Successfully reported this slideshow.
We use your LinkedIn profile and activity data to personalize ads and to show you more relevant ads. You can change your ad preferences anytime.

Morphologies of mid-IR variability-selected AGN host galaxies

77 views

Published on

We use multi-epoch 3.6 and 4.5 µm data from the Spitzer Extended Deep Survey (SEDS) to
probe the AGN population among galaxies to redshifts ∼3 via their mid-IR variability. About
1 per cent of all galaxies in our survey contain varying nuclei, 80 per cent of which are likely
to be AGN. Twenty-three per cent of mid-IR variables are also X-ray sources. The mid-IR
variables have a slightly greater fraction of weakly disturbed morphologies compared to a
control sample of normal galaxies. The increased fraction of weakly distorted hosts becomes
more significant when we remove the X-ray emitting AGN, while the frequency of strongly
disturbed hosts remains similar to the control galaxy sample. These results suggest that mid-IR
variability identifies a unique population of obscured, Compton-thick AGN revealing elevated
levels of weak distortion among their host galaxies.

Published in: Science
  • Be the first to comment

  • Be the first to like this

Morphologies of mid-IR variability-selected AGN host galaxies

  1. 1. MNRAS 476, 1111–1119 (2018) doi:10.1093/mnras/sty164 Advance Access publication 2018 January 19 Morphologies of mid-IR variability-selected AGN host galaxies Mugdha Polimera,1‹ Vicki Sarajedini,1‹ Matthew L. N. Ashby,2 S. P. Willner2 and Giovanni G. Fazio2 1Department of Astronomy, University of Florida, Gainesville, FL 32611, USA 2Harvard–Smithsonian Center for Astrophysics, 60 Garden St, Cambridge MA 20138, USA Accepted 2018 January 16. Received 2018 January 16; in original form 2017 September 18 ABSTRACT We use multi-epoch 3.6 and 4.5 µm data from the Spitzer Extended Deep Survey (SEDS) to probe the AGN population among galaxies to redshifts ∼3 via their mid-IR variability. About 1 per cent of all galaxies in our survey contain varying nuclei, 80 per cent of which are likely to be AGN. Twenty-three per cent of mid-IR variables are also X-ray sources. The mid-IR variables have a slightly greater fraction of weakly disturbed morphologies compared to a control sample of normal galaxies. The increased fraction of weakly distorted hosts becomes more significant when we remove the X-ray emitting AGN, while the frequency of strongly disturbed hosts remains similar to the control galaxy sample. These results suggest that mid-IR variability identifies a unique population of obscured, Compton-thick AGN revealing elevated levels of weak distortion among their host galaxies. Key words: galaxies: active – galaxies: evolution – infrared: galaxies. 1 INTRODUCTION Active galactic nuclei (AGN) are galaxies which accrete signifi- cant amounts of material on to their central super massive black holes (SMBHs) and have long been one of the most interesting phenomena in extragalactic astronomy. Questions relating to their formation, structure, and SMBH accretion have been contemplated since theories explaining the powering mechanism of AGN were developed (Lynden-Bell 1969). AGN may represent an evolution- ary phase for many galaxies and could have a significant impact on their star formation. To understand the role played by AGN in the evolution of galaxies, it is necessary to identify them in galaxy surveys out to redshifts of ∼3 where significant AGN fuelling and bulge growth occur. Historically, AGN have been identified using several meth- ods such as colour selection (Stern et al. 2005; Kochanek et al. 2012), spectroscopic signatures (Morse, Raymond & Wilson 1996; Veilleux 2002), and variability (Sarajedini, Koo & Klesman 2009; MacLeod et al. 2010). Studies using optical data found that identi- fication is biased against more obscured or faint sources (Richards et al. 2002). To address these issues, other studies have used X-ray selection (Alexander et al. 2003) and mid-IR colour selection (Lacy et al. 2004; Stern et al. 2005; Park et al. 2008), but they each have their own biases. For instance, the X-ray selection is biased against heavily obscured AGN, while colour-selection is biased against AGN that are masked by the intrinsic luminosity of the host galaxy (Kocevski et al. 2015, hereafter K15). E-mail: mugdhapolimera@ufl.edu (MP); vicki@astro.ufl.edu (VS) AGN have long been identified as variable sources with the variability most likely originating from instabilities in the ac- cretion disc or temperature fluctuations (e.g. Ruan et al. 2014). For sources obscured by dust in the vicinity of the AGN, per- haps in the form of a dusty torus as envisioned in the uni- fied model, the variable UV photons may then be reprocessed and produce variability in the mid-IR. Thus, mid-IR variabil- ity may be more sensitive than other selection techniques to obscured, Compton-thick AGN. Recent results from the NOAO Deep Wide Field Survey and Spitzer Deep Wide Field Survey (Kozłowski et al. 2010, 2016) have revealed that as many as 1.1 per cent of galaxies are significant