Understanding Post-Starburst Quasars and Their Role in Mutual Black Hole -

Bulge Growth of Galaxies

PI: S. L. Cales, Advisor: M. S. Brotherton

scales@uwyo.edu, mbrother@uwyo.edu

Physics & Astronomy Department, University of Wyoming, Laramie, WY 82071

ABSTRACT

The observational result that massive galaxies harbor supermassive black holes(BHs) at their centers along with the tight correlation between black hole mass andstellar velocity dispersion has led astronomers to hypothesize that supermassive BHsand their host galaxies have mutual formation and evolutionary histories. Hierarchi-cal merging, the process/mechanism by which larger (elliptical) galaxies are formedby mergers of smaller (spiral) ones, is a common thread among astronomers studyingmutual BH/bulge growth. Indeed, the ubiquitous presence of ultra-luminous infraredgalaxies (ULIRGs) involved in mergers lends circumstantial evidence to this theory.Furthermore, the host galaxies of “normal” quasars have been found to be disturbed,a telltale sign of interaction. A new class of objects, post-starburst quasars (PSQs;quasars showing starburst activity within the last few 100 Myrs), might represent anintermediate phase between merger and the end product (red elliptical) of hierarchicalmerger theory, in which feedback from the quasar has recently blown away obcsur-ing/feeding material. To test this theory I propose a multi-wavelength study of thisnew class (PSQs) using high-quality data (HST, Keck, KPNO, IRTF, Spitzer) of asample of 29 PSQs. Specifically, points of study include: i) morphology and quasar tohost galaxy light fractions via HST ACS and IRTF imaging; ii) demographics of hostgalaxy and quasar (starburst mass and age, black hole mass and accretion rate) via Keckand KPNO spectra; iii) PSQ infrared colors and dust contributions via Spitzer spectra,and; iv) comparisons with other classes of objects (ULIRGs, post-starburst galaxies,normal quasars, elliptical galaxies. . .) and theoretical models. Ultimately, this projectwill establish the potentially critical role of post-starburst quasars in evolutionary mod-els for mutual BH/galaxy evolution, determining fundamental observational constraintson theoretical models, which are popular and running rampant on this topic.

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1. Introduction

This thesis will be testing the theory that the MBH -σ∗ correlation (§ 1.1) arises as a directresult of quasar feedback (“blowout”) via extragalactic observations of PSQs. Many classes ofobjects (e.g., ULIRGs, post-starburst galaxies, normal quasars, elliptical galaxies) are expected toadhere to the MBH -σ∗ relation (§ 1.3) who’s formation is attributed to hierarchical merging (§ 1.2).Furthermore, PSQs are predicted to be a phase in the life of a quasar/galaxy at or around “blowout”(§ 1.4) and thus, the “smoking gun” evidence for AGN/quasar feedback showing how the MBH -σ∗correlation arises. The detailed characterization of the first discovered PSQ, UN J1025-0040 (§ 1.5),solidifies the proposed evolutionary sequence as a plausible theory.

1.1. Supermassive Black Hole Mass-Bulge Mass Correlation

Recent research has shown that most galaxies harbor supermassive black holes at their centers(Kormendy & Richstone 1995). Additionally, observations have established that there exists atight correlation between the masses of these black holes and that of the host galaxy stellar bulgecomponent (MBH ∼ 0.15% Mbulge; Figure 1; Magorrian et al. 1998; Gebhardt et al. 2000b), ormeasured from the stellar velocity dispersion with a tighter correlation (M -σ∗ relation; Gebhardtet al. 2000a; Ferrarese & Merritt 2000; Tremaine et al. 2002). This MBH -Mbulge relation suggeststhat the growth of black holes is intimately linked to the formation and evolution of their hostgalaxies and hints at their originating via a common physical process (Kauffmann & Haehnelt2000; Granato et al. 2004; Hopkins et al. 2007).

1.2. Modeling the Starburst-AGN Connection

According to cold dark matter cosmology, hierarchical merging is responsible for the formationof larger galaxies via mergers of smaller ones. Additionally, the triggering of major starburst isattributed to mergers, possibly accounting for the growth of supermassive black holes and theformation of quasars (i.e., high luminosity class of active galactic nuclei), and ultimately predictingthe formation of many of the structures that we see in the Universe today. Theorists have goneon to model star formation and black hole growth in galaxy mergers in an effort to explain theMBH -Mbulge link (e.g., Kauffmann & Haehnelt 2000; Burkert & Silk 2001; Granato et al. 2004).Many of the models invoke the hierarchical model of galaxy formation and evolution put forth byToomre (1977) in which most elliptical galaxies form by mergers.

Hydrodynamical simulations incorporating star formation, supernova feedback, BH growth andAGN (active galactic nuclei) feedback support hierarchical clustering. These simulations follow starformation and BH growth simultaneously during galaxy-galaxy collisions and find that mergers leadto strong gaseous inflows that feed the quasar, starbursts, and, in due course, energy released by

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Fig. 1.— BH mass versus (a) bulge luminosity and (b) velocity dispersion of Gebhardt et al. (2000b, Figure1). There are 33 points in the dispersion plot: 26 from the compilation of Gebhardt et al. (2000a, open circles)and seven from reverberation mapping (filled circles). Solid and dotted lines are the best-fit correlations andtheir 68% confidence bands from Gebhardt et al. (2000a) fitted only to the galaxies with spatially resolvedkinematics. The error bars in the lower right of each plot are representative of the reverberation mappinguncertainties.

the quasar quenches both star formation and further black hole growth (Di Matteo, Springel, &Hernquist 2005). In a simulation of the color evolution of merger remnants (Springel, Di Matteo,& Hernquist 2005), it was found that reddening (i.e., aging of the galaxy) was significantly quickerin remnants containing black holes, an effect brought on by feedback from the quasar (“blowout”).Furthermore, the simulations of Cox et al. (2006) concerning the morphological and kinematicproperties of merger remnants argue that gas-rich major mergers trigger quasars and starbursts,fuel the growth of black holes, and produce remnant galaxies that have the colors, scaling relationsand kinematic structures in concordance with ellipticals of the current epoch. Thus, black holeaccretion and AGN feedback is expected to have a crucial impact on the evolution of its hostgalaxy.

1.3. The Importance of Being Ultraluminous

Ultraluminous infrared galaxies (ULIRGs), strongly interacting merger systems heated by bothstarburst and AGN power sources, have been hypothesized to evolve into normal quasars after thecentral engine clears away the dust associated with the massive star formation (Sanders et al. 1988;Sanders & Mirabel 1996; Veilleux 2006). These objects are possibly the first observational step inthe evolution and formation of a larger elliptical galaxy. Furthermore, the host galaxies of “normal”

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quasars have been found to be disturbed (Canalizo et al. 2007; Bennert et al. 2008), a telltale signof interaction. Post-starburst galaxies are thought to be the evolved galaxies after a few 100 Myrafter the peak of the star formation and AGN activity (Tremonti, Moustakas, & DiamondStanic2007). Thus, an incorporated evolutionary schema upon merger could be: ULIRG → ? → quasaror post-starburst galaxy → late type galaxy, elliptical. There are other possibilities still, such as;minor mergers, harassment, and other interactions which could lead to both black hole growth andbulge growth.

1.4. PSQs, Objects During Feedback?

A new class of objects, post-starburst quasars, might represent objects during the feedbacktime-frame, (i.e., at or around “blowout”). Post-starburst quasars (PSQs) are broad-lined AGN thatalso possess the spectral signatures of massive, moderate-aged stellar populations (in excess of tenbillion solar masses and ages of hundreds of Myrs). The typical composite spectra of PSQs displaybroad emission lines as well as Balmer jumps and strong Balmer absorption lines characteristic oftype-A stars. The absence of obscuring dust in the host galaxy along with the presence of both amassive starburst and BH suggests that they are evolved ULIRGs at or around “blowout”. Followingrecent merger theory, PSQs might then represent a phase in the life of a galaxy/quasar where amerger has activated the central engine, as well as, a burst of star formation, and feedback from thequasar (and/or supernovae) has recently blown away the obscuring (feeding) material. WhetherPSQs represent an evolutionary phase common to all galaxies that shows how the correlationbetween black hole mass and stellar bulge mass arises, or they represent a rare but particularlyviolent interaction that leads to both massive starbursts and nuclear activity is not yet clear.

1.5. The First PSQ, UN J1025-0040

The post-starburst quasar prototype, UN J1025-0040, upholds many features of the evolu-tionary theory. UN J1025-0040 is hosted by a galaxy with a ∼400 Myr old strong starburst(Brotherton et al. 1999), the host appears as a merger remnant (Brotherton et al. 2002), and hasa companion galaxy in a post-starburst phase (Canalizo et al. 2000). A younger UN J1025-0040(tens of Myrs after the starburst) would have a more luminous stellar population and would likelybe dust-enshrouded, placing it in the ULIRG class. These observations suggest that UN J1025-0040 is a plausible transition between ULIRGs and quasars. Timescales of the quasar activityand post-starburst phase likely affect the evolutionary sequence. If, for example, the timescale ofthe quasar’s duty cycle is less than that of the post-starburst phase, then after the quasar’s dutycycle UN J1025-0040 would presumably appear as a post-starburst galaxy. Alternatively, if thepost-starburst phase is shorter, we would expect to see a “normal” quasar after the post-starburstphase had subsided. After both quasar activity and post-starburst phase timescales, UN J1025-0040 would likely appear as an elliptical, i.e., the evolutionary schema then becomes: ULIRG →

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Fig. 2.— The stacked PSQ and QSO, 70 percent of the normalized PSF and corresponding host radialbrightness profiles (as indicated). The differential surface brightness scale is correct, while the absolute scalemight be arbitrary. The PSQs host galaxy is more dominant than that of the QSOs (normal quasars). Also,notice that the shapes of the galactic components differ.

PSQ → quasar or post-starburst galaxy → late type galaxy, elliptical.

