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The Astrophysical Journal, 778:188 (12pp), 2013 December 1
C

doi:10.1088/0004-637X/778/2/188

2013. The American Astronomical Society. All rights reserved. Printed in the U.S.A.

EMPIRICAL LINKS BETWEEN XRB AND AGN ACCRETION USING THE COMPLETE z < 0.4 SPECTROSCOPIC CSC/SDSS CATALOG
Markos Trichas1 ,2 , Paul J. Green2 , Anca Constantin3 , Tom Aldcroft2 , Eleni Kalfountzou4 , Malgosia Sobolewska2 , Ashley K. Hyde5 , Hongyan Zhou6 , Dong-Woo Kim2 , Daryl Haggard7 , and Brandon C. Kelly8
EADS Astrium, Gunnels Wood Road, Stevenage, Hertfordshire SG1 2AS, UK; markos.trichas@astrium.eads.net 2 Harvard-Smithsonian Center for Astrophysics, Cambridge, MA 02138, USA 3 Department of Physics and Astronomy, James Madison University, PHCH, Harrisonburg, VA 22807, USA 4 Center for Astrophysics, Science & Technology Research Institute, University of Hertfordshire, Hatfield AL10 9AB, UK 5 Astrophysics Group, Imperial College London, London SW7 2AZ, UK 6 Center for Astrophysics, University of Science and Technology of China, Hefei 230026, China Center for Interdisciplinary Exploration and Research in Astrophysics, Northwestern University, 2145 Sheridan Road, Evanston, IL 60208, USA 8 Department of Physics, Broida Hall, University of California, Santa Barbara, CA 93107, USA Received 2013 June 3; accepted 2013 October 27; published 2013 November 13
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ABSTRACT Striking similarities have been seen between accretion signatures of Galactic X-ray binary (XRB) systems and active galactic nuclei (AGNs). XRB spectral states show a V-shaped correlation between X-ray spectral hardness and Eddington ratio as they vary, and some AGN samples reveal a similar trend, implying analogous processes at vastly larger masses and timescales. To further investigate the analogies, we have matched 617 sources from the Chandra Source Catalog to Sloan Digital Sky Survey spectroscopy, and uniformly measured both X-ray and optical spectral characteristics across a broad range of AGN and galaxy types. We provide useful tabulations of X-ray spectral slope for broad- and narrow-line AGNs, star-forming and passive galaxies, and composite systems, also updating relationships between optical (H and [O iii]) line emission and X-ray luminosity. We further fit broadband spectral energy distributions with a variety of templates to estimate bolometric luminosity. Our results confirm a significant trend in AGNs between X-ray spectral hardness and Eddington ratio expressed in X-ray luminosity, albeit with significant dispersion. The trend is not significant when expressed in the full bolometric or template-estimated AGN luminosity. We also confirm a relationship between the X-ray/optical spectral slope ox and Eddington ratio, but it may not follow the trend predicted by analogy with XRB accretion states. Key words: galaxies: active ­ galaxies: Seyfert ­ quasars: emission lines ­ quasars: supermassive black holes ­ X-rays: binaries ­ X-rays: galaxies Online-only material: color figures, machine-readable table

1. INTRODUCTION There is now strong evidence that powerful active galactic nuclei (AGNs) play a key role in the evolution of galaxies. The correlation of central black hole (MBH ) and stellar bulge mass (e.g., Gultekin et al. 2009; 2012), and the similarity between the cosmic star formation history (e.g., Hopkins & Beacom 2006) and cosmic MBH assembly history (e.g., Aird et al. 2010) both suggest that the growth of supermassive black holes (SMBH) is related to the growth of host galaxies. Understanding what drives the formation and co-evolution of galaxies and their central SMBHs remains one of the most significant challenges in extragalactic astrophysics. Understanding the feedback mechanisms, hence the AGN energy production, remains a fundamental question that needs to be answered. Recent attention has focused on models where AGN feedback regulates the star formation in the host galaxy. These scenarios are consistent with the MBH ­ relation and make various predictions for AGN properties, including the environmental dependence of the AGN/galaxy interplay and the relative timing of periods of peak star formation and nuclear accretion activity. The key feature of these models is that they can potentially link the apparently independent observed relations between star formation, AGN activity, and large-scale structure to the same underlying physical process. For example, in the "radio-mode" model of Croton et al. (2006), accretion of gas from cooling flows in dense environments (e.g., group, cluster) may produce 1

relatively low-luminosity AGNs (LLAGNs), which in turn heat the bulk of the cooling gas and prevent it from falling into the galaxy center to form stars. Alternatively, Hopkins et al. (2006) propose that mergers trigger luminous QSOs and circumnuclear starbursts, which both feed and obscure the central engine for most of its active lifetime. In this scenario, AGN outflows eventually sweep away the dust and gas clouds, thereby quenching the star formation. This "QSO-mode" likely dominates in poor environments (e.g., field, group), as the high-velocity encounters, common in dense regions, do not favor mergers. These proposed models make clear, testable predictions about the properties of AGNs, while observational constraints provide first-order confirmation of this theoretical picture (e.g., Trichas et al. 2009; 2012). Merger-driven scenarios, for example, predict an association between optical morphological disturbances, star formation, and an intense obscured AGN phase in low-density regions. The "radio mode" model, in contrast, invokes milder AGN activity in early-type hosts and relatively dense environments with little or no star formation. Low-redshift galaxies offer the best observational testbeds to study quasar evolution. While environmental studies of nearby AGNs are consistent with non-merger-driven fueling (Constantin & Vogeley 2006; Constantin et al. 2008), analysis of the observed distribution of Eddington ratios as a function of the black hole (BH) masses suggest that at z 0 there might be two distinct regimes of BH growth, which are determined by the supply of cold gas in the host bulge (Kauffmann & Heckman


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2009). Optical studies of narrow emission line galaxies (NELGs) using emission line ratio diagnostics (e.g., Trichas et al. 2010; Kalfountzou et al. 2011), although quite successful in identifying cases where the dominant mechanism is either accretion onto a black hole or radiation from hot young stars, remain inconclusive for low-ionization narrow emission-line galaxies (LINERs) and composite objects. However, using the latter method, Constantin et al. (2009) revealed a sequence from star formation via AGN to quiescence which may be the first empirical evidence for a duty cycle analogous to that of the high-z quasars. The X-ray emission arguably affords the most sensitive test for measurements of the intensity and efficiency of accretion. Combining X-ray properties with optical emission line ratios for a large unbiased sample of low-redshift galaxies can be especially useful because of the high-quality diagnostics available. The Chandra Source Catalog (CSC; Evans et al. 2010), when cross-matched with the Sloan Digital Sky Survey (SDSS), provides an unprecedented number of galaxies in the local Universe for which we can combine measurements of both the X-ray and optical emission. Previous studies of the relation between the X-ray nuclear emission, optical emission line activity, and BH masses provide important physical constraints to the AGN accretion. The conclusions are that LLAGNs are probably scaleddown versions of more luminous AGNs (e.g., Panessa et al. 2006), and that MBH is not the main driver of the X-ray properties (Greene & Ho 2007). The LLAGNs are claimed to be X-ray detected at relatively high rates, and are found to be relatively unabsorbed (e.g., Miniutti et al. 2009), with the exception of those known to be Compton thick. Nonetheless, the X-ray investigations of AGN activity at its lowest levels remain largely restricted to LINERs and Seyferts. In this work, we utilize the largest ever sample of galaxies with available optical spectroscopy and X-ray detections, a total of 617 sources, to build on the work done by Constantin et al. (2009). We combine the CSC X-ray detections with a sample of SDSS Data Release 7 (DR7) spectroscopically identified nearby galaxies that includes broad-line objects, creating a large sample of galaxy nuclei that spans a range of optical spectral types, from absorption line (passive) to actively line emitting systems, including the star-forming and actively accreting types, along with those of mixed or ambiguous ionization. Our main goal is try to verify whether we see the same turning point found by both Constantin et al. (2009) and Wu & Gu (2008) in the ­L/LEdd relation that occurs around = 1.5. This is identical to the stellar mass that X-ray binaries exhibit, indicating that there is probably an intrinsic switch in the accretion mode, from advection-dominated flows to standard (disk/corona) accretion modes. 2. SAMPLE DEFINITION AND DATA ANALYSIS Our sample has been obtained by cross-matching the SDSS DR7 spectroscopic sample with the CSC. We began with a Bayesian-selected cross-match of the CSC (Rev1.1; Evans et al. 2010) and the SDSS (York et al. 2000), performed by the Chandra X-ray Center (Rots et al. 2009), containing 16,852 objects with both X-ray detections and optical photometric objects in SDSS DR7. Detailed visual inspection of matches was performed to eliminate obviously saturated optical sources, or uncertain counterparts in either band. Since both redshifts and emission-line measurements are required for this study, the sample was further restricted to objects for which there also exist SDSS optical spectra, leaving 2000 objects. 2

To take advantage of the diagnostic power of the H /[N ii] emission line complex, we set a limit of z < 0.392 as for the Constantin et al. (2009) sample, yielding 739 objects; of these, 685 are new relative to the aforementioned sample. The SDSS spectra for all 739 objects were downloaded and checked by eye to exclude objects with serious artifacts in the spectrum or with grossly incorrect redshifts. The latter included primarily stars and several broad absorption line quasars. Upon completion, 714 spectra remained, corresponding to 682 distinct objects. A number of the objects are present in multiple Chandra obsids. For simplicity, we merely selected the best observation to use in the X-ray spectral fitting, primarily favoring the smallest off-axis angle ( ) and the longest exposure time. Upon further analysis of the available X-ray data, we rejected 50 objects that were either saturated, or too close to a chip edge. 2.1. Optical Spectroscopic Analysis We limit the investigation to z 0.4sothat theH and other key emission lines are available within the wavelength range of the SDSS spectrograph to perform classic emission line ratio classification (e.g., Baldwin et al. 1981, hereafter BPT; Kewley et al. 2006). We fit optical spectra as described in Zhou et al. (2006), beginning with starlight (using galaxy templates of Lu et al. 2006) and nuclear emission (power-law) components that also account for reddening, blended Fe ii emission, and Balmer continuum fitting. Iterative emission line fitting follows, using multiple Gaussian or Lorentzian profiles where warranted, to fit broad- and narrow-line components. Best template fits of the underlying host star light provide estimates of the stellar mass, MBH (via ), along with mean stellar ages via the strength of the 4000 å break and the H Balmer absorption line. The optical spectral measurements include stellar velocity dispersions with errors for all galaxies as well as measurements of numerous emission line fluxes. For the broad-line objects, we measure the FWHM of the H emission line. Additionally, the AGN flux at 5100 å is calculated for these objects, along with the AGN fraction of the total continuum. Unlike previous studies, this method enables us to use spectroscopic analysis that is as uniform as possible for a diverse sample. For our broad-line objects, BH masses have been retrieved either from Shen et al. (2011), who have compiled virial BH mass estimates of all SDSS DR7 QSOs using Vestergaard & Peterson (2006) calibrations for H and C iv and their own calibrations for Mg ii. For other broad-line AGNs (BLAGNs)--predominantly those spatially resolved Sy 1s that are not targeted by the SDSS QSO programs--we use our own H emission line fits. For all galaxies lacking broad emission lines, we use our measurements of to calculate MBH values using the M­ relation of Graham et al. (2011). Figure 1 shows the comparison between the BH masses derived from the broad H emission line and the BH masses associated with the velocity dispersion. The number of objects with successful spectral analysis includes both broad and narrow emission line galaxies, totaling 617 in our final sample. Figure 2 shows the de-reddened r band magnitude (SDSS modelMag) for the sample, plotted against redshift. 2.2. Multi-wavelength Data A prime advantage of our CSC/SDSS sample, in comparison to deeper pencil-beam X-ray surveys, is its relatively shallow depth that allows for easier source identification in other wavelengths. We have cross-correlated our spectroscopic


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10 9

log10(MBH(Ha) / M )

8

7

6

5 4 4 5 6 7 log10(MBH / M ) 8 9 10

Figure 1. Relation between MBH (via ) and the MBH derived from the broad H emission line for the 176 X-ray sources with both measurements available. The dashed line corresponds to the 1:1 line.

[N ii]6583/H ,[S ii]6716,6731/H , and [O i]6300/H . We only consider emission lines detected with at least 2 confidence. Following Kewley et al. (2006) classification criteria, the emission line objects are separated into Seyferts, LINERs, composite objects, and star-forming galaxies. A quite large (25%) fraction of the emission-line objects remains unclassified as their line ratios, although accurately measured, do not correspond to a clear spectral type in the two diagrams. For the majority, while the [N ii]/H ratio shows relatively high, Seyfert like values, the corresponding [S ii]/H and [O i]6300/H place them in the composite or star-forming objects regime. Thus, because the [S ii] and [O i] emission lines are better AGN diagnostics than [N ii], these systems are likely to be excluded from the AGN samples selected via these classifications. As a consequence, our samples based on the six-line classification are small. To enlarge our samples of galaxy nuclei of all spectral types, we also explored an emission-line classification based on only the [O iii]/H versus [N ii]/H diagram, i.e., a fourline classification method, for the X-ray detected sources. The emission line galaxy samples comprise thus all objects showingatleast2 confidence in the line flux measurements of these four lines only. The delimitation criteria of star-forming and composite objects remain unchanged, while Seyferts and LINERs are defined to be all objects situated above the Kewley et al. (2006) separation line, and with [O iii]/H greater and less than 3, respectively. Throughout the analysis presented in this paper, we will call narrow-line AGNs (NLAGNs) the objects classified as Seyferts via the BPT diagrams. 4. BOLOMETRIC LUMINOSITIES To estimate bolometric luminosities and check for the presence of starburst and/or AGN activity in our sample, we fit the X-ray-to-radio fluxes with various empirical spectral energy distribution (SEDs) of well-observed sources as described in Trichas et al. (2012). We have used a total of 41 such templates, 16 from Ruiz et al. (2010) and Trichas et al. (2012), and 25 from Polletta et al. (2006). We have adopted the model described in Ruiz et al. (2010) and Trichas et al. (2012) which fits all SEDs using a 2 minimization technique within the fitting tool Sherpa (Freeman et al. 2001). Our fitting allows for two additive components, using any possible combination of AGNs, starburst, and galaxy templates. The SEDs are built and fitted in the rest frame. For each galaxy, we have chosen the fit with the lowest reduced 2 as our best-fit model. Fractions of AGNs, starburst and galaxy contributions are derived from the SED fitting normalizations as these are derived from Ruiz et al. (2010) model: F = FBol (ui +(1 - )uj ), (1) where i and j can be AGNs, starburst, or galaxy, FBol is the total bolometric flux, is the relative contribution of the i component to FBol , F is the total flux at frequency , while ui and ui are the normalized i and j templates. 4.1. Comparison between SED Fitting and Optical Spectroscopic Classification Among the 617 sources in our sample, 203 are BLAGN and 414 are NELGs/absorption line galaxies (ALGs). The majority of the sources are best fitted with a combination of templates however of the 203 BLAGNs, in 168 (82%) the dominant contribution is fitted with one of our QSO templates, in 34 (17%) with one of our NLAGN templates and in only 2 (<1%) with a non-AGN template. Of the 414 NELGs or ALGs, in 399 (96%) 3

Figure 2. Dereddened SDSS r-band mag (modelMag) plotted against redshift for our full z < 0.4 spectroscopic sample detected in the Chandra Source Catalog v1.1. Black open circles are BLAGNs, cyan filled circles are ALGs, blue stars NLAGNs, red squares are star-forming galaxies (STARBURST), orange triangles are LINERs, green squares are composite systems, and magenta downward triangles are unclassified sources. (A color version of this figure is available in the online journal.)

sample with publicly available GALEX (DR6; Morrissey et al. 2007), UKIDSS (DR4; Lawrence et al. 2007), Two Micron All Sky Survey (Skrutskie et al. 2006), Very Large Array (Becker et al. 1995), and WISE (Wright et al. 2010) catalogs. We have retrieved these catalogs using the Virtual Observatory TOPCAT tool (Taylor 2005). Using Monte Carlo simulations and the Fadda et al. (2002) method, we have concluded that a search radius of 2. 5 provides us with a P (d )< 0.02, where P (d ) is the Poisson probability of a GALEX source to have a random association within a distance d, yielding an expected rate of random associations of less than 5%. The GALEX catalog contains only sources that were detected at signal-to-noise ratio (S/N) > 5 in at least one of the near-UV, far-UV filters. All matches were then visually inspected to remove any apparent spurious associations. We have adopted a similar method for the catalogs at other wavebands. 3. OPTICAL SPECTRAL CLASSIFICATION To classify emission line sources we use BPT diagnostic diagrams, which employ four line flux ratios: [O iii]5007/H ,


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the dominant contribution is fitted with one of our NELG/ALG templates with only 15 (4%) being fit with a BLAGN template. Of the 414 NELGs/ALGs based on spectral features and reliable emission line diagnostics, were possible, we have 63 passives, 39 H iis, 77 transition objects, 130 Seyferts, and 38 LINERs. In 84% of the passives the dominant contribution is fitted by one of our elliptical templates, in 100% of the H iis with one of the star-forming templates and in 98% of the Seyferts with one of our Seyfert templates. All (100%) of the transition objects require a combination of AGN and star-forming templates to fit observed photometry. In the case of LINERs, the dominant contribution is best fitted with a Seyfert, passive, or star-forming template in 37%, 50%, and 13% of the cases, respectively. Based on the above, we can securely claim that the agreement of our SED fitting with optical spectroscopic classification is excellent for all types of these objects. 5. X-RAY SPECTRAL FITTING Based on the method used in Trichas et al. (2012), we perform X-ray spectral fitting to all X-ray sources in our sample, using the CIAO Sherpa9 tool. For each source, we fit three powerlaw models that all contain an appropriate neutral Galactic absorption component frozen at the 21 cm value:10 (1) photon index , with no intrinsic absorption component (model "PL"); intr (2) an intrinsic absorber with neutral column NH at the source redshift, with photon index frozen at = 1.8 (model "PLfix"). Allowed fit ranges are -1.5 < < 3.5 for PL and intr 1018 < NH < 1025 for PLfix. (3) A two-parameter absorbed intr power-law where both and the NH are free to vary within the Gal above ranges while NH is fixed (model "PL_abs"). All models are fit to the ungrouped data using Cash statistics (Cash 1979). The latter model, PL_abs, is our default. As discussed in Trichas et al. (2012), the best-fit from intr our default model is not correlated with NH , which illustrates that these parameters are fit with relative independence even in low count sources. Furthermore, the best-fit in the default PL_abs model correlates well with that from the PL model for the majority of sources; the median difference is 15% of the median uncertainty. A potentially useful Figure 3 shows the distribution of for all sources with log LX > 42 divided by optical spectroscopic class. As the X-ray emission in all these sources is predominantly coming from an AGN, the peak of its distribution appears to be at around = 2 as expected. This indicates that for luminous X-ray sources is not likely to be severely affected by stellar X-ray emission from the host. However, although the peak of each distribution is the same, the histogram shape appears to change as we move to the type 2 spectral type sources corresponding to lower luminosity, or weaker accretion, e.g., LINERs and composites, that could account for different inclinations, and thus dustier circumnuclear regions and not necessarily for intrinsically hard ionizing continua, for which there is a strong hard tail in the distribution and a sharp drop above = 2. For ALG, the mode is at 2.0, confirming that these sources contain a powerful AGN, but a soft tail also indicates the likelihood of either a different accretion mode, or perhaps contributions from softer emission components such as thermal bremsstrahlung from a hot interstellar medium, or even from circumgalactic hot gas, e.g., from the remnants of
9 10

a "fossil" galaxy group. These sources are typical examples of X-ray Bright Optically Inactive Galaxies (Comastri et al. 2002). Four possible explanations have been proposed for the nature of these objects (e.g., Green et al. 2004): a "buried" AGN (Comastri et al. 2002), an LLAGN (Severgnini et al. 2003), a BL Lac object (Yuan & Narayan 2004), and galactic scale obscuration (Rigby et al. 2006; Civano et al. 2007). The unclassified objects appear to follow a similar distribution to the dustier objects. This is expected as these are sources with very strong narrow emission lines which we fail to classify because of the issues discussed in Section 3. Similar trends are also found when we include only X-ray spectra with >20 counts in the histograms (shadowed histograms). For objects with sufficient X-ray counts, and for which none of the aforementioned models provide a satisfactory fit, multiple additional models could be fitted to account for other possible sources of X-ray emission, e.g., from the hot interstellar medium, or a separate power-law component from X-ray binary (XRB) populations. In fact, most of our sources have too few counts to warrant such detailed fitting. Objects for which we find very low (or even negative) could be heavily intrinsically absorbed, in which case we observe primarily the reflected component. Modeling this in the 2­10 keV band with a power law would result in very hard, apparently unphysical, slopes. We show a simple simulation in Figure 4 to illustrate. We used two very simple XSPEC (Arnaud et al. 1996) models (1) phabs*PL and (2) PL+pexriv (with ionization parameter 10, so effectively reflection from neutral matter). The different contours show how the measured depends on the (1) absorbing column NH and (2) strength of reflection /2 , for intrinsic = 1.0, 1.5, 2.0, 2.5, 3.0. These plots illustrate that negative observed values of more likely correspond to the absorption case, at least in this very simple approach. To clarify this issue, deep X-ray exposures, preferably with hard X-ray response extending above 8 keV, are required to allow more detailed X-ray spectral analysis. Additionally, we might expect that such objects show less X-ray variability, since intrinsic variability would be averaged by the reflection process (Sobolewska & Done 2007). All multi-wavelength data are given in an online table available from the journal. A subsample of this table is given as an example in Table 1. 6. ­L/L
Edd

RELATION FOR z < 0.4 AGN/GALAXIES

http://cxc.harvard.edu/sherpa Gal Neutral Galactic column density NH taken from Dickey et al. (1990)for the Cha nd r a , aim-point position on the sky.

The relation between the X-ray photon index and the Eddington ratio for the entire SDSS/CSC sample of sources with optical spectra at z < 0.4 is illustrated in Figure 5. Xray luminosity has been calculated using the method described in Green et al. (2011) and bolometric luminosities have been calculated as described in Section 4. Figure 5 shows 484 sources with minimum net counts of 20 and where the difference between the upper and lower 90% confidence limits to (max - min ) 3. These selection criteria are applied in order to include only sources that have meaningful X-ray spectral fits (Section 5). Different colors in Figure 5 represent the different spectral classes as shown in Figure 2. To allow sampling of higher accretion rates, the BLAGN sample in Figure 5 contains both CSC/SDSS z < 0.4 QSOs and high-redshift QSOs from the Chandra Multiwavelength Project (ChaMP) spectroscopic sample of Trichas et al. (2012). We have estimated the bolometric luminosity of the AGN component for every SED in our sample for the purpose of 4


The Astrophysical Journal, 778:188 (12pp), 2013 December 1
200 150 100 50 0 10 8 6 4 2 0 3 2 1 Number of objects 0 8 6 4 2 0 12 10 8 6 4 2 0 12 10 8 6 4 2 0 25 20 15 10 5 0 -2.0

Trichas et al.

BLAGN

ALG

STARBURST

COMPOSITE

NLAGN

LINER

UNCLASS

0.0

2.0

4.0

6.0

8.0

10.0

Figure 3. Distribution of for the log LX > 42 sample, separated into subclasses based on optical spectroscopic classification. Error bars show the Poisson errors on the number sources in each bin. The shadowed histograms include only sources with minimum net counts of 20. From top to bottom: BLAGNs, ALGs, star-forming objects, composite objects, NLAGNs, and unclassified objects. (A color version of this figure is available in the online journal.)

Figure 4. Observed X-ray photon index as a function of the absorption column density (left: XSPEC model phabs â power law) and reflection amplitude (right: XSPEC model pexriv with ionization parameter 10). The intrinsic X-ray photon index varies between 1.0 and 3.0. X-ray power-law spectra absorbed with NH > 1023 cm-2 , and reflection-dominated X-ray spectra with /2 > 5­10 result in dramatically decreased observed X-ray photon index, reaching negative values for NH > few times 1023 cm-2 . (A color version of this figure is available in the online journal.)

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The Astrophysical Journal, 778:188 (12pp), 2013 December 1

Table 1 Sample of Our Full Online Catalog SDSS J Namea CXOJ122137.2+295701 CXOJ123614.5+255022 CXOJ112314.9+431208 CXOJ153600.9+162839 CXOJ120100.1+133127 CXOJ141652.9+104826 CXOJ090105.2+290146 CXOJ122959.4+133105 CXOJ122843.5+132556 CXOJ141531.4+113157 CXOJ111809.9+074653 CXOJ121531.2-003710 CXOJ145241.4+335058 CXOJ102451.2+470738 CXOJ082332.6+212017 CXOJ141910.3+525151 CXOJ011544.8+001400 CXOJ011522.1+001518 CXOJ082001.8+212107 CXOJ152154.0+082916 CXOJ142428.1+351922 CXOJ142909.9+353615 CXOJ020925.1+002356 CXOJ153311.3-004523 CXOJ002253.2+001659 CXOJ155627.6+241800 CXOJ103515.6+393909 CXOJ132451.4+362242 CXOJ075630.4+410210 Redshiftb 0.17 0.18 0.08 0.38 0.20 0.02 0.19 0.10 0.25 0.26 0.04 0.35 0.19 0.14 0.02 0.08 0.04 0.39 0.08 0.11 0.17 0.23 0.06 0.15 0.21 0.12 0.11 0.02 0.07 Classc 5 5 3 6 6 5 3 3 0 6 2 0 6 5 1 2 3 5 1 6 6 2 2 3 5 2 3 4 4 log M
BH d

log MBH(H -99.99 -99.99 -99.99 7.83 7.40 -99.99 -99.99 -99.99 -99.99 7.99 -99.99 -99.99 7.62 -99.99 -99.99 -99.99 -99.99 -99.99 -99.99 7.35 7.04 -99.99 -99.99 -99.99 -99.99 -99.99 -99.99 -99.99 -99.99

)

e

Sigma Starf 185.70 177.12 154.35 168.63 143.47 329.85 200.91 126.48 296.18 166.43 135.45 367.86 177.61 169.35 160.63 136.41 91.59 329.65 97.73 117.34 188.09 123.97 108.53 189.97 160.11 142.79 85.54 240.08 249.54 ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± 9.34 7.95 11.37 143.47 18.22 3.73 13.8 18.62 17.58 20.25 5.16 31.4 19.11 11.16 8.23 6.43 4.93 26.03 8.43 11.64 19.99 11.82 7.10 21.76 14.70 11.86 13.84 2.74 4.49

Net Countsg 26 30 123 69 68 2004 67 228 64 2936 29 21 43 57 53 43 350 386 29 855 395 634 40 37 150 94 33 241 133



h

Nintr H

i

F(2­8 keV)j 13.94+6..37 -6 36 11.53+4..46 -4 43 33.33+3..21 -3 25 139.4+29..8 -30 0 203.5+27..6 -28 0 240.5+5..6 -5 4 +5.70 43.95-5.63 119.7+8..2 -8 2 137.9+60..3 -59 61 229.9+4..4 -4 3 4.38+0..73 -0 73 +1.67 4.07-1.64 53.84+8..66 -8 57 17.73+2..56 -2 54 17.13+2..58 -2 60 3.28+0..57 -0 56 100.10+5..7 -5 73 147.70+7..80 -7 70 +2.23 11.03-2.21 1281.0+59..0 -59 0 284.3+16..6 -16 5 97.79+3..91 -3 92 99.61+18..99 -18 80 164.8+28..0 -28 4 43.03+3..69 -3 66 +15.6 119.1-15.5 109.2+20..2 -19 08 81.04+5..39 -5 44 73.62+6..69 -6 62

log LX /Lbol - - - - - - - - - - - - - - - - - - - - - - - - - - - - - 3.09 3.01 2.99 1.13 1.32 4.88 2.62 1.80 2.72 1.29 4.17 4.42 2.33 2.93 4.70 3.70 1.89 2.49 2.43 0.77 1.81 1.03 1.97 2.16 2.03 1.90 0.89 4.95 3.81

k

LBol

l

LAG

N

m

7.96 ± 0.40 7.86 ± 0.42 7.55 ± 0.37 7.83 ± 0.22 7.55 ± 0.30 9.24 ± 0.54 8.14±0.34 7.11±0.30 9.00±0.31 7.81±0.29 7.26±0.49 9.49±0.22 7.92±0.30 7.75±0.38 7.64±0.42 7.27±0.46 6.38±0.50 9.24±0.25 6.53±0.42 7.20±0.40 8.02±0.29 7.06±0.37 6.76±0.44 8.01±0.28 7.63±0.34 7.38±0.37 6.24±0.34 8.54±0.58 8.62±0.51

1.84+0..88 -0 58 2.91+1..66 -0 84 1.86+0..39 -0 36 2.13+0..24 -0 40 1.68+0..54 -0 47 2.05+0..08 -0 08 2.16+0..44 -0 41 0.3+0..24 -0 22 2.37+0..86 -0 67 1.74+0..05 -0 05 1.97+0..68 -0 54 3.67+1..09 -0 87 1.41+1..05 -0 86 2.25+0..53 -0 48 2.35+0..55 -0 50 1.44+0..53 -0 40 1.7+0..17 -0 17 1.31+0..19 -0 19 2.82+0..97 -0 48 1.87+0..13 -0 09 +0.18 1.9-0.15 2.13+0..13 -0 13 1.83+0..53 -0 34 1.92+1..27 -1 13 1.03+0..42 -0 40 0.94+0..73 -0 67 1.7+1..23 -1 01 1.13+0..20 -0 19 2.99+0..45 -0 30

21.04 ± 0.52 22.80 ± 0.22 21.94 ± 0.37 21.00±0.52 21.77 ± 0.03 20.60 ± 0.30 21.26±0.03 21.28±0.15 21.25±0.15 20.48±0.01 20.90±0.35 21.94±0.15 22.73±0.14 21.23±0.01 21.49±0.13 20.95±0.55 20.90±0.01 22.25±0.30 20.30±0.08 20.00±0.70 20.47±0.43 20.60±0.18 20.30±0.01 22.93±0.28 22.55±0.52 22.61±0.30 22.83±0.23 21.51±0.51 20.48±0