variables in the Spitzer bands at 3.6 and 4.5 µm. Their study covered a 9 deg2 field with short (∼90 s) exposures in five epochs spanning ∼10 yr. The mid-IR variable light curves had lower amplitudes at short time-scales compared to optical AGN which can be inter- preted as the accretion disc or dust torus smoothing out the short time-scale variations. An interesting and unsolved question concerning AGN is how accretion is initiated on to the central SMBH. Hopkins, Kocevski & Bundy (2014) suggested that AGN accretion can be triggered by mergers. However, surveys to quantify merger signa- tures in AGN samples have yielded mixed results. Kocevski et al. (2012) showed that host galaxies of a sample of X-ray detected AGN are no more likely to show morphological disturbances than a mass-matched sample of control galaxies. In a follow-up study, K15 found that the more obscured X-ray detected AGN have higher probability of merger signatures than the unobscured ones suggest- ing that dust may hide the AGN shortly after the fuelling is triggered by a merger. C 2018 The Author(s) Published by Oxford University Press on behalf of the Royal Astronomical SocietyDownloaded from https://academic.oup.com/mnras/article-abstract/476/1/1111/4817554 by sacani@gmail.com on 28 April 2018
  2. 2. 1112 M. Polimera et al. Here, we address the issue of obscuration and its impact on de- tecting merger-triggered accretion by identifying AGN in deep ex- tragalactic surveys by their variability in the mid-IR. The data used in our study are part of the Spitzer Extended Deep Survey (SEDS; Ashby et al. 2013) and the Spitzer-Cosmic Assembly Near-infrared Deep Extragalactic Legacy Survey (S-CANDELS; Ashby et al. 2015), two Exploration programmes completed during Spitzer’s warm mission. The multi-epoch, deep images allow us to probe variability on time-scales from months to several years using 3.6 and 4.5 µm images of five extragalactic fields covering ∼0.9 deg2 of the sky. Our survey extends ∼3 mag deeper than the SDWFS field from Kozłowski et al. (2016) and in some regions includes a larger number of epochs for variability analysis. We perform mor- phological classification for the subset of variables that lie within the near-IR CANDELS survey (Grogin et al. 2011; Koekemoer et al. 2011) to look for evidence of merger-triggered accretion among the AGN host galaxies. In this paper, Section 2 describes the data sample, Section 3 out- lines the steps to identify variable galaxies, Section 4 describes the selection of the control sample for morphology classification. The classification process is described in Section 5. Section 6 discusses the results and the following section lists our conclusions. 2 DATA DESCRIPTION The Spitzer IRAC data used for this study are fully described by Ashby et al. (2013, 2015). The survey covered the 30 × 30 arcmin2 Extended GOODS-South (a.k.a. the GEMS field, here- after ECDFS), 30 × 30 arcmin2 Extended GOODS-North (HDFN), 50 × 50 arcmin2 UKIDSS Ultra-Deep Survey (UDS), a 10 × 60 arcmin2 region within the Extended Groth Strip (EGS), and a 10 × 60 arcmin2 strip within the UltraVista deep survey of the larger COSMOS field. Three epochs of SEDS observations, each consisting of up to 4 h integration time per pointing, were obtained for these fields in 2010 and 2011. However, all of the fields had previous Spitzer observations, some dating back to 2003, providing a total of four to 10 epochs of observations separated by several months to years. Table 1 lists all epochs of data obtained for each SEDS field used in this study. The spectral energy distributions of AGN have a minimum at a rest wavelength of 1 µm (Elvis et al. 2009; Assef et al. 2010) with bluer light originating from the accretion disc and redder light coming from hot dust in the vicinity of the AGN (Urry & Padovani 1995; Asmus et al. 2014; Vazquez et al. 2015). The Spitzer images from our survey probe from just near the AGN minimum at z ∼ 3 into the dust-dominated regime at lower redshifts. Because our goal is to identify variability in the mid-IR, we make use of images produced from the individual epochs for each field. Identical reduction procedures were applied to all data to ensure a uniform data quality throughout the survey. Each individual epoch consists of the combined images, or mosaics, which were re- sampled to 0.6 arcsec. per pixel resolution and locked to the same world coordinate system. In this way, any particular (x, y) pixel coordinate corresponds to the same RA and Dec. in every epoch. We used SEXTRACTOR (Bertin & Arnouts 1996) to identify ex- tended and point-like sources in each epoch of every field in our survey. Photometry was measured in both the 3.6 and 4.5 µm im- ages using apertures ranging from 2.4 to 12 arcsec in diameter. A very low detection threshold (1σ) was adopted to improve source completeness in the individual epochs. Sources found in each epoch were matched by RA and DEC to within 0.5 pixels of a source in the SEDS catalogues (Ashby et al. 2013). This eliminated spuri- ous detections found in the individual epochs due to bad pixels around bright sources or near the noisy edges of the mosaic. The photometry from each epoch was zero-point corrected to match the photometry in the SEDS catalogues. To determine the offsets, we used sources in the bright-to-mid-range between 18th and 21st mag- nitude which consisted of ∼1800 to ∼14000 sources depending on the image size of the epoch. Offsets were found up to ±0.03 mag but were generally closer to ±0.01 mag. These offsets were applied to each epoch independently, ensuring photometric consistency in each epoch. Based on the PSF for unresolved sources in the Spitzer images, we chose the 3.6 arcsec diameter aperture for our variabil- ity analysis. This aperture is large enough to include nuclear light from a varying active nucleus while excluding light from the non- varying, outer portions of the host galaxy and provides maximum sensitivity to flux variations originating from the AGN. Table 1 shows the number of sources found in each epoch of each observing field and the median depth of the observation. The differences in the numbers of sources are a reflection of the areas observed. Our analysis includes only those sources detected in both bands in three or more epochs in each field, which amounts to about 30 per cent of all sources detected. Thus, the total number of sources used in our variability analysis is 87670. 3 VARIABILITY SELECTION For each source in the catalogue detected in both bands with at least three epochs of data, we calculated the standard deviation v[X] as a function of the apparent magnitude as done by Kozłowski et al. (2010), v[X] = 1 N − 1 N j=1 (m[X]j − m[X] )2 1 2 , (1) where N is the number of epochs for an observed source, m[X]j is the magnitude of the source in the jth epoch, X is the 3.6 or 4.5 µm band, and m[X] is the average magnitude of the source in band X. Fig. 1 shows the v[3.6] values for all galaxies in the COSMOS field as a function of magnitude. Because the vast majority of galax- ies in our survey are expected to be non-varying, the spread in the v[X] values represents the photometric noise which increases as a function of magnitude. We quantify this noise term by first calculat- ing the median v[X] value, vm[X], in various magnitude bins along the x-axis. The width of the bins used for this calculation ranged from 1 mag at the bright end to 0.5 mag at the faint end to ensure an adequate number of sources per bin. The vm[X] values were then fitted with a line to produce a smooth distribution (red line in Fig. 1). Next, we calculate the dispersion, σ[X], as the standard deviation of v[X] values from vm[X] in each magnitude bin. These values were also fitted to produce a smooth distribution of σ[X] values as a function of magnitude (black line in Fig. 1). Making use of both the 3.6 and 4.5 µm data available for each source, we calculate the joint significance parameter, which quantifies the degree of variability of the source in both bands as σ12 = v[3.6] − vm[3.6] σ[3.6] 2 + v[4.5] − vm[4.5] σ[4.5] 2 1 2 (2) where σ12 is essentially the significance level of the variability in each band added in quadrature. Fig. 2 shows the joint significance values for all sources with the thresholds of 3 and 4σ indicated. To quantify the coupling of variances in both the bands, we calculate a variability covariance C and Pearson’s correlation factor MNRAS 476, 1111–1119 (2018)Downloaded from https://academic.oup.com/mnras/article-abstract/476/1/1111/4817554 by sacani@gmail.com on 28 April 2018
  3. 3. Morphologies of mid-IR AGN host galaxies 1113 Table 1. Photometric depth of data. Field PIDa Date Source count Median magnitudeb Number of Number of Number of yyyy-mm-dd [3.6] [4.5] [3.6] (AB) [4.5] (AB) detected sources analysed sourcesc variable sourcesd COSMOS 61043 2010/01/30 20334 19776 23.282 22.225 40635 13673 (34%) 107 (0.8%) 61043 2010/06/18 20857 19633 23.235 23.245 61043 2011/01/31 20049 19341 21.689 21.739 80057 2012/02/11 5812 5249 23.307 23.219 80057 2012/07/03 5674 5804 22.761 23.128 ECDFS 194 2004/02/08 4058 3229 22.874 22.191 76792 18936 (25%) 163 (0.9%) 194 2004/08/13 4399 3125 22.579 22.379 20708 2005/08/23 23417 17413 22.562 22.218 20708 2006/02/09 22203 18161 22.527 22.174 60022 2010/09/24 31680 28366 23.510 23.060 60022 2011/03/28 30676 28557 23.247 23.340 60022 2011/10/15 31648 28831 23.354 23.308 70204 2011/03/21 2838 2778 23.407 23.597 80217 2011/09/27 2085 2012 23.232 23.372 EGS 0008 2003/12/25 30070 25195 23.000 22.928 75217 21471 (29%) 305 (1.4%) 0008 2004/07/01 29476 24459 22.923 23.000 0008 2006/03/28 5845 4537 22.741 22.214 41023 2008/01/25 10798 8266 22.319 21.966 41023 2008/07/23 11181 8821 22.512 21.944 61042 2010/02/06 27856 25803 22.957 22.972 61042 2010/08/05 29047 26028 22.883 23.078 61042 2011/02/10 28085 26155 23.084 22.999 80216 2011/08/19 5181 4626 23.464 23.043 80216 2012/02/23 5711 5213 23.553 22.817 80216 2012/08/31 5004 4658 23.403 22.853 HDFN 00169 2004/05/16 4402 3177 22.640 22.480 57354 12450 (22%) 112 (0.9%) 00169 2004/11/17 4114 3413 22.806 22.393 00169 2005/11/25 1144 1026 22.485 22.597 20218 2005/12/09 4820 2759 22.127 21.805 20218 2006/06/02 3785 3208 22.395 21.932 61040 2010/05/27 23750 22815 22.951 22.893 61040 2011/02/28 24324 20984 22.734 23.142 61040 2011/06/02 24706 22772 22.853 22.914 UDS 40021 2008/01/28 32351 26772 23.132 22.766 67181 21140 (31%) 186 (0.9%) 61041 2009/09/21 32469 29639 22.984 23.373 61041 2010/02/25 31945 30597 23.207 21.539 61041 2010/09/23 32907 29842 23.180 23.294 80218 2012/03/11 4807 4459 22.565 23.422 80218 2012/10/13 4752 4160 21.668 23.162 80218 2013/03/16 3610 3402 23.216 22.790 aSpitzer program identification number; bThe number of sources meeting our criteria of detection in both bands with at least 3 epochs and the percentage of total detected sources this represents; cThe number of variable sources in the field and the percentage of analysed sources this represents; dMedian magnitude of sources detected in this field. r as C = 1 N − 1 (m[3.6]j − m[3.6] )(m[4.5]j − m[4.5] )) (3) r = C v[3.6]v[4.5] . (4) Fig. 3 is a histogram of the correlation factors for all 3 and 4σ variables. The correlation factor r is constrained in the closed inter- val [−1, 1], where −1 shows absolute anti-correlation between the variability in the two bands and +1 shows complete correlation. To ensure a robust selection of true variables above the photomet- ric noise and with highly correlated light curves, we set the criteria for variable galaxies as σ12 ≥ 3 and r ≥ 0.8. The selected galaxies were examined by eye, of which 16 per cent were classified as spu- rious detections due to artefacts like diffraction spikes from nearby foreground stars or close proximity of a source to the edge. Since variability could not be accurately determined for these sources, they were removed from the survey. Fig. 4 shows the distribution of [3.6] mag of all detected sources and all the variability-selected sources in all five fields. The variables span the entire magnitude range of the galaxy survey and extend about 3 mag deeper than the mid-IR variability survey in the SDWFS (Kozłowski et al. 2010, 2016). The results of this analysis are summarized in the last column of Table 1. The number of variables in each field are as fol- lows: 107 (0.8 per cent) in COSMOS, 163 (0.9 per cent) in ECDFS, 305 (1.4 per cent) in EGS, 112 (0.9 per cent) in HDFN, and 186 (0.9 per cent) in UDS. This gives us a total of 873 variables or ∼1.0 per cent of analysed sources in our survey which is in agree- ment with the result from Kozłowski et al. (2010) where the AGN fraction was found to be ∼1.1 per cent. MNRAS 476, 1111–1119 (2018)Downloaded from https://academic.oup.com/mnras/article-abstract/476/1/1111/4817554 by sacani@gmail.com on 28 April 2018
  4. 4. 1114 M. Polimera et al. Figure 1. Standard deviation (equation 1) versus magnitude in the 3.6 µm band of the COSMOS field. The blue dots represent all the sources, the red line represents a fit to the vm [3.6] median values, and the black line repre- sents the 1σ dispersion above the median line. This plot is representative of the other fields in our survey. Figure 2. Joint significance (equation 2) versus magnitude in the 3.6 µm band for all sources in all five fields. The red and black dashed lines represent the 3 and 4σ thresholds. Although our aim is to identify AGN, other sources such as su- pernovae can be detected as variable objects. An optical variability survey by Falocco et al. (2015) identified 0.06 per cent of sources as SNe among ∼33 300 sources with their SNe selection criteria being r ≥ 23 and Nepoch ≥ 6. Given this statistic, the number of SNe in our galaxy survey of ∼87 000 sources should be about 52, which is ∼6 per cent of the variability-selected sources. However, the spec- tral energy distribution models of SNe from Smitka (2016) show that the luminosity of the SNe dips in the mid-IR, suggesting that the fraction of SNe among our mid-IR variability-selected galaxies has an upper limit of 6 per cent and is likely to be even lower than the percentage based on the Falocco et al. (2015) statistics. We estimate the number of spurious detections in our survey using the average number of sources with σ12 ≥ 3 but having correlation factor values r < 0.8. These are sources which have uncorrelated light curves and are unlikely to be true variables. The average number of sources found per bin having r < 0.8 is 84 Figure 3. Histogram of the correlation factor r (equation 4) for all sources with cut-offs as σ12 ≥ 3 (blue) and σ12 ≥ 4 (green). The red and black dashed lines represent the average r number of objects per bin at r < 0.8 for the 3 and 4σ sources, respectively. (red dashed line in Fig. 3). Extrapolating this line to sources with r > 0.8 implies that about 15 per cent of the variables meeting our variability criteria are spurious. This is much smaller than the number of spurious sources found by Kozłowski et al. (2016). They found 34 to 47 per cent false positives among variables identified with the same criteria we use (σ12 > 3 and r > 0.8) among variables having four to five epochs of data. Our estimated percentage of false positives combined with the percentage of sources which may be SNe results in ∼20 per cent of our variables being spurious or non- AGN in nature. Therefore, we estimate ∼80 per cent of the mid-IR variability-selected sources are likely due to the presence of an AGN. It can be seen that the false positive rate cited by Kozłowski et al. (2016) is much higher than our estimated rate. But, the rate drops significantly in Kozłowski et al. (2016) when they consider presumably brighter sources with magnitudes measurable in all four Spitzer bands. These have only a 6 to 7 per cent false positive rate. Therefore, we could say that since our false positive rate falls between that of their two photometry groups, this may indicate that the parameters used for photometry of sources in our survey result in a sample somewhere between these two photometry groups. 4 COMPARISON TO MULTIWAVELENGTH DATA Smaller regions within the SEDS fields have extremely large amounts of multiwavelength data from X-ray to radio wavelengths. For the purposes of our study, we focus on the sub-regions of the fields with deep, HST imaging and well-sampled spectral energy distributions for determining masses, redshifts, and morphological classification of the host galaxies. The Rainbow data base (P´erez- Gonz´alez et al. 2008)1 is a thorough compilation of photometry and spectroscopy for several extragalactic fields which includes portions of the SEDS fields. Multiwavelength photometry is used to deter- mine photometric redshifts as well as stellar mass. We searched for sources in the Rainbow data base within 1 arcsec of sources included in our survey. We cross-referenced both the mid-IR variability- selected sources and the non-varying sources and identified 7146 1 Website: http://rainbow.fis.ucm.es MNRAS 476, 1111–1119 (2018)Downloaded from https://academic.oup.com/mnras/article-abstract/476/1/1111/4817554 by sacani@gmail.com on 28 April 2018
  5. 5. Morphologies of mid-IR AGN host galaxies 1115 Figure 4. Histogram of magnitudes of all sources (blue line) and the variability-selected AGN (green) in all five fields. Variable numbers have been multiplied by 100 to be compared with all sources. sources in COSMOS (28 of which were mid-IR variables), 1992 in ECDFS (13 variables), 5574 in EGS (18 variables), 2054 in HDFN (20 variables), and 5173 in the UDS (25 variables). We further re- quired that the Rainbow sources are within the CANDELS F160W field of view. This is necessary for the morphology classification process (see Section 5). This reduced the number of sources in the ECDFS field to 6, EGS to 17, and HDFN to 19, with the number of sources in the other fields remaining the same. Thus, a total of 95 mid-IR variables were found in the Rainbow data base within the CANDELS field of view, representing 11 per cent of all mid-IR variable sources. A visual inspection of the spectral energy distri- bution fits to model templates indicate that the variable sources are well fitted by the templates and should yield accurate redshifts and masses for the galaxies. To produce a control sample for our morphological study of AGN host galaxies, we require a randomly selected sample of galaxies that are non-varying and therefore not likely to be AGN but whose physical parameters are similar to the AGN host galaxy parame- ters. In particular we use galaxy mass, luminosity, and redshift to constrain the control galaxy population. For each field, we used the upper and lower limits of the mass, redshift, and magnitude ranges to search for sources to constitute our control sample. From the pool of candidates, we chose three random, non-varying galaxies per variable source whose masses fall within the range Mvar/2 < Mcontrol < 2 ∗ Mvar, where Mvar is the mass of the AGN host galaxy displaying variability. Of the 285 control galaxies initially selected from the Rainbow data base, 55 were discarded since they were not within the CANDELS image field of view. Fig. 5 shows the mass versus redshift for all mid-IR sources in our survey with available data in the Rainbow data base (grey points), mid-IR-selected variables (red points), and control galaxies (blue triangles). The distributions of the control and variable samples are well matched in the redshift range between z = 0.25 and 3 and in the mass range between log(mass [M ]) = 8.5 and 11.5. This is further displayed in the histograms of these galaxy samples shown in Figs 6 and 7. The sharp decline in AGN at redshifts beyond z = 3 may be due to the combination of several effects. At these high redshifts, we probe the part of the AGN SED that becomes more strongly dominated by the host galaxy, making us less sensitive to the AGN variability. Additionally, the decrease in surface brightness at high redshift leads to greater incompleteness in the Rainbow data Figure 5. Redshifts versus masses of the galaxies (for which data were available from the RAINBOW data base) in our survey (grey dots), the mid- IR-selected AGN host galaxies (red circles), and the control sample galaxies (blue triangles). Note: The three outlier points lie outside the CANDELS fields and thus have no control galaxies nearby. Figure 6. Normalized histogram showing the masses of all galaxies (for which data were available from the RAINBOW data base) in our survey (blue line), the mid-IR-selected AGN host galaxies (green shaded), and the control sample galaxies (red lines). base. We also find few AGN hosts among the low-mass tail of the galaxy distribution. Since AGN are generally more common among massive galaxies, it is not too surprising that we find very few among the lowest mass galaxies. 5 MORPHOLOGY CLASSIFICATION The aim of our morphology study is to look for evidence of mergers or interactions by examining the physical features of the AGN host galaxies. If AGN accretion is indeed triggered by mergers or in- teractions, we should observe a higher fraction of morphologically disturbed galaxies among our variability-selected sample compared to the control sample. We visually classify 30 × 30 pixel (0.9 × 0.9 arcsec2 ) thumbnails of the AGN hosts extracted from the F160W (1600nm) images, available in the CANDELS (Grogin et al. 2011) Public Access data base. The classification was done using the MNRAS 476, 1111–1119 (2018)Downloaded from https://academic.oup.com/mnras/article-abstract/476/1/1111/4817554 by sacani@gmail.com on 28 April 2018
  6. 6. 1116 M. Polimera et al. Figure 7. Normalized histogram showing the redshift distribution of all galaxies (for which data were available from the RAINBOW data base) in our survey (blue line), the mid-IR-selected AGN host galaxies (green shaded), and the control sample galaxies (red lines). criteria described in more detail in Kocevski et al. (2012). First, the general morphology was classified as disc, spheroid, irregular, or point-like. Secondly, the degree of disturbance was classified as Merger/Interacting (highly disturbed with multiple nuclei or two distinct galaxies with interacting features such as tidal arms), Dis- torted/Asymmetric (single asymmetric or distorted galaxies with no visible interacting companion), Close Pair (near-neighbour pair with both the galaxies in a single frame), Double Nuclei (multi- ple nuclei in a single system) or Undisturbed (none of the above). Fig. 8 shows representative galaxies from our survey in the various morphological classes. We performed a blind inspection, which means that the images of the variable and control galaxies within each field were mixed to avoid any biases during the classification process of the galaxies. The classification was performed by two of the authors of this paper individually (M. Polimera and V. Sarajedini), and the results were merged. For ∼30 per cent of the galaxies, each reviewer had classified them differently and these galaxies were reinspected until a consensus could be reached on the proper classification. We chose to employ this type of visual inspection to resolve the subtle, low surface brightness features which are easy to miss using modern automated classification techniques. However, we hope to use these classifications as training sets for a machine-learning- based classifier in the future. 6 RESULTS Figs 9 and 10 show the visual morphology classification results tabulated in Table 2. The 1σ error bars are calculated as confidence intervals for a binomial population as Error = z1−α/2 p(1 − p) n , (5) where p is the fraction of variable galaxies in the each disturbance category and n is the total number of galaxies (e.g. Cameron 2011). We estimate the 1σ error where the confidence level is c = 0.683, so the variate value from the standard normal distribution z1−α/2 = 1 (α = 1 − c). Fig. 9 shows no significant differences between the AGN host galaxy classifications and those for the control sample in terms of Figure 8. Sample galaxies belonging to each of the classification groups employed in our analysis. The size of each thumbnail is 30 × 30 pixels or 0.9 × 0.9 arcsec2. MNRAS 476, 1111–1119 (2018)Downloaded from https://academic.oup.com/mnras/article-abstract/476/1/1111/4817554 by sacani@gmail.com on 28 April 2018
  7. 7. Morphologies of mid-IR AGN host galaxies 1117 Figure 9. Fraction of variable galaxies (blue diamonds) and control galaxies (red squares) in each of the major morphology classes. Figure 10. Fraction of variable galaxies (blue diamonds) and control galax- ies (red squares) in different disturbance classes. galaxy classified as disc, spheroidal, irregular, or point-like. We also find no difference in the percentage of galaxies in a close pair. We find a similar fraction of AGN hosts in galaxies with a discernible disc (40 ± 10 per cent) as did Kocevski et al. (2012) for an X-ray- selected sample (51.4 ± 5.8 per cent). About 30 ± 10 per cent of the mid-IR variables are found in pure elliptical (spheroidal) hosts. This is also quite similar to that found by Kocevski et al. (2012) for X-ray-selected AGN hosts (27.8 ± 5 per cent). In Fig. 10, we compare the percentage of galaxies displaying signs of mergers/interactions and distortion/asymmetry using the same symbols as Fig. 9. We find that AGN hosts and control galax- ies have the same fraction of merger/interaction hosts within the uncertainties (19 ± 5 to 23 ± 3 per cent). However, AGN hosts have a slightly higher tendency to reside in galaxies that are either distorted or asymmetric (42 ± 5 per cent) compared to the control galaxy population (32 ± 3 per cent). This result is significant at the 1.7σ level. Dividing our sample into high- and low-mass hosts and control galaxies also produced similar results. As previously mentioned, Kocevski et al. (2012) studied 72 X-ray-selected AGN hosts in the GOODS-S field using the same CANDELS NIR images analysed in this study. However, they found no statistical difference between AGN hosts and the control galaxies having strongly disturbed (merger/interaction) or weakly disturbed (distorted/asymmetric) morphologies. One possibility they suggest is that X-ray-selected samples of AGN still miss the most obscured sources which could be hiding hosts displaying merger signatures. A follow-up study by K15 explored this issue by determining the level of obscuration in the X-ray-selected AGN using reflection- dominated X-ray spectroscopic analysis. They found that the more obscured X-ray detected AGN (i.e. those with the highest hydro- gen column densities) had a higher fraction of morphologically disturbed host galaxies than less obscured X-ray detected AGN. Our results indicate that mid-IR variability-selected AGN sam- ples have marginally significant levels of weakly disturbed host galaxy morphologies. This could be interpreted as an indication that our sample includes a significant number of obscured, Compton- thick AGN. In order to test this hypothesis, we cross-referenced our sample of mid-IR variables with X-ray identified AGN in the five survey fields [COSMOS: Elvis et al. (2009); ECDFS: Alexander et al. (2003) and Xue et al. (2011); EGS: Nandra et al. (2015); HDFN: Alexander et al. (2003); UDS: Ueda et al. (2008)]. We ini- tially searched for X-ray sources within 1 arcsec of the position of each mid-IR variable and found that matched sources generally lie within a 0.5 arcsec radius. Setting this as our matching thresh- old, we found 22 of our mid-IR variables are also X-ray detected AGN (23 per cent). This means that 77 per cent of our sources, or 73 mid-IR variables, are not identified in deep X-ray surveys and are likely to be either spurious sources (expected among ∼20 per cent of our sample) or may be highly obscured AGN whose X-ray emission has been absorbed. If these represent the most obscured sources, we may expect to find their morphologies to be the most disturbed. Table 2. Disturbance classification. Galaxy type Disturbance class COSMOS UDS EGS HDFN ECDFS Total Variable Merg/Int 6 (21 ± 8 per cent) 5 (21 ± 8 per cent) 2 (11 ± 7 per cent) 2 (11 ± 7 per cent) 3 (50 ± 20 per cent) 18 (19 ± 4 per cent) Dist/Asy 10 (35 ± 9 per cent) 12 (50±10 per cent) 12 (70 ± 11 per cent) 5 (26 ± 10 per cent) 1 (17 ± 15 per cent) 40 (42 ± 5 per cent) Undisturbed 12 (42 ± 9 per cent) 8 (33 ± 9 per cent) 3 (17 ± 9 per cent) 12 (63 ± 11 per cent) 2 (33 ± 19 per cent) 37 (39 ± 5 per cent) Total 28 25 17 19 6 95 Control Merg/Int 10 (22 ± 6 per cent) 17 (26 ± 5 per cent) 12 (25 ± 6 per cent) 11 (20 ± 5 per cent) 7 (35 ± 10 per cent) 57 (25 ± 3 per cent) Dist/Asy 17 (37 ± 7 per cent) 22 (35 ± 6 per cent) 13 (27 ± 6 per cent) 19 (34 ± 6 per cent) 3 (15 ± 7 per cent) 74 (32 ± 3 per cent) Undisturbed 17 (37 ± 7 per cent) 25 (39 ± 6 per cent) 22 (46 ± 7 per cent) 25 (45 ± 7 per cent) 10 (50 ± 11 per cent) 99 (43 ± 3 per cent) Total 45 63 47 55 20 230 ∗ The galaxies used to calculate these percentages are only those that are present in the RAINBOW data base for which the visual morphology classification described in Section 5 has been performed. MNRAS 476, 1111–1119 (2018)Downloaded from https://academic.oup.com/mnras/article-abstract/476/1/1111/4817554 by sacani@gmail.com on 28 April 2018
  8. 8. 1118 M. Polimera et al. Figure 11. Fraction of mid-IR variable galaxies with an X-ray counterpart (green diamonds) and those with no X-ray counterpart detected within 0.5 arcsec (black diamonds) in different disturbance classes. In Fig. 11, we compare the fraction of host galaxies of different morphological classifications for the mid-IR variables that are also identified in X-ray surveys (green diamonds) to the mid-IR variable sources without an X-ray counterpart (black diamonds). We see that while the two samples have the same, low fraction of highly dis- turbed host galaxies, a much higher fraction of mid-IR-only sources have weakly disturbed hosts (48 ± 5 per cent) than those with X-ray counterparts (23 ± 8 per cent). This difference is significant at the 3σ level. When we compare these percentages with those of the most highly obscured, Compton-thick X-ray sources from K15, we find that they agree favourably. We find that 22 ± 5 per cent of mid- IR-only-selected AGN have highly disturbed host morphologies compared to 21.5+4.2 −3.3 per cent of the most obscured X-ray sources in K15. Furthermore, 40 ± 5 per cent of mid-IR-only-selected AGN have weakly disturbed hosts compared to 43.0+4.6 −4.4 per cent in K15. This further supports the claim that AGN selected via mid-IR vari- ability are likely to contain a larger fraction of obscured AGN, pos- sibly more obscured than the most absorbed X-ray-selected sources, revealing a higher fraction of asymmetric hosts and highlighting the unique nature of this selection technique. 7 DISCUSSION It has been speculated that mergers tidally induce non-axis- symmetric modes in the merging galaxy and these modes sweep the interstellar gaseous medium into a disc with a radius of a few par- sec around the central SMBH (Shlosman, Frank & Begelman 1989; Shlosman, Begelman & Frank 1990). The disc then is accreted on to the SMBH. Simulations from Barnes & Hernquist (1992) have shown that these discs are more likely to form in a major head-on merger scenario. Given such a theory, we may expect to see the AGN hosts displaying a higher fraction of major mergers as com- pared to the control group. Our results, however, do not show a sig- nificant fraction of strong interaction signatures among the mid-IR variability-selected AGN hosts. We do find a marginally significant increase in the presence of weak interaction signatures in the host galaxies when compared to a control sample and the significance increases when we remove X-ray-selected AGN and isolate mid-IR variability-selected AGN. One explanation is that during the entire process of a galaxy merger, the galaxies spend more time in stages where they appear to be weakly disturbed versus having a highly disturbed morphology. Based on recent simulations by Capelo et al. (2015), the time a galaxy spends in the weakly disturbed phases is about 7.9 Gyr, but the system shows a highly disturbed morphology only for about 4.6 Gyr. This makes it less likely to ‘catch’ the galaxy in the highly disturbed phase because their signatures dissipate too fast and we see less dramatic distortions in the hosts. Another possibility is that the dominant AGN triggering mecha- nism is not major mergers but that minor mergers are more impor- tant for AGN to begin accretion as the perturbation is sufficient for the orbits of material in the central part of the galaxy to get ran- domized and disturbed just enough to infall into the central SMBH (Hernquist & Mihos 1995; Menci et al. 2014). Hernquist & Mihos (1995) also point out that the occurrences of major mergers are more unusual as compared to minor mergers/interactions. Yet another possibility is that the low fraction of major merger signatures seen in our AGN hosts is due to heavy obscuration which would absorb the variable UV photons from the accretion disc and weaken the mid-IR variable flux necessary for our selection method. As shown in K15, there is a significant increase in the fraction of AGN host galaxy morphology disturbance as a function of obscu- ration, with the Compton-thick AGN hosts showing the highest fraction of disturbed morphologies compared to unobscured hosts. Our study found that the host galaxies of mid-IR variables have the same fraction of disturbed morphologies as the most obscured X-ray-selected sources. It is possible that higher levels of distur- bance are hidden by extreme obscuration such that neither X-ray nor mid-IR variability is able to detect the AGN nature of the source. 8 CONCLUSION The results of our study are summarized as follows. (i) 1 per cent of all galaxies with [3.6] < 24.5 are significantly variable in the mid-IR Spitzer/IRAC mosaics with 80 per cent like- lihood of being AGN. (ii) The AGN host galaxies show a marginally sig- nificant trend (1.7σ significance) towards higher fractions of Disturbed/Asymmetric morphologies (42 ± 5 per cent) as compared to the control sample (32 ± 3 per cent), whereas Merger/Interaction fractions are not statistically different for the AGN and control galaxies. (iii) 23 per cent of mid-IR variables are also identified in X-ray surveys. The mid-IR variables without an X-ray counterpart show a higher fraction of weakly disturbed hosts than those also identified with X-ray emission. This suggests that mid-IR variability selects a unique population of obscured AGN that would likely be missed using other selection techniques. ACKNOWLEDGEMENTS This work is based on observations made with the Spitzer Space Telescope, which is operated by the Jet Propulsion Laboratory, Cal- ifornia Institute of Technology under a contract with NASA. This work has also made use of the Rainbow Cosmological Surveys data base, which is operated by the Universidad Complutense de Madrid (UCM), partnered with the University of California Observatories at Santa Cruz (UCO/Lick,UCSC) and used observations taken by the CANDELS Multi-Cycle Treasury Program with the NASA/ESA HST, which is operated by the Association of Universities for Re- search in Astronomy, Inc., under NASA contract NAS5-26555. MNRAS 476, 1111–1119 (2018)Downloaded from https://academic.oup.com/mnras/article-abstract/476/1/1111/4817554 by sacani@gmail.com on 28 April 2018
  9. 9. Morphologies of mid-IR AGN host galaxies 1119 REFERENCES Alexander D. M. et al., 2003, AJ, 126, 539 Ashby M. L. N. et al., 2013, ApJ, 769, 80 Ashby M. L. N. et al., 2015, ApJS, 218, 33 Asmus D., H¨onig S. F., Gandhi P., