The Central Stellar Structures of Active Galaxies: Insights Into Black Hole - Galaxy Coevolution

Transcription

1 Wesleyan University The Honors College The Central Stellar Structures of Active Galaxies: Insights Into Black Hole - Galaxy Coevolution by Alexandra E. Truebenbach Class of 2012 A thesis submitted to the faculty of Wesleyan University in partial fulfillment of the requirements for the Degree of Bachelor of Arts with Departmental Honors in Astronomy Middletown, Connecticut April 2012

2 The purpose of astronomy is to explore the boundaries between Heaven and Man, to comprehend changes old and new, and finally to form a total perspective. Sima Qian, Chinese Philosopher and Astronomer ca. 100 B.C.E.

3 Acknowledgments First and foremost I d like to thank my advisor, Ed Moran. Without your guidance, this thesis would not exist. Thank you for giving me support when I needed it but always letting me find my own way. Your encouragement to become involved in research early on has led me to become the astronomer I am today. Thank you to Brooke Simmons for advising me on GALFIT and Roy Kilgard for answering my many technical questions. Thank you to my parents for supporting me in everything I do. Even when I out-of-the-blue announced I wanted to study astronomy, you never questioned me. And most of all, thank you for my education; it is truly the greatest gift I have ever been given. Thank you to the Wesleyan Astronomy Department: I came to Wesleyan thinking that I wanted to study astronomy but it is this department that made me fall in love with astronomy and want to continue working in this field after graduation. And finally, thank you to all my friends, who listened to me talk about my thesis at length. This project consumed my thought for months and it was all I could talk about; I appreciated your tolerance. And especially thank you to Michael, who listened to daily updates on my progress and encouraged me to take breaks.

4 Contents 1 Introduction Galaxy - Black Hole Coevolution Low-Luminosity Galaxies Bulgeless High-Mass Galaxies Previous Studies Black Holes in Low-Mass Galaxies Black Holes in Bulgeless Massive Galaxies Extreme Black Hole Hosts Surface Brightness Profiles GALFIT Fitting Strategy NGC Data Acquisition and Processing PSF Surface Brightness Profile Fitting Error Analysis High-Mass Galaxy Sample 49

5 4.1 Sample Selection and Data Acquisition Spectral Analysis Surface Brightness Profile Fitting Discussion NGC High-Mass Galaxy Sample Conclusions & Future Work 77 Bibliography 79

6 Chapter 1 Introduction Within the centers of galaxies lurk enormous, invisible masses. For most of their lifetimes these objects wait, quietly hidden by their hosts. But occasionally gas or dust will stray within their reach and become trapped by the huge gravitational potential of these objects. As the material hurtles towards the lurking mass, the nucleus of the galaxy flares to life, making the presence of the supermassive black hole known. These supermassive black holes [SMBHs] are believed to reside in the centers of most, if not all, galaxies. Together, the galaxy and the SMBH go through life, evolving and growing. Yet, for the most part, the galaxy is unaffected by the comparatively small black hole. Because of this, SMBHs are largely invisible unless they are accreting material. As the material accelerates towards the black hole, it emits high energy radiation, lighting up the center of the galaxy. During this accretion event, the central regions of the galaxy are referred to as an active galactic nucleus [AGN]. Prior to the mid-1990s, supermassive black holes at the centers of galaxies were thought to be rare occurrences (Osterbrock 1991). In fact, many were still unconvinced that active galactic nuclei were powered by the accretion of interstellar material onto SMBHs. It was generally believed that the high emission from quasars was caused by accretion onto unidentified massive dark objects [MDOs].

7 1. Introduction 2 To eject such large amounts of energy, these MDOs had to be far bigger than the stellar-mass black holes detected within our own Galaxy. Therefore, there was no reason to suspect that such a massive black hole could even exist. Nevertheless, the possibility that these MDOs were supermassive black holes residing in the centers of galaxies such as our own was first suggested by Salpeter (1964) and Zel dovich (1964). Their assertion was later supported by many others, including Lynden-Bell (1978), who calculated that the amount of energy emitted through accretion onto a black hole would be consistent with the observed energy emitted by quasars. However, all of these studies were purely theoretical and lacked observational support. These studies also only dealt with black holes at the centers of galaxies with active galactic nuclei. Without an observation of a SMBH at the center of a quiescent galaxy like our own there was no reason to believe that SMBHs were the norm rather than the exception. The first observational evidence for SMBHs at the centers of galaxies was found by Sargent et al. (1978), who used dynamical measurements of stars within M87 to measure the mass of the large central object. Observations of this large elliptical radio galaxy revealed a sharp increase in stellar velocity dispersion in the core of the galaxy. They concluded that this increase was consistent with a central MDO of M M concentrated in a volume less than 110 ± 15 pc in radius, implying that the object was very dense. Additionally, evidence for SMBHs in quiescent galaxies was found by comparing the mass-to-light [M/L] ratios for the nuclei of these quiet galaxies to the ratios for the outer regions of the same galaxies. A nuclear M/L ratio significantly higher than that found in the rest of the galaxy implies that this region contains some object that is far more massive and less luminous than the stellar population expected to reside within the nucleus. This can provide evidence against the theory that AGN are caused by

8 1. Introduction 3 luminous bursts of star formation. If star formation were the cause of AGN, then one would expect to find a relatively constant M/L ratio throughout the galaxy. Kormendy (1988) used this method to find a MDO of M M at the center of the quiescent galaxy M104. This showed that MDOs can also reside within galaxies that do not appear to have AGN. Yet despite this evidence, there was still no direct evidence that allowed astronomers to link MDOs to black holes. Finally, higher-resolution studies of galactic nuclei revealed that the densities of the MDOs within the nuclei were so high that they had to be black holes. The first key measurement that solidified the central black hole model was by Miyoshi et al. (1995). Using water masers around the nucleus of the AGN NGC 4258 to measure the velocity dispersion of the galactic bulge with a high precision, Miyoshi et al. (1995) were able to tightly constrain both the mass of the MDO and the size of the small area in which it must be located. These calculations revealed that the central object must have a density of at least 10 9 M pc 3, a density typically only reached by black holes. This and other studies, including the direct observation of stars orbiting the SMBH at our own Galaxy s center (Genzel et al. 1997; Ghez et al. 1998), cemented the theory of SMBHs at galactic centers and led to the creation of a unified model of SMBH-powered AGN. 1.1 Galaxy - Black Hole Coevolution Although supermassive black holes are, by definition, extraordinarily large, they are tiny compared to their host galaxies. Given the masses of known SMBHs, the region of the galaxy in which the black hole dominates the galaxy s gravitational potential is quite small compared to the overall size of the galaxy. This region, known as the sphere of influence, is proportional to the mass of the su-

9 1. Introduction 4 permassive black hole. For a black hole with mass M = 10 9 M in a galaxy with a stellar velocity dispersion of σ = 200 km s 1 (the stellar velocity dispersion is a measure of the mass of the galaxy), the radius of the sphere of influence is only 100 pc. The galaxies with SMBHs of this size have visual radii on the order of 10 5 pc, orders of magnitude larger than the spheres of influence of the largest supermassive black holes. Thus, SMBHs only interact with a small central portion of their host galaxies. Similarly, host galaxies rarely influence their central black holes. In order for material from the galaxy to fall into the sphere of influence of the black hole, a significant amount of potential energy must be dissipated from the system. Often there is no mechanism with which to dissipate this energy, thereby preventing material from reaching the black hole. All of these factors indicate that the galaxy and SMBH are unaware of each other and should therefore be unrelated. Yet even before the presence of SMBHs within galactic nuclei was confirmed, correlations between characteristics of the central massive dark objects and the large-scale properties of their host galaxies were discovered. Kormendy & Richstone (1995) demonstrated not only that the dynamical movements of stars in the bulges of large galaxies indicated the presence of central objects of masses M, but also that the masses of these SMBHs scale linearly with the absolute blue luminosity of the host bulge or elliptical galaxy (in the absence of stellar population variations, luminosity is a proxy for the stellar mass of a galaxy). This scaling relationship is shown in Figure 1.1 (from Kormendy & Richstone 1995). Later the relationship between black holes and their host galaxies was extended to include the scaling relation between the mass of the black hole and the velocity dispersion of the central stellar bulge, M BH σ (Ferrarese & Merritt 2000; Gebhardt et al. 2000). Like the luminosity of the bulge, velocity

10 1. Introduction 5 Figure 1.1: The correlation between M BH and the absolute B magnitude of the bulge as observed by Kormendy & Richstone (1995). Circles and squares represent objects with stellar- and gas-dynamically determined black hole masses, respectively. Crosses represent upper limits on M BH. Kormendy & Richstone (1995) theorized that this correlation was only the upper cusp of a relation that could continue to lower-mass black holes. dispersion is also an indication of the mass of the galaxy. One of the plots used by Ferrarese & Merritt (2000) to determine this relationship is shown in Figure 1.2. Ferrarese & Merritt (2000) found that this relation was much tighter than the M BH L Bulge correlation developed by Kormendy & Richstone (1995) and that all of the scatter could be attributed to measurement errors alone. More recently, the above relations have been refined and other correlations have also been explored in an effort to better understand the relationship between SMBHs and their host galaxies. For example, Magorrian et al. (1998) found a correlation between the mass of the black hole and the mass of the bulge (M BH M Bulge ). This initial relation had a very large scatter because the galaxies used had poorly resolved spheres of influences, which led to overestimates of the black hole masses by up to a factor of 10 (Häring & Rix 2004). Better imaging techniques

11 1. Introduction 6 Figure 1.2: The black hole mass - velocity dispersion relation as derived by Ferrarese & Merritt (2000). M BH is plotted against the central velocity dispersion σ c of the host bulge or elliptical galaxy (closed circles) and the root mean squared velocity v rms measured at one-fourth of the effective radius (open circles). Crosses represent the lower limit of v rms for cases where the exact v rms could not be determined. The dotted and solid lines represent the best linear fits to the data using σ c and v rms, respectively.

12 1. Introduction 7 and telescopes have allowed studies like that conducted by Häring & Rix (2004) to calculate more accurate black hole masses in order to refine the M BH M Bulge relation (e.g., Bentz et al. 2009). Additionally, the morphology of the central stellar component of a galaxy has also been observed to correlate with the mass of the black hole. Graham et al. (2001) found that the more centrally concentrated a galactic bulge is, the larger the SMBH within. This relationship is at least as accurate, if not more accurate, than the M BH σ correlation. Overall, the study of easily visible galaxies with SMBHs would suggest that the largest black holes exist within galaxies that have high-mass, centrally concentrated bulges with large velocity dispersions. Yet the origin of black hole - galaxy correlations remains an open question. Black holes at the centers of galaxies - even those as massive as 10 9 M - have spheres of influence that are not large enough to dominate the velocity dispersions of their host galaxies. Conversely, the vast majority of mass within a galaxy cannot reach the sphere of influence of the central black hole. Both the black hole and the host galaxy are unable to affect each other. Therefore, some other process must be occurring that affects both the black hole and its host galaxy in such a way that the mass of the black hole scales with the mass of its host. To explain these galaxy - black hole correlations, a system of coevolution was suggested (e.g., Volonteri et al. 2003). This model builds off of the hierarchical model of galaxy evolution first suggested by Toomre (1977b), in which galaxies evolve and grow not only through their own internal processes, but also through interactions with other galaxies. Sanders et al. (1988) expanded upon this theory by suggesting that the merging of two galaxies with AGN could also lead to the growth of the AGN at the center of the resulting galaxy. The merger or interaction of two galaxies can trigger large scale gas inflows, pushing gas and dust towards

13 1. Introduction 8 the nucleus and providing fuel to turn on the AGN (Hernquist 1989). Once AGN and supermassive black holes were linked, it became apparent that galaxy interactions simultaneously added material to the dominant galaxy and sparked accretion onto the black hole, causing it to also gain mass (Di Matteo et al. 2005; Volonteri et al. 2003). When a merger occurs, the SMBHs within the two galaxies will also eventually merge, leading to a more massive final black hole (Haehnelt & Rees 1993; Kauffmann & Haehnelt 2000). But this increase in mass of the resulting SMBH is small compared to the mass gained through accretion. Models of the hierarchical evolution of quasars show that this scenario is consistent with the observed masses and luminosities of both young quasars at high redshifts and quiet galaxies with SMBHs seen today (Kauffmann & Haehnelt 2000). In this coevolution scheme, a galaxy and its SMBH remain quiet between mergers. During this time, the galaxy secularly evolves as dynamical movements cause structural changes to the galaxy and stellar formation and evolution modifies the stellar population and the chemical composition of the galaxy. After a time, it is likely that another galaxy will pass nearby and the two galaxies will gravitationally disrupt each other. The disruption causes a mass exchange between the two galaxies and triggers gas inflow, allowing material to be accreted onto the SMBHs. The rapidly falling gas releases radiation and causes the nuclei to become active. If the two interacting galaxies merge, the two black holes will gravitate to the new potential well and coalesce into a larger black hole. Both accretion and the merging of the two initial black holes cause the final black hole to gain mass, with most of the mass-gain dominated by accretion. Eventually, the AGN will emit enough radiation that it will push away the in-falling material, halting accretion and leading to a quiet galaxy once more. In this model of coevolution, all significant mass-gain for both the galaxy and the SMBH occurs

14 1. Introduction 9 during galaxy interaction events. This leads to proportional growth between the two objects and explains the observed M BH M Bulge correlations. 1.2 Low-Luminosity Galaxies Low-luminosity galaxies are key to understanding the coevolution of galaxies and SMBHs. These galaxies, which should contain intermediate-mass black holes [IMBHs] of M < 10 6 M (if the black hole - host galaxy scaling relations can be extrapolated), have not gained much mass through interactions since their formation. This makes these galaxies an ideal tool for studying galaxy - black hole formation and their early coevolution. For the most part, low-luminosity galaxies have evolved secularly throughout their lifetimes without contamination from other galaxies, making it easy to compare simulations of galaxy formation and evolution to these galaxies. Studying low-mass galaxies is also a good alternative to studying the actual ancestors of large present-day galaxies because they are nearby and easily observed, unlike their high-redshift counterparts. The supermassive black holes that reside at the centers of galaxies were formed through different processes than the stellar-mass black holes (M 3 15 M ) that exist throughout galaxies. Observations of quasars at high redshift reveal that at z 6, supermassive black holes of M 10 9 M already existed (Volonteri & Rees 2006). Even if a black hole created at the earliest possible moment in the Universe grows at the fastest possible rate, it is impossible for a stellar-mass black hole to reach such a large mass by z 6 (Volonteri 2010). To get a 10 9 M black hole at that time, the seed of the SMBH would have to be more massive than the largest possible stellar-mass black hole formed through the death of a present-day star. Therefore, the conditions under which black holes formed in the early Universe

15 1. Introduction 10 must have allowed for the formation of much larger black holes than those formed in the present era. Currently, there are three popular models of SMBH seed formation at high redshift that predict different seed masses. The first is the collapse of Population III stars, the first generation of stars that formed in the Universe (Madau & Rees 2001). Population III stars are formed out of clouds with zero metallicity, reducing the fragmentation of the cloud as it collapses and allowing some stars to have initial masses of M > 260 M (Bromm et al. 1999; Bond et al. 1984). The cores of these massive stars can reach temperatures so high that photodisintegration can occur while the star is still on the main sequence (Bond et al. 1984; Woosley & Weaver 1986). At this point in its lifecycle, the star has no mechanism to stop the collapse caused by the destruction of the heavier ions, so the star s core implodes into a massive black hole with M > 130 M (Fryer et al. 2001). Similarly, the second model suggests that gas clouds with zero metallicity can collapse directly into large black holes (e.g., Lodato & Natarajan 2006). Within the high-density, gaseous nuclear regions of early galaxies, inefficient cooling from the metal-poor gas decreases the likelihood of fragmentation and allows large concentrations of gas to build up. The collapse of this gas can form black hole seeds on the order of M 10 5 M (Lodato & Natarajan 2006). The third model allows for stars with some metals, though still well below the metallicity of the solar nebula. Through dynamical interactions, the gas at the core of a compact nuclear star cluster can become compressed enough to form a massive star that continues to grow through interactions with other stars in the cluster (e.g., Begelman & Rees 1978). Eventually this star will become massive enough that it will collapse into a black hole with mass M M (Omukai et al. 2008). The study of low-luminosity galaxies and their putative IMBHs can be used

16 1. Introduction 11 to help determine the validity of the various massive black hole seed formation mechanisms described above. Models of galaxy - black hole coevolution using these different seed formation scenarios have shown that the present-day black hole occupation fraction is most sensitive at the low-mass end (e.g., Volonteri et al. 2008; van Wassenhove et al. 2010). The occupation fraction describes the fraction of galaxies of a given mass that we expect to contain a supermassive black hole. Thus, an understanding of low-luminosity galaxies and their black holes could help to test the frequency of various formation scenarios. Volonteri et al. (2008) used a Monte Carlo merger tree to simulate the hierarchical evolution of galaxies containing each of the three seed models. Because the black holes in these models are formed through different mechanisms, they have differing formation efficiencies. The difference in efficiencies between the models results in the number of black holes formed through the most-efficient process to be an order of magnitude greater than the number of black holes formed through the least-efficient process (Volonteri et al. 2008). Comparison of the present-day M BH σ c relation predicted by four models with different formation efficiencies to the observationally determined relation shows that the predicted relations all fit the data at the high-mass end (Fig. 1.3). However, the simulated relations differ significantly for low-luminosity galaxies with low-mass black holes. In principle, data for objects that reside at the low end of these graphs will allow us to determine which formation scenario should be favored. Unfortunately, it is in this regime that our information is the most incomplete. The galaxy - black hole correlations discussed in Section 1.1 were derived using the most easily observed galaxies. These galaxies are usually large and very luminous compared to the majority of galaxies in the Universe, which are predominantly small and dim. Additionally, all of these galaxies either have prominent

17 4 RESULTS Detection of gravitational waves from seeds merging at the redshift of formation (Sesana et al. 2007) is probably one of the best ways to discriminate among formation mechanisms. On the other hand, the imprint of different formation scenarios can also be sought in observations at lower redshifts. The various seed formation scenarios based on the Lodato & Natarajan (2006, 2007) seed masses with Q c = 1.5, 2 and 3, and a fourth model based on lower mass Population III star seeds. The upper panel of Fig. 3 shows the fraction of galaxies that do not host any MBHs for different velocity dispersion bins. This shows that the fraction of galaxies without an MBH increases with decreasing halo masses at z = 0. A larger fraction of low-mass haloes are devoid of central BHs for lower seed formation efficiencies. Note that this is one of the key discriminants between our models and those seeded with Population III remnants. 1. Introduction 12 Figure 3. The M BH velocity dispersion (σ c ) relation at z = 0. Every circle represents the central MBH in a halo of given σ c. Observational data are marked Figure 1.3: Bottom Panels: M by their quoted error bars, both in σ c and in M BH (Tremaine et BH σ al. 2002). c relation at z = 0 for four different black hole Left-hand to right-hand panels: Q c = 1.5, Q c = 2, Q c = 3, Population III star seeds. Top panels: fraction seed of formation galaxies a given scenarios. velocity dispersion Each which circledorepresents not host a central ambh. simulated SMBH within a halo for a given σ c. Observational data is marked with error bars for both M BH and σ c. Top panels: Fraction of galaxies at a given velocity dispersion that do not host a black hole. Volonteri et al. (2008). C 2007 The Authors. Journal compilation C 2007 RAS, MNRAS 383, bulges or are elliptical galaxies, whereas small low-luminosity galaxies often have no bulge or are irregularly shaped. As we have seen, the predicted galaxy - black hole relations vary significantly in the low-mass regime depending upon the formation mechanisms considered. Thus, we are unable to simply extend these relations down to low-luminosity galaxies without obtaining observational data. With the advent of larger telescopes and improved observational techniques, we are now able to better observe fainter galaxies and explore their relationships with their central supermassive black holes in order to ultimately understand galaxy - black hole formation and coevolution. 1.3 Bulgeless High-Mass Galaxies In contrast to low-luminosity galaxies, large, luminous galaxies are believed to grow predominantly through mergers with other galaxies. These mergers not

18 1. Introduction 13 only increase the mass of a galaxy, but also alter its structure. Galactic mergers rearrange the material within a galaxy and cause bursts of star formation and ejection of some material into the intergalactic medium, usually leading to the formation of a bulge-dominated or elliptical galaxy (Kormendy & Kennicutt 2004). Thus, we would typically expect a large, massive galaxy to have a bulge or be an elliptical. Yet there are some massive galaxies that lack the expected bulge. Often these galaxies contain just a disk or have a disk with a pseudobulge - a less centrally concentrated bulge (Kormendy & Kennicutt 2004). Both of these latter galaxy morphological features are theorized to form as a galaxy secularly evolves. Pseudobulges are formed by nonaxissymmetric instabilities in the galaxy, such as a bar or spiral arms, that cause a build up of central mass (Kormendy 1993). These morphologies take a long time to form, so galaxies without bulges are not only secularly evolving, but have done so for a significant fraction of the Hubble time. As the Universe expands, the frequency of mergers will decrease until galaxies are left to secularly evolve for the majority of their lifetimes (Toomre 1977a; Conselice et al. 2003). Therefore, galaxies without classical bulges can be thought of as glimpses into the future of galactic morphology. Study of these galaxies can provide an important counterpoint to low-luminosity galaxies. By studying both ends of the spectrum - low-luminosity galaxies that have predominantly secularly evolved and high-mass bulgeless galaxies that have not experienced any major mergers events yet somehow are quite massive - we will be able to gain a more complete picture of the beginning and end stages of galactic evolution.

19 1. Introduction Previous Studies Black Holes in Low-Mass Galaxies The first low-luminosity galaxy suspected to contain a central intermediatemass black hole was the dwarf galaxy NGC 4395 (Filippenko & Sargent 1989). NGC 4395 is a bulgeless late-type spiral galaxy containing a broad emission-line Seyfert 1 active galactic nucleus with M BH M (Filippenko & Ho 2003; Peterson et al. 2005). Prior to this discovery, it was believed that there was a lower luminosity limit for galaxies containing AGN. It was also believed that AGN could only be present in galaxies with well developed bulges. In fact, this second belief was so strongly held that many of the subsequent papers about NGC 4395 sought to determine if the observed nucleus was actually an AGN powered by a SMBH or whether the observed emission features were associated with a less exotic source, such as a large burst of star formation (e.g., Filippenko et al. (1993)). Therefore, the discovery of nuclear activity in this galaxy demonstrated that AGN do not exist exclusively in bulges and, because of this, new relationships between black holes and their host galaxies need to be found that do not rely upon characteristics of the bulge. Since the discovery of NGC 4395, several other low-luminosity galaxies have been discovered to contain AGN, most notably POX 52 (Barth et al. 2004; Thornton et al. 2008). POX 52 also contains a Seyfert 1 IMBH, but, unlike NGC 4395, it is a dwarf elliptical galaxy with a Sèrsic index 1 of n = 3.6 ± 0.2 (Barth et al. 2004). This is a rather high Sèrsic index, similar to that expected for a large elliptical galaxy, indicating that essentially the entire galaxy is a bulge. POX 52 1 See Chapter 2 for an explanation of Sèrsic indices.

20 1. Introduction 15 is thus another example of a galaxy for which the standard relationships between host galaxy and black hole do not necessarily apply. The differences between low-luminosity galaxies and their larger counterparts have led to several surveys of low-luminosity galaxies intended to reconcile the two groups. To verify whether the current M BH M Bulge relation applies for low-mass galaxies, Greene & Ho (2004) collected a sample of 19 low-luminosity galaxies with AGN. Their intermediate-mass black holes were estimated to have masses in the range M, which were determined using the broad emission-line widths of the AGN. A Hubble Space Telescope [HST] study of the same objects by Greene et al. (2008) found that the currently accepted M BH M Bulge relation does not hold for galaxies with M BH < 10 6 M. They also confirmed that a central bulge is not necessary for the presence of an active black hole. However, this survey is limited in both size and selection bias. Because AGN broad line widths are needed to measure the masses of the black holes, only Seyfert 1 galaxies are included in the sample. Furthermore, the sample is too small to reliably suggest an alternative M BH M Galaxy scaling relationship for galaxies with intermediate-mass black holes. A more extensive survey of low-luminosity galaxies was conducted by Jiang et al. (2011). This survey analyzed 147 galaxies with intermediate-mass black holes to determine which host galaxy morphologies are most common for galaxies with small black holes. They found that not only can IMBHs exist in galaxies without bulges, but that it is actually common for them to do so. Indeed, a majority of the galaxies were disk-dominated with pseudobulges. Additionally, only 9% of the galaxies were observed to have companions. These two characteristics imply that these low-luminosity galaxies have undergone very few interactions with other galaxies throughout their lifetimes and are therefore close to the size

21 1. Introduction 16 they were at their creation. Unfortunately, like the survey done by Greene & Ho (2004), this survey only includes Seyfert 1 galaxies because of the need to use broad emission lines to measure the mass of the central black hole. Although there has been significant progress made in understanding the coevolution of black holes and their host galaxies in the low-mass regime, studies have focused almost exclusively on Seyfert 1 galaxies. In order to fully understand this coevolution, we must also include Seyfert 2 galaxies in our analysis Black Holes in Bulgeless Massive Galaxies Like their low-mass counterparts, AGN in bulgeless massive galaxies are often very hard to detect. Bulgeless galaxies are typically late-type spirals with large amounts of dust surrounding the nuclei. This dust obscures the characteristic optical emission lines necessary to identify AGN. Thus, there have been relatively few detections of AGN in bulgeless galaxies. To complicate matters, observations of M33, the most nearby example of a bulgeless galaxy, suggest that this galaxy does not have an AGN. These two facts have led astronomers to question the prevalence of AGN in bulgeless, massive galaxies. The lack of detections could either imply that AGN are present in these galaxies, but are faint and heavily obscured, or that AGN do not often form and grow in the predecessors of presentday bulgeless galaxies. One of the first definitive measurements of an AGN in such a galaxy was of the supermassive black hole in NGC 3621 (Satyapal et al. 2007). This discovery demonstrated that AGN can not only form, but can also grow through accretion in bulgeless galaxies. The AGN has a bolometric luminosity two orders of magnitude larger than that of NGC 4395, indicating that it is far more massive than the AGN

22 1. Introduction 17 within NGC But although this discovery helped prove that AGN do reside in some bulgeless galaxies, it did not address the occupation fraction of AGN in these galaxies. Satyapal et al. (2009) more recently conducted a systematic mid-infrared search for weak or obscured AGN in a sample of late-type (Sd/Sdm) galaxies. Out of 18 surveyed galaxies, this search only uncovered one additional AGN, located in NGC Again this AGN has a bolometric luminosity several orders of magnitude greater than that of NGC 4395, but the discovery of only one AGN in 18 surveyed galaxies suggested that perhaps AGN are uncommon in bulgeless galaxies. In general, searches for obscured AGN in bulgeless, late-type galaxies have been more successful at mid-infrared wavelengths because of the decreased dust extinction at these wavelengths compared to optical wavelengths. Several other AGN in bulgeless host galaxies have been discovered using infrared surveys and X- ray followup observations (e.g., Ghosh et al. 2008; Desroches & Ho 2009; McAlpine et al. 2011). However, a majority of the AGN discovered in these searches are within low-mass galaxies. These galaxies demonstrate that low-mass bulgeless galaxies can contain AGN, but the surveys fail to address whether this correlation extends to the high-mass regime, where one would typically expect galaxies to grow to high masses through mergers and subsequently develop bulges. Without major merger events to increase the size of the galaxy and black hole, massive bulgeless galaxies with AGN must have either formed at close to their current masses or have grown through other processes that do not lead to the formation of a classical bulge. Therefore, there is a need to discover AGN in high-mass bulgeless galaxies to further characterize the black hole occupation fraction for this class of galaxies and to provide evidence that high-mass galaxies can be created through processes other than hierarchical evolution.

23 1. Introduction Extreme Black Hole Hosts In this paper I will explore the relationship between host galaxy and SMBH for two distinct types of Seyfert 2 galaxies: low-mass galaxies and high-mass bulgeless galaxies. For low-mass galaxies, I will investigate NGC 4117, a lowluminosity Seyfert 2 with an IMBH. Because of its proximity to our Galaxy, it is an ideal candidate for exploring the galaxy - black hole relationship for this type of low-luminosity galaxy. NGC 4117 has been included in several surveys of galaxies but has never been studied in detail. As contrast, I will also analyze a small sample of more massive Seyfert 2 galaxies that visually appear to be bulgeless. All of these galaxies are previously unidentified AGN so our analysis will both add to the collection of known bulgeless galaxies, thereby increasing the AGN occupation fraction of bulgeless galaxies, and provide further evidence that massive black holes in bulgeless galaxies can be created through processes other than hierarchical evolution. For both types of galaxies, I will present two-dimensional surface brightness models of the galaxies, in order to determine whether they are bulgeless. I will also analyze the nuclear emission-line spectra of the AGN in the high-mass sample to verify that these galaxies contain AGN. In Chapter 2, I will discuss the surface brightness profile fitting techniques we used. In Chapters 3 and 4, I will present the results for NGC 4117 and the sample of high-mass galaxies, respectively. Then I will discuss the implications of these results and my conclusions in Chapters 5 and 6. This study will help us gain a better understanding of the relationship between the central black hole and host galaxy for these two atypical galaxy classes. Ultimately, this will be another step towards extending our knowledge of black hole - galaxy coevolution to include low- and high-mass bulgeless galaxies.

24 Chapter 2 Surface Brightness Profiles The overall morphology of a galaxy can be determined through modeling its surface brightness profile. Galaxies can often be decomposed into several components, such as disks and bulges. These components contain different distributions of light that vary diversely as a function of distance from a galaxy s photometric center. When combined, these components can be used to accurately describe the overall surface brightness of a galaxy, which to first order can represent its overall stellar mass distribution. Ideally, different surface brightness components represent different physical components of a galaxy that are distinct in terms of stellar populations, stellar kinematics, and/or formation histories. As discussed in Section 1.3, both galactic mergers and secular evolution can affect the morphology of galaxies. Mergers cause the material within a galaxy to rearrange itself into a centrally concentrated distribution best described by a bulge-dominated disk or an elliptical. On the other hand, secular evolution is theorized to yield pseudobulges or disks that lack additional central structure. Therefore, by determining the components of NGC 4117 and a small sample of high-mass bulgeless galaxies, we will be able to better understand their past evolutionary histories. If they are bulge-dominated, we can guess that they have recently undergone major mergers, leading to the growth of both the galaxy and its supermassive black hole. But if (a) the central components of the galaxies are

25 2. Surface Brightness Profiles 20 pseudobulges, or (b) they simply do not have bulges, then it is likely the galaxies have been isolated for a significant period of time and secular evolution has been dominant. If this is the case, these galaxies may provide useful insight into the secular coevolution of galaxies and their central black holes. The first galaxies to have their surface brightness profiles modeled were large elliptical galaxies. The profiles of these galaxies were described by de Vaucouleurs (1948) with the following formula: { [ ( ) 1/4 r Σ(r) = Σ e exp ]}, (2.1) r e where r e is the effective radius, Σ e is the surface brightness at r e, and 7.67 is a constant chosen such that if the galaxy were circularly symmetric and if the formula were valid for all radii, then half the light of the galaxy would be within r e (Binney & Merrifield 1998). This profile, now know as the de Vaucouleurs profile, accurately models the majority of large elliptical galaxies. However, many are more complex and require multiple de Vaucouleurs profiles, or other types of profiles with more free parameters, to describe them. The de Vaucouleurs profile can also be used to describe the central bulges of many early-type spiral galaxies. These components have the same distribution of light as the larger ellipticals for which the profile was developed. However, the profile cannot be used to describe the light distributions of the disks that make up the outer portions of most late-type galaxies. These disks fade with radius much more than the de Vaucouleurs profile. Thus, a variation on the de Vaucouleurs profile, known as an exponential profile, was created to describe galactic disks.

26 2. Surface Brightness Profiles 21 This profile is typically written in the form: ) Σ(r) = Σ 0 exp ( rrs, (2.2) where Σ 0 is the central surface brightness and r s is the scale length of the disk - the radius at which the surface brightness decreases to 1/e of the central surface brightness. These two surface brightness profiles were later generalized into one unifying formula by Sèrsic (1968). This power-law profile, now known as the Sèrsic profile, takes the form: [ ( ( ) 1/n r Σ(r) = Σ e exp κ 1)], (2.3) r e where r e is the effective radius, Σ e is the surface brightness at r e, n is the Sèrsic index, and κ is a dependent variable of n chosen such that the region within r e contains half of the profile s total light. Depending upon the Sèrsic index, this profile describes a variety of radially symmetric light distributions. For n = 1, the Sèrsic profile reduces to an exponential profile, whereas for n = 4 it reduces to the de Vaucouleurs profile. Sèrsic indices greater than n = 4 are used occasionally, but they typically only fit very luminous ellipticals, such as cd ellipticals (Binney & Merrifield 1998). Most galaxies decrease in surface brightness as one moves to larger radii, but the outer edges of cd galaxies actually increase in brightness. cd ellipticals are very rare, so Sèrsic indices of n > 4 are not typically expected. More interestingly, a variety of galaxy components have been best modeled by Sèrsic profiles with intermediate values of n. Kormendy & Kennicutt (2004) found that pseudobulges are best fit by Sèrsic profiles with n = 1 2. See Figure 2.1 for a comparison of components with different Sèrsic indices (from Peng et al.

27 2. Surface Brightness Profiles 22 No. 6, 2010 DETAILED DECOMPOSITION OF GALAXY IMAGES. II. Figure 3. Sérsic profile where r e and Σ e are held fixed. Note that the larger the Sérsic index value n, the steeper the central core, and more extended the outer wing. A low n has a flatter core and a more sharply truncated wing. Large Sérsic index components are very sensitive to uncertainties in the sky background level determination because of the extended wings. (A color version of this figure is available in the online journal.) Figure 2.1: Models of Sèrsic profiles for a range of Sèrsic indices from Peng et al r e and Σ e are held fixed for each model. Note that the larger the Sèrsic index, the steeper the central core and the more extended the outer wing. Sèrsic profiles with large n are more sensitive to uncertainties in the sky background level determination because of these extended wings. The exponential disk profile. The exponential profile has some historical significance, so Galfit is explicit about calling this 2010). Overall, profile the Sèrsic an exponential profile disk, is a good eventhough alternative anobject to that the has de an Vaucouleurs and exponential profile needs not be a classical disk. Historically, an exponential disk has a scale length r s,whichisnottobe exponential profiles confused because with the it effective allows for radius more r e used complicated in the Sérsicintermediate profile. structures For situations where one is not trying to fit a classical disk, to be identifieditwithin would be a less galaxy s confusing overall nomenclaturewise morphology. to use the Sérsic function with n = 1, and quote the effective radius r e.but More recently, because additional the exponential surfacedisk brightness profile is profiles a special have case of been the created to characterize a variety between of other r e and rlight s,givenby distributions. Of particular interest to Sérsic function for n = 1(seeFigure3), there is a relationship surface The Gaussian profile. The Gaussian profile is another special where β is the outer case ofpower the Sérsic lawfunction slope, with γ isn the = 0.5(seeFigure3), inner slope, rbut b ishere the radius at which the size parameter is the FWHM instead of r e.thefunctional form is ( r 2 ) Σ(r) = Σ 0 exp, (10) 2σ 2 and the total flux is given by Figure 4. Modified Ferrer profile. The black r r out = 100, α = 0.5, β = 2, and Σ 0 = reference only in the α parameter, as indicated the green curves differ from the reference only i by the green numbers. (A color version of this figure is available in the where FWHM = 2.355σ.Thesixfree are x 0, y 0, m tot, FWHM, q, andθ P.A.. The modified Ferrer profile. The F Binney & Tremaine 1987) hasanear truncation. The sharpness of the trunc parameter α, whereasthecentralslo parameter β. Becauseoftheflatco behavior, historically it is often used lenses. The profile, Σ(r) = Σ 0 ( 1 (r/rou is only defined within r r out,beyon r e = 1.678r s (for n = 1only). (7) a value of 0. The eight free parameter brightness profiles of galaxies with AGN is the Nuker profile (Lauer et al. 1995), x 0, y 0,centralsurfacebrightness,r out, The functional form of the exponential profile is It is worth mentioning that a Sérsi which is used to effectively fit the centers of galaxies, especially those ) n<0.5 containing hassimilarprofileshapes;thu Σ(r) = Σ 0 exp ( rrs of the Ferrer function. a bright point source (e.g., an AGN). This function, is a double (8) power-law that The empirical (modified) King profi breaks from oneand slope the total another flux is given atbya given radius. It has the form profile (Figure 5)isoftenusedtofitth clusters. It has the following form (Els F tot = 2πrs 2 Σ 0q/R(C 0 ; m). (9) ( ) γ [ ( ) α ] γ β The six free parameters of the profile are x 0, y 0, m tot, r s, θ P.A., Σ(r) = Σ 0 [1 Σ(r) = Σ b 2 β γ r r α α 1 +, (2.4) and q. r b r b [ 1 (1 + (r t /r c ) 2 ) 1/α 1 (1 + (r/r c ) 2 ) 1/α ( The standard empirical King profile h index α = 2. In Galfit, α can be a free the flux parameter to fit is the centra expressed in mag arcsec 2 (see Equat parameters are the core radius (r c )and in addition to the geometrical paramete radius, the function is set to 0. Thus, the

28 2. Surface Brightness Profiles 23 the slope transitions from β to γ, Σ b is the surface brightness at r b, and α controls the sharpness of the transition. This profile allows for an abrupt change from one light distribution to another, which is sometimes necessary when the nuclear region of the galaxy is completely dominated by light from an AGN. Unfortunately, this equation has a large number of free parameters, which makes it difficult to use in practice. Thus, despite its potential usefulness, it is not frequently employed in surface brightness modelling. 2.1 GALFIT In order to decompose our galaxies into their components, we used the image analysis algorithm GALFIT (Peng et al. 2002). This program is a twodimensional, nonlinear least-squares fitting algorithm that uses the Levenberg- Marquardt technique to find the optimum solution to a fit. GALFIT attempts to model an input image by adjusting user-specified surface brightness profiles to match the brightness levels throughout the image. The program determines the goodness of fit by calculating χ 2 for a potential model and then computing how to adjust the model parameters to improve χ 2. Once a model that locally minimizes χ 2 is found, GALFIT outputs this model s parameters, along with an image of the model and a residual image showing the difference between the model and the input image. Fitting the two-dimensional profile of a galaxy is preferable to fitting its azimuthally averaged profile because it can provide important flexibility in the presence of bars or other complex substructures. Additionally, two-dimensional fitting allows components of different position angles to be fitted simultaneously. GALFIT has been successfully used to characterize the morphologies of galaxies in a variety of studies (e.g., Ho & Peng 2001; Bell et al. 2006; Kim et al.

29 2. Surface Brightness Profiles ). Here we use the latest version of this program, GALFIT 3.0 (Peng et al. 2010) 1. This version of GALFIT primarily adds the ability to fit lopsided and spiral components. To run, GALFIT requires the user to specify which surface brightness profiles should be used to fit the galaxy image. All of the profiles discussed above are available, along with a variety of more complex profiles for fitting substructures and irregularly shaped features. There are also profiles available for modeling a single point source (such as an unobscured AGN) and for modeling the background sky level. The user must give initial guesses for the parameters of the profiles that are included in the model. These guesses are then used as the initial parameters from which GALFIT attempts to minimize χ 2 and generate a model of the galaxy. The user can also specify which parameters should be kept fixed and which are given to the algorithm as free parameters. This provides a useful constraint that is used in some of the fitting strategies discussed in Section 2.2. In addition to the initial surface brightness profiles, the user must also supply information about the input image that is being fit. Most importantly, GALFIT requires a point spread function [PSF] to convolve with the model components during fitting. The PSF of an image describes how a single point of light will appear in the image. Obtaining a PSF is particularly important when determining the surface brightness profile of an AGN because, if the AGN is unobscured, it will appear as a point source in the image. Failure to accurately model this point source when determining the surface brightness profile of a galaxy could result in a model that does not allow the light from the point source to be distributed in a realistic manner. In some cases it is beneficial to provide a PSF that is 1 More information on GALFIT can be found at the GALFIT home page online:

30 2. Surface Brightness Profiles 25 more finely sampled (i.e., has more pixels per arcsecond) than the image itself. In this case, the degree of oversampling can be specified and taken into account when GALFIT convolves the PSF. The user may also provide a sigma image to help GALFIT make a more physically accurate fit. Each pixel in the sigma image represents one standard deviation of the counts from a mean sky level for the corresponding pixel in the original image. The sigma image is used to give relative weights to the pixels during fitting and helps GALFIT differentiate noise from detections. If none is provided, GALFIT will calculate one automatically using the CCD gain specified in the image header. As part of calculating the sigma image, GALFIT will estimate the sky level in the image because the sky level is a potentially large source of noise. Therefore, if there is reason to suspect that GALFIT has incorrectly measured the sky level, independently generating a sigma image is recommended. A bad pixel mask can also be included if the user would like to exclude some pixels from the fitting procedure. This is typically used when there is significant dust in the galaxy or there are nearby stars that could potentially mislead the fitting algorithm. Galaxies close in projection can also be excluded from the fit, but it is often preferable to simultaneously fit these along with the main galaxy since it is difficult to determine where contamination from a foreground or background object ends. Unfortunately, like any program, GALFIT has its limitations. The most significant limitation is GALFIT s tendency to produce models with unrealistic parameters. As mentioned above, the algorithm seeks to minimize χ 2 when generating a fit. If the guessed initial parameters of the components are too far from the realistic solution, the program will often find local minima in χ 2 that do not accurately represent the galaxy s physical surface brightness components. If a solution

31 2. Surface Brightness Profiles 26 parameter is egregiously small or large, GALFIT will alert the user, but in general the program cannot determine whether or not its best fit is physically reasonable. Therefore, when fitting a galaxy, one must try to strike a balance between finding the fit with the lowest χ 2 and finding a fit with realistic parameters. One of the largest sources of uncertainty in the generated models is the uncertainty of the sky level. If the sky level used when fitting the surface brightness profile is incorrect, GALFIT may incorrectly determine the effective radii of the extended components in the galaxy. Jiang et al. (2011) assessed the effect of this uncertainty on their models of the host galaxies of intermediate-mass black holes. Since we are using GALFIT for a similar purpose, their findings can also be applied to the errors in our own models. To estimate the uncertainties in the sky level, Jiang et al. (2011) kept all of the parameters of their best-fitting model fixed except for the radii and integrated magnitudes of the components. They then had GALFIT recalculate the best-fitting model for three different sky levels: the sky level determined by GALFIT, and the sky levels one standard deviation above and below their measured value. Then they compared how much the radii and magnitudes of the galaxy components differed from their original model. The typical uncertainties they found were differences of 0.1 in the integrated magnitude of the components and 1 kpc in the effective radius of the outermost component. Many of the models tested had effective radii of only a few kpc, showing that the sky level used when fitting a model to a galaxy image can dramatically affect the effective radius of the outer components. Thus, it is very important to determine accurately the sky level before attempting to model the light distribution of a galaxy. Another large source of uncertainty is the uncertainty of the PSF used to convolve the model surface brightness profiles. Errors in the determination of the

32 2. Surface Brightness Profiles 27 PSF can lead to incorrect total integrated magnitudes. To test the extent of this uncertainty, Jiang et al. (2011) fit a variety of PSFs to their best-fitting models to explore how these PSFs affected the models. To create their best-fitting models, they used a PSF model generated by the Tiny Tim Software (Krist et al. 2010). This software theoretically generates model PSFs for images taken by Hubble Space Telescope (See Chapter 3 for more discussion of Tiny Tim). After generating their best-fitting models, they then created new models with two alternative PSFs: a model PSF chosen from the HST PSF library and an observationally determined PSF. Again they kept all of the parameters of their best-fitting models fixed except for the radii and central/effective magnitudes of the components. The differences in the resulting models yielded uncertainties in the magnitudes of the point sources of 1. This measurement of uncertainty is much larger than that found by Peng et al. (2002) for their models of large Seyfert 1 galaxies with highluminosity AGN. Peng et al. (2002) estimated that GALFIT was able to extract nuclear point source magnitudes with an accuracy of ±.2.3 magnitudes from these galaxies. This shows that the uncertainty in the magnitude of a nuclear point source is mostly due to the uncertainty in the PSF, and not the inherent uncertainties in the fitting algorithm. For this study we are primarily interested in modeling the inner regions of our galaxies. If our models include point sources, it will be particularly important to create a PSF that is as accurate as possible so that we can reduce the uncertainties of the components in these inner regions. 2.2 Fitting Strategy Galaxies, like all things in nature, are hopelessly complex. To describe galaxies, the models used to fit their surface brightness profiles must therefore be equally

33 2. Surface Brightness Profiles 28 complicated. Because of this complexity, attempting to fit a galaxy with a model with a large number of free parameters is unlikely to succeed. Thus, a more systematic fitting strategy is needed to constrain the number of free parameters to a manageable amount. Previous studies of galaxy morphology done with GALFIT have used a variety of different fitting strategies. The most systematic approach is that described by Jiang et al. (2011). First, they visually examined each galaxy image to determine if there was an obvious bar, which was then included in the model if present. Then they generally fitted each galaxy with a point source (to represent a type 1 AGN) and a single Sèrsic profile. Next they visually inspected the residuals of each model and added an additional Sèrsic profile to the model if there were large residuals. They continued adding components to the model until no large features were observed in the one-dimensional residuals and the reduced χ 2 ν was 1 or lower. This fitting technique is similar to the techniques employed by most GALFIT users. What makes this technique more unique is that Jiang et al. (2011) only used discrete values of n when fitting additional Sèrsic components onto the initial point source and Sèrsic model. When they added a second component, they fixed n = 1, 2, 3, 4, respectively, and included the index that resulted in the best fit (lowest value of χ 2 ) in their final model. This strategy of using discrete values of n has been used by several other groups (e.g., Benson et al. 2007; Kim et al. 2007; Simmons & Urry 2008) and is useful for generally determining the morphology of a galaxy. However, confining the Sèrsic index of a component to a discrete set of values can affect the realism of the fit. Therefore, for an in-depth study using high-quality data of well-resolved galaxies such as ours, it would be unwise to restrict n when determining our final model. Greene et al. (2008) used an even more restricted version of this approach to fit

34 2. Surface Brightness Profiles 29 several low-luminosity galaxies. First they divided their galaxies into two groups: those that looked like blobs, and those that appeared to be disk-dominated. Like Jiang et al. (2011), they initially fitted each blob galaxy with a point source and a Sèrsic profile. They confined this Sèrsic profile to indices of n = 1, 2, 3, 4, in order to determine which of the four resulting models best described the galaxy. However, they did not attempt to fit any secondary components to the blob galaxies. For the disk galaxies, Greene et al. (2008) initially fitted a PSF and an exponential disk to each galaxy. If the galaxy visually appeared to have a bar, they would then add a bar component to the model. Finally, they added a second Sèrsic component with discrete values of n if the residuals showed an under-abundance of light in the inner regions of the galaxy. To model the galaxies in this study, I adopted a variation of these techniques. I first masked out any regions affected by visible dust, foreground stars, or background galaxies that significantly overlapped with the inner regions of the galaxy. I also visually inspected the galaxies for bars, rings, or other features. If present, bars can be effectively modeled by Gaussian profiles (Sèrsic profiles with indices of n = 0.5, Freeman 1966; de Jong 1966). Next I determined the average sky level of the image by measuring the average number of ADU counts per pixel for several apparently blank sky regions in the image. For my initial fit of each galaxy, I fixed a flat background sky model with the average value I had previously calculated, combined with a Sèrsic profile with an initial index of n = 2.5 on top. I did not fit a point source component at this stage because, unlike the galaxies fit by Jiang et al. (2011) and Greene et al. (2008), all of our objects are Seyfert 2 galaxies and may not have unobscured AGN. I fit this single Sèrsic component twice, once for only the inner regions of the galaxy to allow GALFIT to determine where the component was centered, and then again for the entire galaxy to determine

35 2. Surface Brightness Profiles 30 the other model parameters (e.g., r e and n). If the residuals of this single Sèrsic model were noticeably negative or positive for a large portion of the inner galaxy, I added a second Sèrsic component centered at the same location as the first. This Sèrsic component also had an initial index of n = 2.5, but I allowed all the parameters in this component to vary freely, while keeping the first Sèrsic component fitted by GALFIT fixed. This allowed GALFIT to place constraints on this new component without being overwhelmed by an excess of free components. If the χ 2 of the fit was substantially improved by the addition of a second component then I interpreted this to mean that the galaxy was better modeled by two Sèrsic profiles rather than one. Ideally, to test the statistical significance of a change in χ 2, I would perform an F-test. However, systematic errors in our fits (due to spiral arms and other asymmetric features that could not be fitted) caused many of the models to have χ 2 ν >> 1 (see Section for details). Large χ 2 ν values can decrease the validity of F-tests, thereby making these tests unhelpful in determining the significance of changes in χ 2 ν for our models. To truly test the significance of changes in our models in the future, we will need to devise a more statistically robust means of judging changes in χ 2. Unfortunately, most studies using GALFIT do not report any statistical tests used to judge the appropriateness of their models or the uncertainties in their model parameters. Therefore, any test we devise has the potential to make our models far more robust than models in previous studies. After I fit a second component, I refit both components using the new parameters determined by GALFIT but allowing all parameters in both components to vary freely. This allowed GALFIT to converge upon a better fit for both components. For all galaxies, I also attempted to fit a third Sèrsic profile with n = 2.5, along with the first two components. If GALFIT returned a model with

36 2. Surface Brightness Profiles 31 one or more obviously physically unrealistic parameters or if χ 2 was not noticeably improved, I interpreted this as an indication that the current combination of components was not feasible for that galaxy image. This was my indication that I had reached the maximum number of components that could be realistically included to describe the galaxy. Once I reached this point, I designated the model with the largest number of realistic components (i.e., all parameters are realistic values) and the lowest χ 2 as one possible final model. To determine if the galaxy contained an unobscured point source at its center, I added a point source component to the final model and let all components vary during the fit. By comparing the χ 2 and residual map of this model to the potential final model without a point source, I was able to determine which model resulted in a better fit to the galaxy image and thus assess whether a point source was actually visible within the galaxy. As an alternative to this fitting method, I also tried to fit each galaxy with two Sèrsics simultaneously with suggested indices of n 1 = 1 and n 2 = 4 and effective radii such that this proposed model effectively described a disk and central bulge morphology. Because the fitting algorithm seeks out local minima in χ 2 to determine an ideal model, these different starting parameters have the potential to lead to an alternative final model. After GALFIT had generated a model from this fitting technique, I once again refit with a point source component added to this final model to determine the likelihood of an unobscured point source. Once I had generated all of these potential final models, my goal was to determine which model best fit the data (had the lowest χ 2 value and the most featureless residual map), while still producing a realistic model with plausible parameters. The reported morphologies of all of the galaxies below were determined from the models that best fit these criteria.

37 Chapter 3 NGC 4117 NGC 4117 has been a known Seyfert 2 galaxy for decades. It was first tentatively identified as one by Huchra et al. (1982) as part of the CfA Redshift Survey. Since then it has been included in a variety of AGN surveys, primarily because of its X-ray properties and / or the fact that it has a companion galaxy (e.g., Dahari 1984; Stephens 1989; Moran et al. 2001; Cardamone et al. 2007). However, it has never been independently studied. We chose to focus our study of low-luminosity AGN on NGC 4117 because it is relatively nearby (d = 18.5 Mpc) and contains a type 2 AGN. Its proximity enables us to easily obtain detailed images of its central regions, while, as a Seyfert 2 galaxy, it has no bright nuclear point source that could be difficult to separate from a compact bulge-like component. Both of these aspects of the galaxy will allow us to model its central regions in detail. Our analysis will also serve as an ideal counterpart to studies of NGC 4395 and POX 52, two low-luminosity Seyfert 1 galaxies without the canonical bulge-disk morphology. Spectroscopy of NGC 4117 has revealed that it is a low-mass, low-luminosity galaxy. It has an integrated absolute magnitude of M g = and a total stellar mass of M = M (Moran et al. 2012, in preparation). Unfortunately, it is difficult to measure the black hole masses of Seyfert 2 galaxies because they lack the broad emission lines typically used to estimate SMBH masses. Thus, Seyfert 2

38 3. NGC galaxies like NGC 4117 have largely been excluded from studies of low-luminosity AGN host galaxy morphology. By studying this Seyfert 2 galaxy, we will not only extend the conclusions of these studies to a lower mass regime, but also test these relationships for Seyfert 2 galaxies. Visually, NGC 4117 has been classified as an S0 galaxy (de Vaucouleurs et al. 1991). Figure 3.1 shows an optical image of the galaxy from the Sloan Digital Sky Survey, Data Release 7 [SDSS DR7]. Nelson & Whittle (1995) measured its stellar velocity dispersion to be σ = 95 ± 15 km s 1 from its Mg b nuclear absorption lines. This is much smaller than the average stellar velocity dispersion of AGN in their sample, confirming that NGC 4117 is a low-mass galaxy. Radio observations of NGC 4117 reveal that it is a fairly weak radio emitter, particularly at 3.6 cm (Nagar et al. 1999). However, radio data does show that the radio emission peak coincides spatially with the photometric center of the galaxy, confirming that the AGN is the source of the radio emission (Ulvestad & Wilson 1989). NGC 4117 is also a companion of the larger galaxy NGC Studies of the relationship between these two galaxies have determined that although NGC 4111 may have played a role in NGC 4117 s evolution in the past, the two have not recently interacted (Dahari 1985). Thus, NGC 4117 is still a good candidate for exploring the secular evolution of low-luminosity galaxies. Finally, optical images of NGC 4117 show a large amount of dust around the nucleus (see Figure 3.3). This dust is a particularly noteworthy feature for our study because it has the potential to mislead GALFIT and produce an unrealistic surface brightness decomposition. Several previous studies of galaxy morphologies have included surface brightness decomposition of NGC Capetti & Balmaverde (2007) analyzed the surface brightness profile of NGC 4117 as part of a study aimed at exploring the galaxy - black hole connection for early-type galaxies containing type 2 nuclei.

39 3. NGC Figure 3.1: Multi-wavelength image of NGC 4117 from the Sloan Digital Sky Survey, Data Release 7. At the bottom left is NGC 4118, a background galaxy. The image is oriented with north upward. Image size: The image of NGC 4117 used in this study was taken in the infrared with the NICMOS camera on Hubble Space Telescope using the filter F160W. Capetti & Balmaverde (2007) were primarily interested in determining whether the degree of central light concentration was correlated to the amount of radio emission observed in each galaxy. Thus, they only determined whether each galaxy s surface brightness profile was best described by a Sèrsic profile or a Nuker profile. To do this, they measured a one-dimensional surface brightness profile for each galaxy by fitting each image with elliptical isophotes and determined which model best fit the galaxy s profile. With this technique they determined that NGC 4117 was most accurately modeled by a Nuker profile with α = 1.31, β = 1.50, γ = 0.53, r b = 49 pc, and µ b = ergs s 1 cm 2 Å 1. Fisher & Drory (2010) also analyzed the surface brightness profile of NGC 4117, but obtained a different result. The goal of their study was to explore the galaxy - bulge scaling relationship for galaxies with pseudobulges and classical bulges. Like Capetti & Balmaverde (2007), Fisher & Drory (2010) also used

40 3. NGC infrared data. To model the surface brightness profile of each galaxy, they combined the profile measured from Spitzer images taken with IRAC 1 centered at 3.6 µm and the profile measured from NICMOS with F160W. They fitted each galaxy with a one-dimensional disk and Sèrsic profile using the isophote fitting routine by Bender & Moellenhoff (1987). For NGC 4117, they determined that the galaxy was best fit by a n = 1 disk and a pseudobulge with n = 1.36 ± 0.47 and r e = 389 ± 2 pc. From these two previous studies of NGC 4117 s morphology, one can see that depending upon the fitting algorithm used, the fitting technique, and the image quality, it is possible to produce a variety of different final surface brightness decompositions. Both of these studies chose different fitting techniques based upon the ultimate goals of their study. Therefore, as we proceed with fitting the surface brightness profile of NGC 4117, it is important to attempt to remain unbiased by the results that we may hope to produce. Additionally, both of these studies used different instruments and filters than our study. Galaxies appear different at different wavelengths, so it will be interesting to compare our results with those of these previous studies. In contrast, the studies by Greene et al. (2008) and Jiang et al. (2011), described in Section 1.4.1, both use similar instruments, fitting algorithms, and fitting techniques to our own study. They also have a similar scientific goal; to explore the nuclear morphologies of low-luminosity AGN. But their studies do not include NGC Thus, it will be particularly interesting to examine whether our conclusions for this Seyfert 2 galaxy mirror their results for Seyfert 1 galaxies.

41 3. NGC Data Acquisition and Processing In order to accurately create a surface brightness profile that is sensitive to smaller structures in the galaxy, we needed to acquire a fairly high-resolution image of NGC The image we used was taken with the Wide Field Planetary Camera 2 [WFPC2] on HST during cycle 9, and was obtained from the Barbara A. Mikulski Archive for Space Telescopes 1. WFPC2 is made of four CCDs, arranged to form one continuous image (see Figure 3.2). The central CCD is taken with the Planetary Camera [PC1], a small high-resolution camera with a focal ratio of 28.3, a plate scale of /pixel, and a field size of Around PC1 are three lower resolution, larger cameras, known as Wide Field Cameras 2-4 [WF2-4]. These cameras each have a focal ratio of 12.9, a plate scale of 0.1/pixel, and a field size of Typically, extended objects are centered on PC1, with the less interesting portions of the objects allowed to extend onto the WFCs. Because of this, emphasis is placed on focusing the Planetary Camera, leaving the wide field images all slightly out of focus. Additionally, the pixel scale of the images change towards the edges of the fields, especially for the wide field CCDs. This can make analysis of objects on the image edges difficult. We obtained two images of NGC 4117 taken successively with WFPC2. The two images had exposure times of 160 seconds and 400 seconds. Both centered the galaxy on the Planetary Camera CCD [PC1] and used the F606W filter (λ c = 5907 Å, λ = 2500 Å). However, the galaxy is so nearby that it fills PC1 and extends onto the WFCs. The images were processed using the HST data pipeline, which calibrates the images to remove biases and known hot pixels, and adjusts for factors such as the movement of the spacecraft. 1 archive.stsci.edu

42 3. NGC Figure 3.2: CCD arrangement of WFPC2. To reduce cosmic rays, we combined the two images using MultiDrizzle 2, a software package in PyRAF designed by STScI specifically for use with HST images. This program identifies and removes cosmic rays from several input images of the same target by comparing each pixel value with the median pixel value from the combined images and then replacing any bad pixels with the average of the corresponding pixel values in the input images. For WFPC2 images, MultiDrizzle also stitches the four images taken by PC1 and WF2-4 together and adjusts them so that all four images have the same plate scale as the WF images (0.1 / pixel). This ensures that the galaxy is the same scale throughout the combined image. Figure 3.3 shows the final image used to analyze the surface brightness profile of NGC PyRAF and MultiDrizzle are products of the Space Telescope Science Institute, which is operated by AURA for NASA.

43 3. NGC Figure 3.3: Top: Image of NGC 4117 taken with WFPC2 on HST. Image size: Bottom: Detail of the inner region of the galaxy. Notable features include several dust lanes and two background galaxies. Image size =

44 3. NGC PSF In Chapter 2, I discussed the necessity of using an accurate point spread function when modeling the surface brightness profile of a galaxy. Failure to do so can result in a model surface brightness profile with incorrect integrated magnitudes or effective radii. The ideal way to determine the PSF of an image is to find several unsaturated point sources (e.g., stars) in the image and use the combined shape of these point sources as the PSF. Unfortunately, the PSF in WFPC2 images is spatially unstable; it varies significantly between the different CCDs and even across each individual CCD. This means that if we wish to obtain an accurate PSF for one particular location in the image, we must determine the PSF using information from only that location; we cannot use stars located elsewhere in the image as a proxy. Furthermore, GALFIT only allows one PSF to be specified per image. Thus, we must chose one location on the image for which it is the most important to have an accurate PSF and measure the PSF at only that location. Although GALFIT uses the PSF to convolve surface brightness models throughout the galaxy, we are most interested in the fairly large-scale structure around the nucleus. We are also interested in determining whether the galaxy contains an unobscured point source. Both of these goals dictate that we create a PSF that is most accurate at the center of the galaxy. Unfortunately, this portion of PC1 is entirely covered by the galaxy, leaving no stars with which to make the PSF. In this circumstance, the best method of generating a PSF is through theoretical modeling. We used TinyTim, a software package created by STScI that is specifically designed to create a PSF for most HST instruments (Krist et al. 2010). Given a filter and chip location, TinyTim can create a model PSF that accounts for the spatial and spectral variations of the wavefront, as well as the effects of

45 3. NGC charge diffusion on the CCD. In this case, we created a PSF for the optical center of the galaxy. We also oversampled the PSF by a factor of four, in order to ensure that the PSF was Nyquist sampled (i.e., the full width half maximum of the PSF spaned two or more pixels). 3.3 Surface Brightness Profile Fitting To find a surface brightness model that accurately described the light distribution in our image of NGC 4117, I followed the fitting procedure described in Section 2.2. As a first step, I created a mask of bad pixels that included the blank areas surrounding the four CCDs, the excessively noisy areas at the edges of WF2 and 4, and any visible dust around the center of the galaxy. Next I visually determined that the galaxy does not contain a bar. This conclusion agrees with a study by Laine et al. (2002), in which they determined through fitting elliptical isophotes to NGC 4117 that there is no optical evidence for a bar. Since there has never been any research to suggest a bar in NGC 4117 and it does not visually appear to have one, I did not include one in my surface brightness model. Galaxies analyzed with GALFIT in previous studies of surface brightness profiles have all been quite small in angular extent and dim in their images compared to our image of NGC In this image, light from NGC 4117 dominates a significant portion of the image. The galaxy is so extended that we were unsure whether the image actually contains the outermost edges of the galaxy. If these portions of the galaxy are absent, then it is probable that GALFIT will underestimate the spatial extent of the outermost component. To test whether all radii of the galaxy are represented in the image, I measured the mean sky value at the bottom left of WF3, where there was no contamination from the galaxy, and

46 3. NGC compared this sky value to the value at the upper left corner of WF2, where the light along the major axis of the galaxy appears to terminate. Both of these sky values were the same within one standard deviation, implying that all radii of the galaxy are adequately represented in the image. Finally, examination of the inner regions of NGC 4117 show that there are two background galaxies in the image. Because these galaxies overlap with the brightest portions of NGC 4117, it is impossible to determine how far their surface brightness profiles extend. Without knowledge of their extent, it is impossible to mask out these galaxies without losing significant amounts of NGC 4117 data. Instead, we chose to fit them simultaneously with NGC 4117 to ensure that light contamination from these galaxies did not mislead the fitting algorithm. With all of these considerations in mind, I performed the fitting procedure detailed in Section 2.2 and produced four potential models for the surface brightness profile of NGC Table 3.1 lists the parameters of the four models and the χ 2 ν of each model (see Section for a discussion of these χ 2 ν values). Model 1 was created using the first fitting method described in Section 2.2. I kept adding Sèrsic profiles to the model, each with an initial Sèrsic index of n = 2.5, until χ 2 was not substantially improved by the addition of another component or GALFIT had to assign one of the the Sèrsic profiles unrealistic values in order to minimize χ 2. In this case, the latter occurred when I added a third Sèrsic to the model. Furthermore, there was a large improvement to χ 2 when I added the second Sèrsic component to the model while holding the first fixed. This shows that the improvement to χ 2 from adding a second component is truly due to the addition of the second component and does not result from GALFIT arbitrarily adjusting the model parameters to compensate for this new addition. Overall, this demonstrates that NGC 4117 is best described by two Sèrsic components.

47 3. NGC Table 3.1. Potential NGC 4117 Models Model No Sèrsic 1 m ± ± ± ± 0.00 r e [kpc] 1.29 ± ± ± ± 0.00 n 1.66 ± ± ± ± 0.00 Sèrsic 2 m ± ± ± ± 0.05 r e [kpc] 0.05 ± ± ± ± 0.00 n 1.68 ± ± ± ± 0.14 AGN m ± ± 0.02 χ 2 ν Notes Effective radii are calculated assuming the distance to NGC 4117 is d = 18.5±1.3 Mpc, calculated using the recessional velocity corrected for infall from the Virgo Cluster and the Great Attractor (Mould et al. 2000) and a Hubble constant of 73 km s 1 Mpc 1. The magnitudes are the integrated magnitude of each component, as it would appear in the F606W filter on HST. All errors are statistical errors calculated by GALFIT and based on diagonalizing and projecting the covariance matrix. An error of 0.00 means that the error was less than See Section for an explanation of the χ 2 ν values.

48 3. NGC After creating Model 1 in this manner, I added a point source model to the center of Model 1 to test whether NGC 4117 contains an unobscured point source. The resulting model is Model 2. I created Models 3 and 4 using the alternative fitting method described in Section 2.2. For Model 3, I suggested that GALFIT fit NGC 4117 with a disk component and a bulge component simultaneously but allowed all parameters, including Sèrsic indices, to vary. Like Model 2, I created Model 4 by adding a point source model to Model 3. From Table 3.1, we can see that Models 1 and 3 are identical within the errors (errors are more thoroughly discussed in Section 3.3.1). This demonstrates that both fitting techniques produced the same result. It also shows the robustness of the model created by these two techniques; although the initial parameters GALFIT used to generate these models were different, the fitting algorithm arrived at the same final solution. Additionally, these results demonstrate that NGC 4117 is not best described by the canonical bulge and disk components. Although Model 3 was initially described by a disk (n = 1, r s = 1.35 kpc) and a bulge (n = 4, r e = 0.18 kpc), GALFIT s fitting algorithm determined that the surface brightness profile of NGC 4117 was better modeled by two Sèrsic profiles with different Sèrsic indices. Comparison of Models 1 and 3 with Models 2 and 4 reveals that a central point source is not necessary to accurately model the surface brightness profile of NGC Adding a point source only decreases χ 2 ν by 0.01, a tiny amount. Furthermore, when the models were refit with a point source, the second Sèrsic index increased to n = 5.41 in both cases. For galaxies, a Sèrsic index greater than 4 is typically regarded as unrealistic (Binney & Merrifield 1998). Thus, the addition of a point source not only fails to significantly improve the fit, but also produces a model with unrealistic parameters.

49 3. NGC Overall, the surface brightness profile of NGC 4117 is best described by Models 1 and 3. Because these models have the same final parameters (within their errors), we will arbitrarily choose Model 1 as our final model (see Fig. 3.4 for an image of the model and its residuals). NGC 4117 s surface brightness is most accurately modeled by two Sèrsic profiles. The outer profile has an effective radius of 1.29 kpc and a Sèrsic index of The inner profile is much smaller but has a similar Sèrsic index: r e = 0.05 kpc, n = There is no visible point source at the center of the galaxy. Finally, NGC 4117 does not have a classical bulge. The model described here provides a much better fit than the classical bulge-disk model Error Analysis In most fitting scenarios, one would aim for a final χ 2 ν value of 1, which indicates that the model provides an acceptable fit to the data. Examination of Table 3.1 will show that, for our models, χ 2 ν 7.5. Nevertheless, we can still consider our models to be good approximations of the surface brightness profile of NGC The calculation of χ 2 depends on both statistical errors and systematic errors. The systematic errors, such as incorrectly measured sky values and asymmetric features in the image, can significantly increase χ 2 above the ideal value. For NGC 4117, χ 2 ν is large primarily because the nuclear regions of the galaxy contain large amounts of non-axisymmetric dust. These features can never be fully described by the radially symmetric profiles used in GALFIT (Peng et al. 2010). Thus, there will always be discrepancies between the observed surface brightness profile of this galaxy and the model profile created by GALFIT. These discrepancies will cause χ 2 ν > 1 and create noticeable residuals. Therefore, when

50 3. NGC Figure 3.4: Top: Final model of NGC Bottom: Residuals of the final model, created by subtracting the final model from the image of NGC White = model pixel value is greater than the image pixel value. Black = model pixel value is less than the image pixel value. Both images are the same pixel scale and size. Image size =

51 3. NGC determining which model best describes the surface brightness of NGC 4117, we strove to minimize χ 2 rather than to make χ 2 ν 1. The errors provided in Table 3.1 are calculated by GALFIT based on diagonalizing and projecting the covariance matrix. These errors are purely statistical and do not account for any of the systematic errors that may be present in our input parameters. As discussed in Section 2.1, one of the most significant potential sources of error in our model is an incorrect sky level. Failure to correctly estimate the sky level could lead to a model that contains profiles with incorrect effective radii and integrated magnitudes. To test the accuracy of our sky level and estimate the error associated with our value, we followed the procedure detailed by Jiang et al. (2011) and described in Section 2.1. First, I fixed all of the parameters of Model 1 and allowed the sky value I had used when creating my models ( ADUs) to vary. This produced a decrease in χ 2 ν of 0.001, a miniscule amount. GALFIT fit the sky with a new level of ADUs, which is a change of ADUs. Next I fixed the sky level at one standard deviation above and below my measured value and allowed the effective radii and integrated magnitudes of Model 1 s components to vary as GALFIT created a new model (σ Sky = ADUs). Both of these sky levels led to an increase in χ 2 ν of > 0.753, a large amount. This shows that the sky level used when I generated Model 1 was, in fact, a good estimate of the actual sky level in the image. To estimate the systematic errors that can result from an incorrectly measured sky level, I compared the effective radii and integrated magnitudes from the models that were created with the above alternate sky levels to the effective radii and integrated magnitudes of Model 1, our final model. For the first Sèrsic profile, the largest differences in these values were σ r = 0.24 kpc and σ m = 0.13, while for the second Sèrsic profile, the largest differences were σ r = 0.01 kpc and σ m =

52 3. NGC Table 3.2. Final NGC 4117 Surface Brightness Model Sèrsic 1 Sèrsic 2 m ± ± 0.35 r e [kpc] 1.29 ± ± 0.01 n 1.66 ± ± 0.02 B/T 0.03 ± 0.01 Notes See Table 3.1 for a description of the table parameters. An error of 0.00 means that the error was less than B/T: Bulge-to-total ratio based on the relative magnitudes of Sèrsic 2 and Sèrsic These errors are far larger than the statistical errors reported by GALFIT. However, they are a more accurate representation of the actual errors in our model because they incorporate the largest potential source of systematic error in our model generation procedure. Table 3.2 summarizes our final model of the surface brightness profile of NGC 4117, including these new, more accurate errors. This table also includes the bulge -to-total ratio for our final model, which is based on the relative magnitudes of the two components, using Sèrsic 2 as a proxy for a classical bulge. This is a more accurate representation of the comparative brightnesses of the two components since it does not depend on the zero point magnitude of the data. As a final test of our model, I fitted our image of NGC 4117 with the Nuker profile suggested by Capetti & Balmaverde (2007). I created a model based on their parameters but scaled for our image size and used GALFIT to determine the

53 3. NGC goodness of fit of this model. I found that this model generated a χ 2 ν of This χ 2 ν value is far worse than those calculated for all four of our initial models. Thus, for this image of NGC 4117, our model presented in Table 3.2 is a far better description of the surface brightness profile of NGC Similarly, I fitted our image of NGC 4117 with the disk and pseudobulge (n = 1.36) model suggested by Fisher & Drory (2010). Little information about the disk was provided in their paper so I could not constrain any of its parameters while testing their model. Instead I let GALFIT fit a model with a disk of unknown radius and a pseudobulge with n = 1.36 and r e = 389 pc. From these parameters, GALFIT produced a model with r Disk = 1.71 kpc and χ 2 ν = This model resulted in an increase in χ 2 ν of when compared with our model. This small deviation in goodness of fit from our model is expected; the two models have relatively similar parameters so we would expect the two to have similar χ 2 ν values. Nevertheless, this χ 2 ν is still noticabely worse than the χ 2 ν obtained for our final model. Overall, both the Capetti & Balmaverde (2007) and Fisher & Drory (2010) models fail to describe the surface brightness profile of our image of NGC 4117 as well as the model presented in Table 3.2. From this, we can conclude that our model is indeed an excellent model for this image of NGC 4117.

54 Chapter 4 High-Mass Galaxy Sample 4.1 Sample Selection and Data Acquisition As a counterpart to NGC 4117, we also analyzed the surface brightness profiles of several more massive galaxies without visually apparent bulges. In order to remain bulgeless, these galaxies are believed to have evolved secularly for a majority of their lifetimes, without major interactions with other galaxies. Yet these galaxies are much more massive than NGC 4117 and other low-luminosity galaxies. If the galaxy - black hole scaling relations hold, this implies that the supermassive black holes within these galaxies must also be more massive than the SMBH within NGC The current conventional wisdom is that major merger events are responsible for the growth of SMBHs and their host galaxies (e.g., Hopkins et al. 2006). But if these galaxies are truly bulgeless, then they must have either been born massive or there must be another feeding mechanism responsible for the growth of these galaxies and their SMBHs. Recently, several bulgeless galaxies with AGN have been discovered (e.g., Satyapal et al. 2007; McAlpine et al. 2011), but this sample remains small enough that little can be definitively said about such objects as a class. Thus, we present several massive galaxies with previously unidentified active galactic nuclei in an attempt to add to this sample of massive bulgeless galaxies. By studying their surface brightness

55 4. High-Mass Galaxy Sample 50 profiles, we will be able to confirm whether these galaxies are in fact bulgeless and if, therefore, they are examples supporting an alternative feeding mechanism or galaxy formation scenario. All of the galaxies analyzed in this chapter are drawn from a larger sample of galaxies compiled by Moran et al. (2012). The ultimate intent of the study by Moran et al. (2012) is to create an unbiased, distance-limited sample of galaxies in order to better constrain the occupation fraction of black holes in low-luminosity galaxies. Because this survey is largely complete for all galaxies above a magnitude of M g 15.5, it is an ideal sample from which to select our small sub-sample of high-mass bulgeless galaxies. This parent sample is composed of all galaxies within the Seventh Data Release 1 (DR7; Abazajian et al. 2009) of the Sloan Digital Sky Survey (SDSS) that have extragalactic spectral classifications and heliocentric recessional velocities of v r 5300 km s 1 (i.e., z ), which is equivalent to a distance limit of 80 Mpc. Overall, the final sample includes 9,608 galaxies, all of which have spectra from the SDSS database. For our sample, we selected candidates from the parent survey described above that (a) visually appeared to be bulgeless 2, (b) were close enough to have detailed images in the SDSS archives, (c) had starlight subtracted spectra obtained as part of the large survey, and (d) were luminous enough to qualify them as high-mass galaxies (i.e., not dwarf galaxies). Figure 4.1 shows multi-wavelength SDSS images of the galaxies that were ultimately included in our sample and Table 4.1 describes the general properties of these galaxies. The total stellar masses in the table were calculated using SDSS photometry results, following Bell et al. (2003). These bulgeless : Object appears to have a very compact central structure or a nuclear star cluster that represents a small fraction of the total luminosity. Objects are not known à priori to be bulgeless - the analysis that is presented here is aimed at confirming or refuting our speculation.

56 4. High-Mass Galaxy Sample 51 stellar masses are all greater than M, demonstrating that these galaxies are more massive than NGC 4117 and other low-luminosity galaxies (for NGC 4117, M = M ). To fit the surface brightness profiles of these galaxies, I obtained images from the SDSS DR7 database. I chose to use near-infrared i-band images (λ c = 7625 Å) to minimize the effects of dust extinction within the galaxies, thereby making it easier for GALFIT to produce accurate surface brightness models. All SDSS images have an integration time of 54 seconds and a spacial extent of For the nearby galaxies in our sample, this means that each galaxy only occupies a small fraction of its image. Additionally, each SDSS image has a plate scale of 0.396/pixel. Unlike HST WFPC2, the plate scale and image quality is relatively constant throughout the image. Both the large image size and the constant plate scale allowed me to directly measure the PSF from one or more unsaturated stars within the same image as the galaxy. Generating a theoretical PSF is a good technique in situations like that described in Chapter 3, but an empirically measured PSF is always preferable. Although modeling programs like TinyTim are very reliable, they do not always incorporate time-dependent wavefront errors, such as the effects of atmospheric seeing, into their PSF models. 4.2 Spectral Analysis The spectra of the nuclei of these galaxies were measured by SDSS with a dedicated 2.5m telescope at Apache Point Observatory, NM. The telescope contains a 640 fiber-fed double spectrograph, covering a wavelength range from 3800 Åto 9200 Åwith a resolution of spectral features of λ/ λ = 2000 (Abazajian et al. 2009). With this instrument, the telescope is capable of obtaining the spectra of

57 4. High-Mass Galaxy Sample 52 Figure 4.1: Multi-wavelength images of the high-mass sample from SDSS DR7. All images are oriented with north upwards and have the same scale. Image size:

58 4. High-Mass Galaxy Sample 53 Table 4.1. General Properties of the Sample Object Distance [Mpc] M g [mag] (g r) Morph. Type log M [M ] NGC ± SAB(r)c? NGC ± SB(s)cd? UGC ± SA(r)0+? IC ± CGCG ± Sc a Notes Column 2: Distances calculated using the recessional velocity corrected for infall from the Virgo Cluster and the Great Attractor (Mould et al. 2000) and a Hubble constant of 73 km s 1 Mpc 1. Column 3: Extinction-corrected g-band absolute magnitude calculated from the distance in column 2 and the g-band apparent magnitude from SDSS DR7 (λ c = 4770 Å). Column 4: g r color. r-band apparent magnitude also from SDSS DR7 (λ c = 6231 Å). Column 5: NED homogenized morphology, derived from the RC3 catalogue classification (de Vaucouleurs et al. 1991). Column 6: Total stellar mass, calculated using SDSS photometry results and methods developed by Bell et al. (2003). a Morphology determined by Kochanek et al. (2001) over 600 galaxies in a single observation. The data are fed through a calibration pipeline that reduces the data and extracts the spectra for analysis. In order to accurately measure the nuclear emission-line intensities of the spectra, contamination from the host galaxy starlight included in the spectrograph aperture must be removed. This removal process is known as starlight subtraction. In general, the technique of starlight subtraction involves fitting a template spectrum devoid of emission lines to the continuum of the spectrum of interest and then subtracting this template from the data to create a pure emission-line spectrum. A variety of template spectra can be used in this process, including spectra of quiescent (non-active) galaxies, a spectrum of the galaxy of interest that is offset from the nucleus, and models of the spectra from entire stellar populations of a given age and metallicity (see Ho 2008 for more details). To perform starlight subtraction on our data, we used the GANDALF software (Sarzi et al. 2006). This

59 4. High-Mass Galaxy Sample 54 Table 4.2. Integrated Fluxes of Nuclear Emission Lines Hβ [OIII] [OI] Hα [NII] [SII] [SII] Object λ4861 λ5007 λ6300 λ6563 λ6583 λ6716 λ6731 NGC NGC UGC IC CGCG Notes Integrated line fluxes for the lines identified in Figures 4.2 and 4.3. All line fluxes are given in units of ergs cm 2 s 1. program simultaneously fits combinations of starlight templates to the spectrum and convolves them with Gaussians to account for velocity dispersion in the host galaxy. The starlight templates we used in our starlight subtraction process were obtained from the MILES library of stellar population models (Vazdekis et al. 2010), which have been created for a range of stellar populations with varying metallicities, ages, and initial mass functions. Figures 4.2 and 4.3 show our final starlight subtracted spectra for our sample in the Hβ and Hα regimes, respectively. Table 4.2 indicates the line strengths of the most prominent emission lines, which were measured using the IRAF task splot after the spectra had been starlight subtracted. Emission-line galaxies can be optically classified based upon the relative strength of several key emission lines. The relative strengths of emission lines are determined by the excitation mechanism responsible for the emission. Thus, comparison of the line ratios observed in different spectra allows us to differentiate between regions photoionized by UV emission from hot stars in HII regions, by a harder UV and soft X-ray emission associated with black hole accretion in an AGN, and by shock heating of gas such as that observed in supernova remnants (Veilleux

60 4. High-Mass Galaxy Sample 55 Figure 4.2: Prominent spectral lines around Hβ for the galaxies in our sample. All spectra have been starlight subtracted.

61 4. High-Mass Galaxy Sample 56 Figure 4.2 (continued)

62 4. High-Mass Galaxy Sample 57 Figure 4.3: Prominent spectral lines around Hα for the galaxies in our sample. All spectra have been starlight subtracted.

63 4. High-Mass Galaxy Sample 58 Figure 4.3 (continued)

64 4. High-Mass Galaxy Sample 59 & Osterbrock 1987). To utilize this phenomena, Baldwin et al. (1981) created several diagnostic diagrams that compare the line ratios of prominent emission lines. These diagrams, popularly known as Baldwin-Phillips-Terlevich [BPT] diagrams, segregate AGN, starbursts, and shock-heated regions into distinct regions of the graphs. To minimize the effect of reddening correction and errors in flux calibration, Veilleux & Osterbrock (1987) later refined the diagrams to compare the following line ratios: [OIII] λ 5007 / Hβ, [NII] λ 6583 / Hα, [SII] λ / Hα, and [OI] λ 6300 / Hα. More recently, Kewley et al. (2006) created empirical and theoretical lines of maximal starburst to differentiate starburst galaxies and galaxies with AGN activity on BPT diagrams. The theoretical lines were determined using a combination of stellar synthesis models and photoionization models. Figure 4.4 shows the location of our galaxies on three BPT diagrams and in relation to the star-formation lines developed by Kewley et al. (2006). The locations of HII-region galaxies from the same parent sample as our AGN are also shown. Galaxies above and to the right of the line in each BPT diagram have active galactic nuclei, while galaxies below and to the left of the line are star-forming galaxies. From the diagrams, we can see that all of our galaxies lie well above these lines, indicating that the nuclei of these galaxies all contain active galactic nuclei. Conservatively, we expect uncertainties in the line flux measurements of our galaxy spectra of 10 % or less. If the errors for each line are uncorrelated, this translates to an error of only 0.03 in the values plotted in our BPT diagrams. Thus, even with conservative error estimates, our galaxies fall well above the maximal starburst lines in Figure 4.4, securely identifying them as AGN. It is also important to note that a couple of our spectra do not contain [OI] λ

65 4. High-Mass Galaxy Sample 60 Figure 4.4: BPT diagrams showing the location of our AGN (blue crosses) compared to nuclear spectra of HII galaxies (black dots) from the same parent sample (see Moran et al. 2012). Maximal starburst lines from Kewley et al. (2006) are also shown. Spectra above and to the right of the line in each diagram are classified as originating from galaxies that contain AGN.

66 4. High-Mass Galaxy Sample detections. This is because this feature is very weak in these spectra and our exposures were not long enough to detect them. However, we can still conclude that these galaxies are AGN because they lie well above and to the right of the maximal starburst lines on the other two diagnostic diagrams. 4.3 Surface Brightness Profile Fitting I fitted all of the galaxies in the sample using the same procedure described in Sections 2.2 and 3.3. First, I visually inspected each galaxy for bars or unusual features and masked out any regions affected by dust, foreground stars, or background galaxies. Then I created four potential surface brightness models in the same manner described in Section 3.3. If a galaxy appeared to have a bar, or if the galaxy had been previously classified as barred, I generated several additional models that included a bar. For the first bar model, I simultaneously fit a Sèrsic profile with n = 2.5 and a Gaussian profile to represent the bar. Then I proceeded like it was a normal fitting procedure: I added additional Sèrsic profiles, refitting all previous profiles, including the bar, until I reached the maximum number of realistic components. Similarly, I included a Gaussian profile in the alternative fitting procedure. In this case, I simultaneously fit two Sèrsic profiles that initially represent a bulge and a disk, along with a Gaussian profile to represent the bar. Like the procedures described in Section 2.2, I also refit both of these models with a central point source to determine if a compact nuclear feature is visible in the galaxy. Many of the galaxies in the sample have visually apparent spiral arms. These non-radially symmetric features can cause large fluctuations in the residuals when an ellipsoidal component (i.e., Sèrsic, Nuker, etc.) is added to the model. Unfor-

67 4. High-Mass Galaxy Sample 62 tunately, it is very difficult to accurately model the spiral structure of a galaxy; although spiral arms are visually obvious, they are very low contrast features from a numerical standpoint. GALFIT 3.0 has the ability to create such models, but the models are difficult to create and need highly accurate initial parameters in order for the program to identify the arms. Therefore, we have decided not to model the spiral components of the galaxies in our sample. We will also ignore rings and other irregularities. These features, although important components in the secular evolution of galaxies, are of limited interest to our study. Since we are primarily interested in larger-scale structures, modeling the spiral arms and other irregular features within our sample is beyond the scope of this study. After creating at least four potential surface brightness models for each galaxy (eight if the galaxy has a bar), I decided which model best represented the profile of the galaxy in the same fashion as Section 3.3. I chose the final model to be the model with the lowest χ 2 value, but that still has physically realistic features. Table 4.3 and Figure 4.5 show these final models for all the galaxies in the sample. Once again, if the addition of a point source to the model did not improve the fit by a sizable amount, then I concluded that a point source was not needed to accurately model the surface brightness profile of the galaxy. Additionally, if neither fitting strategy resulted in a compact, bulge-like component (n 4), I concluded that no classical bulge is necessary to describe the profile of the galaxy. Classically, one would expect a bulge to be very centrally concentrated with a small effective radius compared to the effective radius of an extended disk. If an unmodeled bulge is present in the galaxy, I would expect to see a strong, compact residual that is slightly more extended than a point source. Thus, to fully determine that no classical bulge is present in the galaxy, I also examined the residuals for any noticeable features around the nucleus. Overall, examination

68 4. High-Mass Galaxy Sample 63 of Figure 4.5 shows that none of the residuals from our final models have features indicating the presence of an incorrectly modeled classical bulge. Most of the models do have small residuals in their nuclei, but these are much weaker than those expected for an unmodeled bulge. Based upon these residuals, we can therefore conclude that none of the galaxies in our sample appear to have classical bulges. For all of the final models I also performed the same error analysis procedure as I did for NGC 4117, using deviations in the sky level value to estimate the potential systematic errors in our models. The errors on the integrated magnitudes and effective radii of the final models in Table 4.3 were measured through this analysis. Below I provide comments on the final models of each galaxy. NGC 2628 : Visually this galaxy has a very apparent bar and associated spiral arms. Following the procedure described above, I modeled this galaxy both with and without its bar to analyze whether such a bar is needed to create the best fitting model. There are also several foreground objects within the image of the galaxy that I masked out. Another complicating feature of NGC 2628 is its apparent irregularity at outer radii, caused by a spiral arm that straightens and extends outwards along the major axis of the galaxy. During surface brightness profile fitting, GALFIT misinterprets this arm as an elliptical feature and moves one of the Sèrsic components to be centered at this feature. Obviously this is incorrect and leads to a physically unrealistic model. To fix this problem, I fit one Sèrsic profile to the inner portions of the galaxy where the arm is not visible in order to find the visual center of the galaxy, and then forced GALFIT to use this position as the center of all other profiles. This is a reasonable constraint to place on my models since the large-scale structure should all be centered on the gravitational center of the

69 4. High-Mass Galaxy Sample 64 Table 4.3. High-Mass Sample Final Surface Brightness Models NGC 2628 NGC 5240 UGC 8262 IC 1137 CGCG Sèrsic 1 m ± ± ± ± ± 4.13 r e [kpc] ± ± ± ± ± 0.55 n 0.39 ± ± ± ± ± 0.01 Sèrsic 2 m ± ± ± 1.25 r e [kpc] 0.69 ± ± ± 1.69 n 1.21 ± ± ± 0.03 AGN m ± ± ± ± 0.07 Bar m ± 0.06 r e [kpc] 4.09 ± 2.38 χ 2 ν Notes Effective radii are calculated using the distances reported in Table 4.1. The magnitudes are the integrated magnitude of each component, as it would appear in the nearinfrared SDSS i-band. Errors in n are statistical errors calculated by GALFIT, while errors in m and r e are calculated based on sky level value fluctuations. An error of 0.00 means that the error was less than

70 4. High-Mass Galaxy Sample 65 Figure 4.5: From left to right: the SDSS DR7 i -band image of the galaxy, the final GALFIT model, and the residuals. All images are oriented with north upward and have an image size of The pixel scale is vertically consistent.