2. Foundational Research

2.1. PSQ Catalog

PSQs are a rare class of objects. Of the over 50,000 quasars found by Sloan Digital SkySurvey data release 3 (SDSS DR3; Schneider et al. 2002) ∼600 were spectroscopically selected andcatalogued by Brotherton et al. (2008a, in preparation; hereafter B8a) as PSQs. The automatedselection algorithm was primarily based on that of Zabludoff et al. (1996), an algorithm used toselect post-starburst galaxies. The selection criteria required that the spectra display both thebroad emission lines of luminous AGNs (Seyfert galaxies or quasars) and the Balmer jumps andhigh-order Balmer absorption lines of massive stellar populations on order of hundreds of Myrold. SDSS images of the catalog reveal a 40% near neighbor rate (i.e., close interacting doublesor companions within 10”). Follow up Keck spectroscopy, along with SDSS and HST images ona select 25 PSQs, shows similar results (Brotherton et al. 2008b, in preparation). Of the singlesources, most show extended fuzz or sometimes tidal tails; both symptoms of interaction and mergeractivity. This suggests that PSQs are quasar analogs to post-starburst galaxies; galaxies which areuniformly interacting or merger systems.

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2.2. Multiwavelength UW AGN Group Project

The efforts of the UW AGN group (i.e., M. Brotherton, R. Ganguly, Z. Shang. . . ) are outlinedbriefly to distinguish my efforts of carefully characterizing a small subsample of objects (∼29) froma larger broader analysis of the ∼600 PSQs defined by the SDSS DR3 catalog:

• Catalog Selection - Modified Zabludoff et al. (1996) selection criterion (§ 2.1).

• Environments of PSQs - Large and small scale environments of PSQs in comparison withquasars (§ 2.3).

• Basic Properties - Analysis of SDSS spectra, imaging and archive parameters to determinePSQ colors, starburst age and mass (via evolutionary stellar population synthesis models),BH mass and Eddington accretion rate fraction.

• Comparisons with other classes of objects - By analysis of the data in the SDSS archive andrecent published work on other classes (e.g., ULIRGs, normal quasars) ask the questions:How do the basic properties of PSQs compare with recently published work on other classes?

• Final Catalog - Select final catalog from SDSS Final Release using identifying properties ofPSQs determined from this thesis.

2.3. PSQ Environments

Follow up Keck spectroscopy of the B8a catalog, along with SDSS and HST imaging, wasperformed on a select 25 PSQs with close/possibly interacting companions. Analysis reveals that∼40% of these are indeed close interacting doubles or companions within 10” Brotherton et al.(2008b, in preparation; hereafter B8b). B8b compares the environments of PSQs, of the B8acatalog, with those of normal quasars (QSOs, quasi-stellar objects) on a variety of scales. Usingphotometric redshifts based on SDSS imaging B8b compared the incidence of companion galaxieson scales of 10 to 100 kiloparsecs, finding only a marginal excess of galaxies around PSQs (i.e.,they live in denser regions). Different large-scale environments would suggest that PSQs are notrun-of-the-mill quasars seen at a special time. Typically, PSQs tend to have more neighbors thannormal quasars; about 4:3. In addition, more PSQs have neighbors than quasars; 90%:76%. Amore pronounced difference was found on smaller scale environments. Differences in small-scaleenvironments might indicate direct triggers of the activity that are lost from view at later times. Iutilize image stacking and point source subtraction to compare the immediate environments on 10kiloparsec and smaller scales. The images of the sample are binned by redshift according to 10%differences in size scale. Here I present the smallest redshift bin (Figure 2). The difference in thePSQ and quasar host galaxies is apparent. In all, the large-scale environments of post-starburstquasars are not dissimilar from those of other quasars, thus it is concluded that the more immediate

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small-scale interaction is the primary cause of the post-starburst activity in most objects of theclass.

3. Project Description

While the work of Brotherton et al. (2008a,b) focuses on the broad determination of propertiesof PSQs from a catalog, the aim for this thesis is the careful determination of the properties andnature of PSQs from a smaller sample ∼29. This can be accomplished, by obtaining and analyzinghigh-quality data from various observatories (HST, IRTF, KPNO, Keck and Spitzer ; see Table 1)otherwise not attainable for a large catalog. Specifically, knowledge of: starburst properties(age and mass), quasar black hole mass and accretion rate (relative to Eddington),

PSQ colors, the infrared color of PSQs and dust contribution of the host galaxy, PSQ

allegiances with other classes and theoretical models will broaden our understanding

of PSQs and their environments. Ultimately, the goal paramount to this investigation

is to aid in the understanding of how black hole mass/bulge mass correlations arise

and their evolutionary role. To gain such knowledge, I propose the following small projects asmy thesis:

• Imaging of HST Sample: HST and NASA’s IRTF imaging will determine morphologiesand quasar to host galaxy light contributions (§ 4).

• Host Component Analysis: Keck and KPNO-4m high quality spectroscopy obtained inorder to model starburst properties (age, mass), their black hole mass and accretion rates,and determination of redshifts and classifications of nearby companion galaxies (§ 5).

• Infrared Properties of PSQs: Mid-infrared spectroscopy from Spitzer will provide infrared(IR) colors (mid-IR slope of spectra) and PAH (polycyclic aromatic hydrocarbon) featurestrengths diagnosing the heating source of the dust radiating in the IR (§ 6).

• Statistical Analysis: With the three previously mention studies and further comparisonsto the current AGN-starburst and feedback modeling a comparative (with other classes of ob-jects, such as ULIRGs, normal quasars, elliptical galaxies and possibly post-starburst galaxies)statistical analysis will be performed (§ 7).

4. Imaging of HST Sample

4.1. Le’ Crème

HST’s SNAP programs take advantage of gaps in the General Observer program schedule.During these gaps, nearby SNAP targets can be imaged in a short visit. There are no ‘guaranteed’

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observations of targets, thus, observations of a program are generally not 100 percent completed.As such, chosen targets are randomized with respect to the target list, but do depend on certaingap constraints (e.g., current pointing on the sky). We have received 29 HST/ACS-F606W (broadV-band at 0.05”/pixel) SNAP images (Figure 3) of a target list totaling 80 post-starburst SDSSDR3 quasars. The PSQs were selected from the B8a catalog to have SDSS r < 18.6, Hδ absorptionequivalent widths > 1 Å, 0.25 < z < 0.45 (ensuring high luminosity and similar sizescales, resolvingstructures a half Kpc across) focusing on the clearest most luminous (〈Mr〉 ∼ −22.7) examples ofPSQs. The sample of 29 will hereafter be referred to as the HST sample and is the defining sampleof this thesis. It is a good sample in the sense that these 29 objects are representative of theoriginal SNAP target list of 80 PSQs. Besides important morphological information (§ 4.2), theSNAPshot image analysis will involve point source fitting (§ 4.3) and/or modeling (§ 5.2) similarto what Brotherton et al. (2002) accomplished with UN J1025-0040.

4.2. Morphology

The first step in characterizing PSQs naturally comes from the images themselves. The HSTSNAP images give us a general feel for the morphologies, companions/neighbors and merger/interactionindications of PSQs. Some of the PSQs are obviously quasars living in spiral galaxies, while othersappear to be disturbed ellipticals. There are even hosts that appear to be in-between. In two ofthe images we see obvious merging and about 40 percent have neighbors within 10”. The vast ma-jority of systems show signs of interaction/merger activity, such as, tidal tails, shells, star-formingknots and asymmetries. Brightness profiles of the host galaxy and quasar to host galaxy light ratio(discussed in detail below) will help quantify the nature of PSQ host morphologies.

4.3. Point Source Fitting and Subtraction

Brotherton et al. (2002) estimate the point-spread function (PSF; quasar counterpart to thecomposite image) by a combination of Tiny Tim generated model PSFs and the spectral shape(composite blue quasar spectrum) of the point source. If there was a suitable star in the image fieldone would use that for the PSF estimation. Having obtained good estimates of the PSF, the imageswere then deconvolved using the Richard-Lucy algorithm. From here extended starlight and “fuzz”(centrally concentrated light) colors can be evaluated. PSF-subtracted images separate the hostand quasar components of PSQ UN J1025-0040. The range of the point source to extended fluxratio is determined by finding the minimum percent subtraction of the normalized PSF necessaryto create a smooth surface brightness profile (one with no central dip) and the maximum percentsubtraction necessary for a flux of zero to be present at the center (Figure 4). Knowing the quasarto host galaxy light ratio constrains the composite starburst population modeling of the opticalspectra as discussed further in the next section.

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Fig. 3.— A mosaic of the 29 HST/ACS images. Each image is 10” × 10” and NE is towards the upperleft. At HST’s high resolution (∼0.05”) and an average redshift of 〈z〉 ∼ 0.319, these 200 × 200 pixel imagescorrespond to about 46.7 kpc across. We choose a flat universe in which Ho is 71 km/s/Mpc and ΩM equalto 0.27. The cosmology is chosen to be a flat universe in which Ho is 71 km/s/Mpc and ΩM equal to 0.27.See also http://phsics.uwy.edu/agn/psq/index.html for a finer detailed view.

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Fig. 4.— PSF subtraction, left, and evolutionary stellar population synthesis model fit, right, for UNJ1025-0040. Left, Brotherton et al. (1999) surface brightness profile from ellipse-fitting, UN J1025-0040(triangles) vs. a normalized point source (squares). The circles show the difference between the total lightand 70% of the normalized PSF. Right, the rescaled spectrum of Brotherton et al. (1999, red spectrum) withthe HST -band photometry marked (upper points) and the error bars for the non-PSF (stellar) contribution(lower points). The starburst model (blue spectrum) is a 400 Myr old population plus a young population(in this case, 10 Myr) and should be compared to the lower points. The quasar residual (green spectrum) isthe difference between the two spectra above.

A more novel approach to fitting the point source involves principle component analysis (PCA).PCA is a statistical tool that reduces the number of dimensions of a data set by using the linearcombination of the variables. What does that mean? Well, simply put, the first component of adata set represents the direction in which the data has the largest variance, the second componentmust be normal to the first and represent the direction in which the next largest variance is present,and so on. Thus, in terms of the the PSQ images (in which a host galaxy and quasar point sourceare superposed), the first principle component should be the ‘nice’ PSF (quasar point source). Thatis, the most defining/predictable characteristic of the images will be the PSF. Preliminary resultsof the PCA reveal that the first principle component does indeed look like a PSF although furtheranalysis is required. Originally, I centered the images by using a simple centroiding task in IRAF(Image Reduction and Analysis Facility). I hope to increase the accuracy of the PCA by re-binning,re-centering and then re-running the PCA.

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4.4. IRTF Data and Analysis

We have obtained IRTF/NSFCAM near-IR (K-band) moderately deep imaging of the HSTsample. These will help us further characterize the morphology of the host galaxies and possibleevidence for recent mergers. With a resolution of 0.15”, size scales of about 3 kpc will be probed.Analysis is in progress to determine the quasar contributions at K-band. Like the analysis ofthe HST F606W imaging (§ 4.3), this data will provide constraints on the evolutionary stellarpopulation synthesis modeling of the Keck and KPNO spectra (§ 5.3). Infrared properties of PSQwork (§ 6.2) will also be constrained by the addition of an integrated light data point at K-band.R-K colors are attainable to compare host galaxies, companions and fainter features.

5. Host Component Analysis

5.1. KPNO and Keck Spectroscopy

We have obtained high signal-to-noise optical spectroscopy of all but one object of the HSTsample in order to investigate fundamental characteristics of the PSQ sample. This includes eightobjects observed with KPNO-4m RC-Spec and 20 objects with Keck LRIS. Keck LRIS spectroscopycovers 3100 Å to 1 µm. The 300/5000 grating on the red side and 400/3400 grism on the blue yield2.54 Å/pixel and 1.071 Å/pixel, respectively. The KPNO-4m RC-Spec data using the KPC-10A316/4000 grating spans 3200 Å to 7200 Å with a dispersion of 2.75 Å/pixel. The analysis of thespectra will focus on the determination of the ages and masses of the host stellar populations(utilizing stellar population synthesis models) and the black hole masses and their Eddingtonfractions (by making virial mass estimates). Our approach is to model the spectra as a combinationof a simple instantaneous starburst (more commonly known as simple stellar populations, SSPs;Bruzual & Charlot 2003) and a quasar power-law plus emission lines (including ultraviolet andoptical Fe II blends (Vestergaard & Wilkes 2001). I will model the approach after that of Brothertonet al. (2002) as shown in Figure 4. Host age and mass measurements will be a direct result of thepopulation synthesis models (§ 5.2; Bruzual & Charlot 2003; Maraston 2005; Charlot & Bruzual2007) while the black hole mass and Eddingon fraction estimates (§ 5.3 can be determined from thequasar’s contribution to the composite spectra. The quasar to host light ratio will help constrainthis modeling (§ 4.3).

5.2. Stellar Population Synthesis Modeling

Because the spectra of PSQs depend strongly on starburst strength and time elapsed sincethe starburst, I will build a library of quasar+A type spectra using stellar population synthesis(or evolutionary population synthesis, EPS) models (Bruzual & Charlot 2003; Maraston 2005;Charlot & Bruzual 2007). The technique utilizes the physical properties of age, star formation

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history, metallicity and dust content as the input parameters to create model stellar populations.Bruzual & Charlot (2003) models compute the spectral evolution of stellar populations of differentmetallicities at ages between ∼1 × 105 and 2 × 1010 yr at a resolution of 3 Å FWHM across thewavelength range of 3200 to 9500 Å. Maraston (2005) incorporates contributions from thermallypulsating asymptotic giant branch (TP-AGB) stars into the models for U to K bands with lowerspectral resolution (∼5-10 Å for visible and 20-100 Å for near-IR). TP-AGB stars are expectedto dominate SSPs at intermediate ages (0.3 < t < 2 Gyr). Thus, the addition of the TP-AGBcontributions will be necessary in accurately modeling PSQs, which have stellar populations of∼100s Myrs. The models of Charlot & Bruzual (2007) are similar to Bruzual & Charlot (2003)with the addition of contributions from TP-AGB stars.

The simple stellar population (SSP) modeling technique assumes that all stars are coeval withthe same chemical composition. Stellar populations with star formation history can be expandedin series of these SSPs. More complex models incorporate starbursts that are not instantaneous(composite stellar populations, CSPs) but begin at a certain time and decay exponentially charac-terized by some e-folding time. Higher complexity modeling is gained when SSP and CSP meet inlinear combination. I will model the host stellar population as a linear combination of SSPs and/orCSPs.

The stellar contribution to the library will be built using EPS models of different measuresof the input parameters (i.e., age, star formation history, metallicity and dust content). Addedto each model in the library will be quasar contributions (AGN power-law with emission lines) ofvarying strength. It will be necessary to rebin the library spectra to the same spectral resolutionas the data in order to perform a least-squares error analysis between models of the library andthe spectral data. Since we don’t know the metallicity or dust content of the stellar populationsthere is some uncertainty. The ability of the model to reproduce the strengths of prominent stellarabsorption features in PSQ spectra is notable. In particular, the equivalent width ratio of the Ca IIlines compared to the Balmer lines is an effective age indicator over the age range of these objects(∼50-500 Myr). The addition of the TP-AGB star contribution in the modeling alleviates someof the uncertainty. Also, as mentioned above the determination of quasar to host ratio will helpconstrain this aspect of the modeling.

5.3. Black Hole Mass and Eddington Accretion Rate

Estimations of black hole mass MBH of broad-lined AGN are performed via the reverberationmapping (RM) technique using data from multiple epochs (Peterson 1993). This technique utilizesthe variability properties of AGN to estimate the central mass using the virial theorem, MBH ∼RV 2/G, where R is the size of the broad line region (BLR; as estimated by the mean time delay, τ ,of emission line relative to continuum variations, R = cτ), V is the width of the variable emission-line. The active galaxy RM M -σ∗ (σ∗ is the stellar velocity dispersion of the host bulge component)relation can then calibrated to the M -σ∗ for quiescent galaxies assuming an appropriate scaling

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factor, f (i.e., MBH = fRV 2/G), where f depends on the geometry, kinematics and orientation ofthe BLR. In most instances, RM is time-consuming and impractical. Fortunately, the luminosityof the AGN (L) has been to found to scale with R and can be used as a proxy for cτ in determiningthe MBH (Kaspi et al. 2000). I will rely on scaling relations which relate RM results of physicalproperties (i.e., M , R) to observable quantities (e.g., L, FWHM of C IV, Mg II, Hβ or Hα andcorresponding continuum luminosities) applied to single-epoch data. Mg II and/or Hα will beused due to their location in the optical window for our objects (0.25 < z < 0.4) and the lack ofsignificant contamination from the post-starburst stellar population (strong Balmer absorption).For example, Dietrich & Hamann (2004) give:

MBHM�

= 3.37× 106(

14503000

)0.47(1+α) (λLλ(1450 Å)1044 ergs s−1

)0.47 [FWHM Mg II103 km s−1

]2, (1)

where α is determined from the quasar power-law continuum fit (Fν ∝ να). Estimations of theEddington ratio (Lbol/LEdd) include our MBH estimate and the conversion of the monochromaticluminosity into a bolometric luminosity:

Lbol = f × λLλ(3000Å), (2)

where f ∼ 8.3 is the bolometric correction at 3000 Å and LEdd = 1.51 × 1038(MBH/M�) ergs s−1

(Krolik 1999). Determination of MBH is also useful in estimating a bulge mass, Mbulge. This canbe done using the MBH -Mbulge relation.

6. Infrared Properties of PSQs

6.1. Spitzer Data

We have obtained Spitzer mid-IR spectra for 16 of the 29 PSQs using the two low-resolution(∼60-127) IRS modules. The wavelength range spans 5 to 40 µm. During late Summer of 2007 Iattended the IRS sections of the Spitzer Data Analysis Workshop #5. This workshop covered thevarious pipeline processes for extraction (BCD, Post-BCD, subtraction, extraction). The SPICE(Spitzer IRS Custom Extraction) or SMART (Spitzer contributed software) packages will be usedto re-extract and recalibrate the spectra.

6.2. “Warm” and “Cool” Contributions

Sanders et al. (1988) showed that ULIRGs with “warm” (fν(25 µm)/fν(60 µm) > 0.3) IRcolors have mid-IR emission (∼5-40 µm) of over an order of magnitude stronger than their “cooler”counterparts. This fraction of ULIRGs spans many types of objects, including quasars and isthe premise for the evolutionary connection between ULIRGs and quasars (Sanders et al. 1988).Furthermore, the mid-IR slope has become an AGN/starburst diagnostic of the heating source of

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Fig. 5.— Near to far-IR spectral models of a HyperLIRG, a normal radio-quiet quasar and a star-forminggalaxy (Dale et al. 2006).

the dust radiating at those wavelengths. AGN have powerful UV and X-ray emission which heatsthe surrounding dust to a range of temperatures (even up to the sublimation temperature, Tsub ∼1500 K) resulting in a shallow (warm) mid-IR power-law continuum. Alternatively, stellar powersources do not heat dust to such high temperatures resulting in a steeper mid-IR (cooler) continuumslope (Figure 5). I propose to determine just how warm PSQs are.

6.3. PAH Features of Composite-types (PSQs)

Genzel et al. (1998) showed forbidden mid-IR emission line ratios versus the 7.7 µm PAHemission equivalent width can be used as a diagnostic to quantify energetic contributions fromAGN and stellar sources. This can help constrain the quasar to stellar light ratio. Recent researchhas improved upon this diagnostic by utilizing the clean 6.2 µm feature which does not showcontamination from the 10 µm Si absorption feature (Figure 6). The left panel of Figure 6 clearlyshows why the distribution of the 6.2 µm feature for star-forming objects, ULIRGs and broad-linedAGNs is a useful diagnostic (Dale et al. 2006). To help test the evolutionary theory of ULIRGsevolving into normal quasars via possible transitions objects such as PSQs I will further explorethe previously mentioned diagnostics. Quasars in general do not show PAH features, thus any PAHfeatures found in the SEDs of PSQs presumably come from the host galaxy, which could put themon a path between ULIRGs and normal quasars.

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Fig. 6.— Left, ratios of mid-IR forbidden lines as a function of the 6.2 µm PAH feature equivalent widthfrom Dale et al. (2006). SINGS data are displayed in red with 1 σ error bars based on the statisticaluncertainties; archival data without error bars are indicated with black symbols. The dotted lines are linearmixing models of a “pure” AGN and a “pure” star-forming source. The dashed line in the top panel is asimilar mixing model first presented by Genzel et al. (1998). The solid lines and Roman numerals delineateregions distinguished by Seyfert galaxies, LINERs, star formation, etc. Right, distributions of the equivalentwidth of the clean 6.2 µm PAH feature (Spitzer data) for star-forming objects, ULIRGs and broad-line AGNs(Dale 2006, unpublished). There are more star-forming objects with EW > 1.4 (not shown).

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7. How Do PSQs Fit in the Grand Scheme of Things?

7.1. Comparisons of PSQs with Other Classes of Objects

Once characteristics of the class are defined, a comparative study with other classes (ULIRGs,post-starburst galaxies, normal quasars, normal galaxies) will be performed. The following is abrief overview of the proposed science and the comparisons that will be made:

• Host galaxy demographics - Is there a starburst (SB) mass/bulge mass relation? ASB mass/BH mass relation? If so, how do they compare with other classes that show SBs(ULIRGs, post-starburst galaxies)? Do the host galaxy stellar population ages place them inan evolutionary timeline between ULIRGs, “normal” quasars and red ellipticals?

• Quasar demographics - Same MBH -Mbulge-MSB question as above. How do the BH ac-cretion rate of each class compare?

• Quasar to host galaxy light ratio - Where does the percent contribution of quasar inPSQs put them with respect to ULIRGs, starburst galaxies, normal quasars and galaxies?

• IR spectral shape - If stellar dominated classes are cool and AGN dominated sources arewarm what color should PSQs have, do they lie there and is that in accord with the percentAGN contribution found?

• PAH feature strength - In general PAH features are weak/not found around dominantAGN sources. Given the percent AGN contribution of PSQs do their PAH strengths placethem in logical sequence with AGN and starburst classes?

7.2. Inclusion of PSQs and Other Classes in Theoretical Modeling

It will also be promising to compare results and data from other classes with current theoret-ical models and simulations. This thesis will be testing Hopkins et al. (2007) detailed theory ofhierarchical clustering (Figure 7). As mentioned in § 1.2, Di Matteo et al. (2005) simulated thesimultaneous star formation and BH growth in galaxy-galaxy collisions. Results showed that themerger/interaction triggers inflows which feed (turn-on) the quasar and starburst. But, the energyreleased by the quasar blows away the feeding material quenching the star formation and black holegrowth. Snapshots of the simulation with and without central BH are shown in Figure 8, whilethe star formation rate, BH mass and accretion rate as a function of time are shown in Figure 9.If PSQs are objects at blowout one might expect to see a trend correlating BH accretion rate,mass or star formation rate with an appropriate time since merger indicator (say, dominant stellarpopulation age). If the evolutionary sequence (ULIRG to PSQ to normal quasar or elliptical) iscorrect, then we should see trends there as well placing ULIRGs with the most dominant SB and

– 17 –

youngest stellar populations/AGN and ellipticals/normal quasars with the least dominant SB andoldest stellar populations/AGNs.

8. Other

Research is neither straightforward nor predictable, hence the inclusion of this last section. Itmay become necessary to delve deeper into a result whose meaning is still unclear. Moreover, anadded study increasing the wavelength regime might also prove useful in tying down observed cor-relations. Here are a few “unforeseens” that I foresee; those I don’t will be left to your imagination:

• Affiliated Research

– GALEX UV Archival Photometry Data - Constraints on the SED and quasar contribu-tions in the UV. Constrain UV excesses from young stellar population in the UV.

– Chandra X-Ray Data - X-Ray properties of PSQs, as related to AGN accretion rate,lifetime and fuel availability.

• Follow-up

– Herschel Proposal and Data - Far-IR, ionic fine structure lines probing highly ionizedgas excited by AGN and photoionized gas from young stellar populations.

– Feedback Modeling - Closer involvement with theorists working on merger induced feed-back spurring M -σ∗ correlations.

– Other proposed science as seen fit.

• Funding

– Through Summer 2008 - NSF K-12 Science Enrichment Teaching

– Winter 2008 - Propose for NASA based funding (NESSF, GSRP and Wyoming SpaceGrant)

– Fall 2008 to Summer 2009 - NSF or NASA based funding, Summer Teaching

– Winter 2009 - Propose for NASA based funding

– Fall 2009 to Summer 2010 - NASA based funding

– Fall 2010 to Summer 2011 - Advisor or NASA based funding (if necessary), part-timeTA

– 18 –4Hopkinsetal.

FIG.1.—

Anschematicoutlineofthephasesofgrowthina“typical”galaxyundergoingagas-richmajormerger.ImageCredit:(a)NOAO/AURA/NSF;(b)REUprogram/NOAO/AURA/NSF;(c)NASA/STScI/ACS

ScienceTeam;(d)Optical(left):NASA/STScI/R.P.vanderMarel&J.Gerssen;X-ray(right):NASA/CXC/MPE/S.Komossaetal.;(e)Left:J.Bahcall/M.Disney/NASA;Right:GeminiObservatory/NSF/University

ofHawaiiInstituteforAstronomy;(f)J.Bahcall/M.Disney/NASA;(g)F.Schweizer(CIW/DTM);(h)NOAO/AURA/NSF.

Fig

.7.

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– 19 –

–11–

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– 20 –

– 13 –

Fig. 9.— Star formation rate (top), black hole activity (middle) and black hole growth (bottom) plotted asa function of time during the galaxy-galaxy merger of Figure 8 by Di Matteo et al. (2005). The three linesin each panel correspond to models with galaxies of virial velocity Vvir = 80, 160, and 320 km/s, (bottomto top lines in each panel, also labeled in the bottom panel). For comparison also shown is the evolution ofthe star formation rate for the model without a black hole of Figure 8 (dashed line for the Vvir = 160 km/sgalaxy). We note, in particular, that owing to AGN feedback, the peak amplitude of the starburst duringthe merger is lowered by a significant factor. The black solid circles in the individual panels identify thetimes of the corresponding snapshots shown in Figure 8.

– 21 –

9. Timeline

Some research is currently underway (HST analysis of images, PSF fitting/subtractions) andmuch of the data is already in hand. No doubt, pursuing a thesis involves analyzing data, writingpapers and proposals, making new discoveries, following up on those that are interesting and/orperplexing and becoming an expert in the field by keeping up to date on the literature and obser-vational techniques. Nevertheless, it is possible to complete my thesis by the Spring/Summer of2010. This allows roughly six months per sub-project plus an extra six months for “Other” andwrapping up, e.g.,

• Winter 2008 - Candidacy Exam, HST and IRTF Imaging and PSF subtraction, Begin PaperI (Imaging of HST Sample), Begin Dissertation (Chapters 1 and 2)

• Spring 2008 - Continue with HST data analysis (AGN to host galaxy light fraction), Com-plete and Submit Paper I to Astrophysical Journal, Continue with Dissertation (CompleteChapters 1 and 2)

• Summer 2008 - Reduce Kitt Peak 4-m data, Recalibrate Keck data, EPS of optical dataconstrained by AGN to host galaxy light fraction, Chandra proposal (Other), Begin Paper II(EPS of optical data), Continue with Dissertation (Chapter 3)

• Fall 2008 - Travel?: Porto Alegre, Brazil (M. Brotherton, Collaboration), Riverside, CA (G.Canalizo, EPS Tutorial), Finish analysis (AGN and starburst demographics) for and writingPaper II (Submit to ApJ), Prepare results for AAS meeting, Continue with Dissertation(Complete Chapter 3)

• Winter 2009 - Present results at Long Beach, CA AAS meeting, Reduce Spitzer data,IR diagnostics (Color and PAH features), Begin Paper III (IR diagnostics), Continue withDissertation (Chapter 4)

• Spring 2009 - Finish IR diagnostics analysis (Color and PAH features), Complete PaperIII (Submit to ApJ), Continue with Dissertation (Complete Chapter 4), Travel? China (Z.Shang, Collaboration)

• Summer 2009 - Collect data of other classes of objects and modeling, Herschel proposal(Other), Begin writing Paper IV (Comparisons of PSQs with objects and models), Teach asummer Astronomy 1050 Course, Continue with Dissertation (Chapters 5 and Other)

• Fall 2009 - Finish analysis for and writing Paper IV, Job search and applications, Continuewith Dissertation (Complete Chapter 5), Begin final analysis

• Winter 2010 - Wrap up “Other” if necessary, Job search and applications, Continue withDissertation (Complete Final Chapters), Prepare for Dissertation Defense and AAS Meeting

• Spring 2010 - Defend Dissertation and present at AAS Meeting, Get a Job!

– 22 –

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This preprint was prepared with the AAS LATEX macros v5.2.

– 24 –

Table 1. HST Sample Data Sets

Imaging Spectroscopy

Sample ID SDSS Target z HSTa IRTFb Keckc /KPNOd Spitzere

74 212843+002435 0.346 X X XKeck X43 124833+563507 0.266 X X XKeck X10 040210−054630 0.270 X X XKeck · · ·38 115159+673604 0.274 X X XKPNO · · ·58 145640+524727 0.277 X X XKPNO X39 115355+582442 0.319 X X XKPNO X76 231055−090107 0.364 X X XKeck X07 023700−010130 0.344 X X XKeck X04 020258−002807 0.339 X X XKeck X14 075549+321704 0.420 X X XKeck X61 154534+573625 0.268 X X XKPNO X05 021447−003250 0.349 X X XKeck X13 075521+295039 0.334 X X XKeck X17 081018+250921 0.263 X X XKeck X12 075045+212546 0.408 X X XKeck X11 074621+335040 0.284 X X XKeck X32 105816+102414 0.275 X X XKPNO X69 170046+622056 0.276 X X XKPNO X65 164444+423304 0.317 X X XKPNO · · ·41 123043+614821 0.324 X X XKPNO · · ·71 210200+000501 0.329 X X XKeck · · ·73 211838+005640 0.384 X X XKeck · · ·72 211343−075017 0.420 X X XKeck · · ·75 230614−010024 0.267 X X XKeck · · ·80 234403+154214 0.288 X X XKeck · · ·02 005739+010044 0.253 X X XKeck · · ·59 145658+593202 0.326 X X · · · · · ·78 233430+140649 0.363 X X XKeck · · ·01 003043−103517 0.296 X X XKeck · · ·

aHST/ACS WFC F606W broad V-band filter with spatial resolution of ∼ 0.05”.bNASA’s IRTF/NSFCAM2 K-band (near-IR) imaging at 0.15”/pixel.

cKeck LRIS spectroscopy covering 3100 Å to 1 µm. The 300/5000 grating on the red

side and 400/3400 grism on the blue yield 2.54 Å/pixel and 1.071 Å/pixel, respectively.

dKPNO-4m RC-Spec using the KPC-10A 316/4000 grating spanning 3200 Å to 7200 Å

with a dispersion of 2.75 Å/pixel.

eSpitzer/IRS spectroscopy using the two low resolution (R ∼ 60-127) modules covering5.2 to 38 µm.