Fig Also the inclusion of the H-band data does not provide any hint in favour of a cluster (or proto-cluster) 3 Passive evolution was estimated via a Starburst99 model (Vázquez & Leitherer 2005) with an instantaneous burst age 1 Gyr before z = 1.53 and thus an age of 10 Gyr at z = 0. 4 Vera Hoffmeister & Rolf Chini

52 NO EVIDENCE FOR CLUSTERING AROUND 3C 437 3C 437 center field 10 2 F 3.6 / F z F 3.6 [ µjy ] 3C 437 left comparison field 10 2 F 3.6 / F z F 3.6 [ µjy ] Figure 3.9: Color-magnitude diagram F(3.6)/F(z ) versus F(3.6) of the quasar field and control field. top: quasar field with all sources detected at 3.6 µm bottom: control field with all sources detected at 3.6 µm. The dotted lines mark the range of EROs having a redshift similar to that of 3C 437. Note that there is no excess of such objects in the cluster field compared to the left field

53 CHAPTER 3. THE CLUSTER SEARCH 53 around this radio galaxy. If 3C 437 were actually surrounded by a cluster (as 3C 270.1) the one explanation could be the limited sensitivity leading to the fact that we only see the tip of the ice berg of the galaxies in the environment of the radio source. Maybe the cluster is in the state of growing and the existing satelite galaxies are small and not luminous enough to detect them. This is consistent with the predictions that galaxy clusters should form between redshift z = 1.5 and z = 2.0. The case of 3C 437 reveals that some of the huge and massive radio sources, which would lie in the cluster potential, show no sign of a large number of massive clustered galaxies in their environment Preliminary Conclusion Given that we have suitable data for two massive radio sources 3C and 3C 437, both at redshift z 1.5, a formal statistical extrapolation would yield, that at this redshift range only 50 % of massive radio sources are surrounded by a cluster or protocluster of EROs. This is roughly consistent with the high redshift decline of clustering predicted by cosmological models. Obviously, the statistics is too sparse and larger samples should be investigated, including spectroscopic verification of the redshift of the cluster candidates. Nevertheless, the pilot studies demonstrate the sucsess of the photometric method to identify cluster candidates around high redshift radio sources, the cluster signposts.

54 NO EVIDENCE FOR CLUSTERING AROUND 3C 437

55 Chapter 4 Parallel 3D radiative transport This chapter describes the model part of my thesis. Containing a new developed parallel 3D Monte Carlo method with a speed up factor of 100. Careful benchmarking of the new method with existing codes for 1D and 2D configurations Expansion of the Benchmarks to the third dimension, which is now possible with this speed up Explaining the average 2 16µm SEDs and spectra of high redshift AGN with a model consisting of a central power source and three dust components. 4.1 Theory Monte Carlo method One approach to solve the radiative transfer equation in an inhomogeneous dusty medium is the Monte Carlo (MC) method. In our MC procedure the bolometric luminosity L = 0 L ν dν of the heating source is divided into N monochromatic photon packages of equal energy ǫ = L/N. The spectral energy distribution of the source is divided into 1 j m bins. The width of the emitted photon package (in the following called photons) 55

56 THEORY Figure 4.1: Illustration of our model grid and a typical photon path in the x,zplane. A photon package is emitted at the heating source (i), interacts with the dust, and can be either absorbed (ii) or scattered (iii). It is also possible that more than one event occurs in a grid cell (iv). The line style illustrates the frequency change during absorption events. Finally the photon leaves the model space (v).

57 CHAPTER 4. PARALLEL 3D RADIATIVE TRANSPORT 57 is set by its energy and the frequency can be calculated from (j 1) νj 2 L = L ν dν. (4.1) m o Photon packets with high frequency photons have a smaller number of photons than packets with low frequency photons. It is useful to set up a second grid of frequencies to account for the dust re-emission at long wavelengths and low temperatures. In the radiative transfer problem it is necessary to treat the interaction between photons and dust particles. This is described by the cross-sections for absorption κ abs ν and scattering κ sca ν. The model space is set up as a Cartesian grid in which each cube can be divided into sub-cubes of volume V i and constant density ρ i (Fig. 4.1), where i is the index of the sub-cube. This separation of cubes allows a finer sampling wherever required: For example close to the dust evaporation zone or at places where the optical depth of a cube is large. The trajectory of the photons is illustrated in Fig Photons are initially emitted by the source at frequency ν j and then traced through the model (Fig.4.1(i)). The direction of a photon is randomly chosen so that 0 φ < 2π and 1 cos θ 1. The distance from the entry point of a cell to the exit point in travel direction of the photon is l i. In the MC method the interaction of photons with the dust in a sub-cube is probabilistic and can be determined with a uniformly distributed random number 0 z 1 (Witt (1977), Lucy (1999)) and the optical depth τ i (ν) = (κ abs ν + κ sca ν ) ρ i l i. (4.2) Photons leave the cell if τ i log z and otherwise they interact. If the photon leave the cell it enters along its travel direction a neighboring cell or reaches the border of the model space. We use a new random number in the new cell to determine the interaction probability of photons with the dust 1. The photon interacts with the dust after it has traveled a distance l i, given by l i = ρ(κ abs ν log z + κ sca ν ), (4.3) The travel distance l i defines the point in the cell where interaction takes place and photons are either scattered (Fig. 4.1(ii)) or absorbed (Fig. 4.1(iii)). As indicated in Fig. 4.1(iv), it is possible that multiple scattering or absorption 1 We confirmed that the interaction probability calculated with a new random number is in agreement with the treatment of a travel distance determined by one random number (see also Lucy (1999)).

58 THEORY events occur in one cell. The probability of scattering is given by the albedo A ν = κ sca ν / (κ sca ν + κ abs ν ) and the chance of an absorption event is 1 A ν. When a photon scatters it keeps its frequency, but changes its travel route as given by the phase function. We use the asymmetry factor g ν to approximate anisotropic scattering (Eq. 2.7 in Krügel 2008): κ sca ν = (1 g ν )κ sca ν. For isotropic scattering g equals 0. If the photon is absorbed a new photon is immediately emitted, with a new direction and usually a different frequency. The emitted photon package has the same energy as the absorbed one. The absorbed photons heat the dust in the cell and the temperature can be calculated by the number of absorbed photons. After k absorptions the grains reach the temperature T k, κ abs ν B ν (T k ) dν = kǫ 4πρ i V i, (4.4) where B ν (T) is the Planck function. The dust emits a photon given by the minimum frequency ν computed from ν 0 κ abs ν db ν (T k ) dt k dν z 0 κ abs ν db ν (T k ) dt k dν. (4.5) In the Monte Carlo method one follows all N photon packages through the dust cloud until they reach the outer boundary of the model. The dust temperature of a cell is calculated iteratively. Each absorption event increases the temperature of the dust which implies a change of the radiation field unless local thermal equilibrium is reached. As in the method by Lucy (1999) convergence is usually reached after 3-5 iterations. The number of photons, which are needed to calculate the temperature, can be decreased by an optimization algorithm developed by Lucy (1999). In his treatment every photon package which crosses a cell accounts to a temperature increase. However, as the interaction takes place in every cell this procedure is computational expensive. An iteration free MC method is developed by Bjorkman & Wood (2001). Other MC methods and optimizations exists citep(min09,bae08,gor01) Parallelization Microprocessors based on a single central processing unit (CPU), like the Intel R Pentium R or the AMD R Opteron TM, increased their performance during more than two decades. The actual speed of these microprocessors are giga floating-

59 CHAPTER 4. PARALLEL 3D RADIATIVE TRANSPORT 59 point operations per second (GFLOPS), which means they are able to calculate one billion mathematical operations per second. The increase of the number of floating point operations per CPU slowed down during the last years (Fig. 4.2). This is mainly caused by the increased energy consumption and heat dissipation for higher clock speeds. However, to further increase the FLOPS of the CPUs nearly all vendors developed models where multiprocessing units are used in each chip. This switch has a tremendous impact on the code developer (Sutter & Larus 2005). Most of the existing codes are written as sequential programs (von Neumann 1945). These sequential codes do not scale with multi processing units, which yields to a small speed increase with the new microprocessors. Without the speed up it is not possible to implement new features into these codes. The solution to this challenge are parallel codes which use the power of the multi processing units. A sequential code segment which is executed on different cores of the multiprocessor is called thread. In order to get good performance these threads should act indepently. Every interaction of threads usually wastes time, for instance, if one thread finished the calculation but has to wait for the solution of the other thread to continue. Figure 4.2: Floating-Point operations per second for the central processing unit (CPU-blue) and NVIDIAs graphics processing unit (GPU-green).

60 THEORY Figure 4.3: Show the CUDA scheme take from CUDA manual. The executes serial code and organize the exectution on the graphics cards Pseudo random number generator in parallel In this section we discuss the pseudo random number generator for parallel applications. One of the major properties of Monte Carlo methods is the huge dependence on random numbers. Therefore it is necessary to have a random number generator, which can produce random numbers as fast as possible. Another important fact is the needed unique sequence of numbers for every thread, in parallel running systems. This unique sequence can be created serially. But this is slow for Monte Carlo simulations because of the large amount of these random numbers needed. In order to get the best performance increase from the parallelization we use a special random number generator. It is called parallel Mersenne Twister (Matsumoto & Nishimura (2000)) This random number generator is able to produce unique sequences for each parallel thread. We also performed several tests in order to check the randomness of this random number generator. All of our tests were passed successfully, and the performance scaling is nearly the number of parallel threads. This random number generator fulfills all the conditions needed for fast parallel Monte Carlo simulations CUDA TM parallelization on graphic cards This section contains a short overview of the data parallelism and program structure of CUDA. Fig. 4.3 and 4.4 illustrates the concept of a cuda project. The

61 CHAPTER 4. PARALLEL 3D RADIATIVE TRANSPORT 61 Figure 4.4: Show the separation of grids into blocks. Ever thread in block can access the same shared memory while threads in different blocks need to be independent of each other computing system consists of a host, the traditional CPU, and one or more devices for massive parallel computing (GPUs). The CUDA program consists of phases which are executed either on the CPU or on the GPU. The code parts without parallel structure are executed on the host and the other on the device. The first phase of a project initializes all the needed data on the host and than copy the data which is needed for parallelization into the device (Fig. 4.3). The second phase is the execution of the threads, called kernels, on the device. The huge parallel power of the graphic cards is utilised by an auto scaled parallel running of threads. The scheme of the parallel running threads is shown in Fig The threads are grouped as blocks which have access to the same fast shared memory. The blocks are grouped into a grid. Threads from different blocks can only interact through the slow global memory of the device. After finishing phase two the device copies the data during phase three back to the host. Depending on the project the host may analyse this data and either repeats phase one or finishes the project. Further details are discribed in NVIDIA (2009). 4.2 Imaging The Monte Carlo simulation was speeded up for axisymmetric problems. In such a symmetry the calculation of one octant is sufficient. For the visualization process the complete cube is necessary. Reflecting and moving the octant coordinates

62 IMAGING e z e x (0,0) e z e y Figure 4.5: This figure shows the geometrical situation for parallel projection. The MC cube has the coordinate system e x, e y, e z and the detector plane has the coordinate system e x, e y and the pixels x,y. The center of pixel x 0 = (x 0,y 0) is at position x 0. The vector e z defines the observing direction. x 0 (0,0,0) e y e x (x,y,z ) leads to the new cube coordinates (x,y,z): x = n x + ( 1) ( i 2 0 mod2) x + 1 i [1 + ( 1)( mod2) ] y = n y + ( 1) ( i 2 1 mod2) y + 1 i [1 + ( 1)( mod2) ] z = n z + ( 1) ( i 2 2 mod2) z + 1 i [1 + ( 1)( mod2) ] Where i = [0..7] is a running number over all octants, n x,n y,n z are the number of grids in the octant and n x,n y,n z are the number of grids in the cube. At astronomical distances the MC cube can be displayed using parallel projection. We set the image at position (D,θ,φ). Where D is the distance from the center of the cube to the image plane and (θ,φ) are angles in spherical coordinates (Fig. 4.6c). Vector e z is the normal surface of the image plane and e x, e y are the normal unit vectors for the x,y axis. e z = (sinθ cos φ, sin θ sin φ, cos θ) (4.6) e x = (cosθ cosφ, cos θ sin φ, sin θ) (4.7) e y = ( sin φ, cos φ, 0). (4.8) The required size of the image is derived by projection of the vertices of the cube (Fig. 4.6). It consists of n x n y quadratic pixels and was at position x 0 = [x 0,y 0,z 0 ], behind the cube in observing direction. The position x 0 has

63 CHAPTER 4. PARALLEL 3D RADIATIVE TRANSPORT 63 O S l e x e z θ φ e z e y Figure 4.6: The left image shows the line of sight l and the position of the observer O and the intersection of l with the z-plane of the cube. The upper right Image shows the definition of the spherical coordinates. The lower right Image shows the projection of the cube. the pixel coordinates [x 0,y 0] (Fig. 4.5). A vector x x,y in cube coordinates is represented in pixel coordinates (x,y ) of the image by: x x,y = x 0 + (x x 0) e x + (y y 0) e y. (4.9) The ray tracer follows the line of sight l from the observer O in direction e z of each pixel through the 3D grid. The vector form of the linear equation for l is: l = O + a ez, (4.10) where a is a scaling factor. For the xy-plane (z = c = const.) we calculate the intersection point between l and the z-plane, with the scaling factor a xy = c (0, 0, 1) O (0, 0, 1) e z. (4.11) Analogously we calculate the intersection points with the x,y-plane. The point S, where the line of sight hits the border of the cube, is derived with Eq The distance between the entry point S into the cube and the center of the cell is l = ( O S) 2 (4.12) as shown in Fig Taking the emission and absorption of every cell along this line, we compute the flux received by the observer. For simplicity we neglect foreground absorption. The emission is calculated according to Kirchhoff s law. A dust grain in a

64 IMAGING radiation field absorbs as much energy as it emits. For local thermal equilibrium (LTE) the emissivity ǫ ν per unit mass is ǫ ν = K abs ν B ν (T), (4.13) with temperature T, the absorption cross section Kν abs per unit mass and the Planck function B ν (T). The intensity of a cell with dust mass m dust is I ν (0) = ǫ νm dust A p, (4.14) where A p is the projected source area on the detector plane. The light, which reaches the observer, is weakened by dust, located between the cell and the border of the cube. For pure extinction I ν = I ν (0)e τν (4.15) where τ ν is the optical depth at frequency ν. It is calculated with the distance l (eqn. 4.12) and the dust density ρ( x) distribution l τ ν = K abs ν ρ ( x)ds. (4.16) 0 The flux density is F ν = I ν Ω (4.17) with solid angle Ω, which is for large distances D: Ω = A source D 2 (4.18) where the source area A source = A p for parallel projection. Therefore the flux density is F ν = Kabs ν m dust B ν (T)e τν. (4.19) D 2 for each cell. Adding the flux densities from the cells along the line of sight l we compute the flux density for each detector pixel. F x,y = x,y,z l F ν (4.20)

65 CHAPTER 4. PARALLEL 3D RADIATIVE TRANSPORT 65 Figure 4.7: 10 micron image of our solar system up to Jupiter at a distance of 1 parsec. This face on view is generated with ray-tracer and 64k 64k pixels Solar system The solar system is an excellent environment to test the correct behavior of our raytracing technic. We set the solar system at a distance of 1 parsec and simulate the observed flux at 10 micron. The flux can be determined analytically with a black body radiation at the different temperatures given with Eq The simulated fluxes in the image 4.7 are correct within 1% accuracy. The difference is caused by the number of observing pixels in comparison to the model pixel. More observing pixels correspond to a better accuracy. It is useful to give a lower limit of observing pixels per model pixel to guarantee the 1% level correctness. 4.3 Benchmark test To test our code we use several benchmarks tests, which are available in the literature. All test cases assume local thermal equilibrium with the radiation field. For the simplest 1 dimensional case we use the test from Ivezic et al. (1997). For the 2 dimensional case we use the disk geometry introduced by Pascucci et al. (2004). And for the 3 dimensional case we extend the Pascucci et al. (2004) disk by spiral structure, which may be caused ba a proto-planet. We show that we can reproduce the temperature distributions and the SEDs for all existing benchmark cases in much less time Spherical symmetry (1D) This benchmark is completely described in Ivezic et al. (1997). They assume a central point source which is embedded in a spherical symmetric dust envelope. The innermost volume is free of dust. The source radiates as a black body with a given temperature T. The one dimensional dust density distribution is assumed

66 λf λ / F tot BENCHMARK TEST λf λ / F tot diff. [%] wavelength [µm] Figure 4.8: upper panel: SEDs from DUSTY as reference code for 4 different optical depth of 1(magenta), 10(blue), 100(orange), 1000(green). The colored lines shows the SED calculated with our code, while the black line represents the reference code. Note for the higher optical depth 100 and 1000 we use a larger number of iterations. lower panel: The difference of our code to the solution of Ivezic et al. (1997) is less than 5%. to decrease with a power law r p. They combine the parameters, dust density, envelope size, opacity in one parameter, the optical depth, which is fixed at 1 µm. To minimize the number of analytic functions they choose. q abs = q sca = 1 (4.21) for λ 1µm, and q abs = 1 λ,q sca = 1 λ 4 (4.22) for λ > 1µm. They argue that the value of 1 µm is chosen, because this is the typical astrophysical grain size. These quantities fully describe the benchmark problem. We compare our code with four different values of optical depth, at temperature T = 2500K, and a constant density distribution p = 0. The outer radius is fixed to 1000 r inner, with r inner given by the radiation field (Table 4.1). It is calculated with equation 4 in Ivezic et al. (1997) and references therein. We performed Monte Carlo simulations for this one dimensional test case with photon packages per iteration, on a Cartesian grid with 10 6 cells. The

67 CHAPTER 4. PARALLEL 3D RADIATIVE TRANSPORT 67 Table 4.1: Values for r inner for four different optical depth, r outer = 1000 r inner, L = L and T = 2500K. The values are calculated with Equ. 4 in Ivezic et al. (1997) τ r inner [ cm] number of iterations depends on the optical depth for, τ = (1, 10, 100, 1000) the number of iterations is (3, 3, 4, 5). The time needed for the highest optical depth configuration is 78 min. The temperature distributions are compared with the results of the dusty code for the same optical depth. We reproduced the temperature distribution of the dusty code, within an error smaller than one percent. With this temperature distribution we generated the SEDs shown in Fig We plot on the y-axis λf λ divided by the total flux F tot = F λ λdλ and the wavelength in micron on the x-axis. Each curve represents a different optical depth. The SEDs generated with our code and the one from the reference code DUSTY are in good agreement. The error is below 5 percent for optical depth τ < 100. It is largest on the boundaries of the simulated wavelength range, where the flux F λ is low compared to the total flux. This is caused by the statistical behavior of the Monte Carlo method. It increases for the highest optical depth case of τ = 1000, but is still below 5 percent. The larger deviation is caused by the limited number of grid cells Disk geometry (2D) This benchmark is completely described in Pascucci et al. (2004). They consider the general astrophysical case of a point source surrounded by a circumstellar dust disk. The disk is composed of spherical dust grains, with absorption and scattering properties of 0.12 µm silicate grains. (see Fig. 4.10). The absorption and scattering efficiencies are taken from their web-page. 2 With an inner region r < r inner free of dust, they adopt the following density distribution ρ(r,z) = ρ 0 ( r ) 1.0 e π 4 h(r) 2 (4.23) r d z with h(r) = z d ( r r d ) (4.24) 2

68 BENCHMARK TEST λf λ [Wm -2 ] 20 difference [%] wavelength [µm] wavelength [µm] Figure 4.9: left column: face on disc at an inclination angle of 12.5 right column: edge on disc at an inclination angle of 75 upper panel: SEDs from (Pascucci et al. 2004) as reference code for 4 different mid-plane optical depth of τ = 1(purple), 10(green), 100(blue). The symbols shows the SED calculated with our code. Note for the higher optical depth of 100 we use a larger number of iterations. lower panel: Difference of our code with to the solution of Pascucci et al. (2004). These equations describe a disk which is similar to that of Chiang & Goldreich (1997, 1999). The complete list of model parameters is shown in Table 4.2. We performed Monte Carlo simulations with photon packages per iteration, grid cells and 3 iterations for the optical depth of τ = 1, 10 and 4 iterations for τ = 100. The calculated SEDs and relative differences to the reference code are shown in Fig. 4.9 for two different inclinations(face on and edge on) and four different optical depth values. The results of our three dimensional Monte Carlo codes agree with the benchmark to better than 10 percent. The largest deviation arises at the 9.8 micron feature for the highest optical depth case. This can be explained by the fact that for this case the grid arrangement plays an increasing role. Our code uses a Cartesian grid which is not as ideal as spherical grids for axis symmetric problems. We also want to note that our code reproduces the slope

69 CHAPTER 4. PARALLEL 3D RADIATIVE TRANSPORT 69 Table 4.2: Model parameter space as in Pascucci et al. (2004) Symbol Meaning Value M Stellar Mass 1 M R Stellar Radius 1 R T Stellar effective temperature 5800K R out Outer disk radius 1000 AU R in Inner disk radius 1 AU z d Disk height 125 AU a Grain radius 0.12 µm ρ g Grain density 3.6 gcm 3 τ v Optical depth at 550 nm 0.1, 1, 10, 100 at long wavelength correctly. This slope only depends on the dust properties Dust properties We use an analytic function for the dust properties, which can be described as follows for λ 0.01µm, q abs = q sca = 1 (4.25) λ 3 for 0.01µm λ 0.5µm, q abs = q sca = 1 (4.26) for 0.5µm λ 10µm, and q abs = 1 λ,q sca = 1 λ 4 (4.27) for λ 10µm q abs = ac 2 1 (10 4 λ) 2,q sca = 0 (4.28) and at 10 micron we add a Gaussian with a maximum value of 0.1 and a deviation σ = 1, where a is the dust grain radius, λ the wavelength in micron. The dust extinction coefficient is plotted in Fig against the dust properties of Pascucci et al. (2004). We expand the extinction up to 0.01µm Spiral expansion of disk structure (3D) From theoretical considerations one may suggest that in the process of planet formation a circumstellar dust disk will show spiral structures in the dust density distribution. Hydrodynamical simulations of proto-planetary disks show spiral structures, produced by the orbiting planet (FARGO). Therefore we extend the

70 BENCHMARK TEST Figure 4.10: Extinction efficiency for used in the 2D and 3D configurations. The solid line shows the extinction coefficient for astronomical silicate grains as taken from Pascucci et al. (2004). The dashed line is the extinction coefficient described by equations dimensional case by adding a spiral structure to the dust density distribution. The dust density is described with Eqn and decreased by a factor of 10 5, in the spiral: r(φ) = R out 2πn loop φ (4.29) Where n loop is the number of loop in the spiral and R out is size of the disk. This benchmark configuration with the simplified spiral dust geometry shows weak differences in the SEDs compared to the two dimensional disk. (Fig 4.13) Which is the different dust emission peak, caused by the missing of warm dust in the spiral structure. For small optical depth up to 100 this shifts the peak to right for a spiral configuration Clumpy Torus geometry (3D) Another astronomical source which is suitable to be calculated in three dimensions is the environment of an AGN. In the unified scheme (Antonucci 1993) these objects are likely surrounded by a dust torus, which might be composed of dust clumps (Nenkova et al. 2002). We use this as a second benchmark configuration

71 CHAPTER 4. PARALLEL 3D RADIATIVE TRANSPORT 71 Figure 4.11: Density structure for the 3D spiral test case. The values are normalized to the maximum density. The density decrease from cyan(minimum) to black (maximum) Figure 4.12: Left: modeled 11 µm flux at a distance of 50 pc. Right: Prediction for the planed European Extremly Large Teleskop (EELT)

72 BENCHMARK TEST λf λ [Wm -2 ] 20 difference [%] wavelength [µm] wavelength [µm] Figure 4.13: left column: face on disc at an inclination angle of 12.5 right column: edge on disc at an inclination angle of 75 upper panel: SEDs from (Pascucci et al. 2004) as reference code for 4 different mid-plane optical depth of τ = 1(purple), 10(green), 100(blue). The symbols shows the SED calculated with our code. Note for the higher optical depth of 100 we use a larger number of iterations. lower panel: only a small difference at long wavelengt of the 3D spiral expansion to the solution of Pascucci et al. (2004).

73 CHAPTER 4. PARALLEL 3D RADIATIVE TRANSPORT 73 Figure 4.14: Density distribution for the 3D torus structure. On the left is the face on view. While the right shows the edge on view. Values are normalize to maximum density and the density decrease from red (maximum) to black (minimum). Even in the edge on view it is possible to see the hot inner dust. for the three dimensional radiative dust transfer. We assume clumps of the same size with a constant density ρ clump. These clumps are distributed randomly within a half opening angle of 35 degrees around the mid-plane. We use the random number generator suggested by numerical recipes, which is very fast and can be easily reproduced. I n+1 = I n (4.30),with I 0 = 0. The floating point random number f n+1 is calculated by f = I n To check if the random number generator is working correctly, we list the first 4 values I 1 I 4 in Table 4.3. The value ρ 0 is given in Table 4.4, and r is the Table 4.3: 4 values I 1 I 4 of the random number generator for I 0 = 0 i 1 i 2 i 3 i 4 HEX 3C6EF35F D1CCF6E9 AAF95334 DEC float distance of the clump center to the AGN. We distribute N clump = 1 000, and of these clumps in an area within a half opening angle of 35 degree between r inner and r outer. The density map is shown in Fig 4.14 This dust distribution can

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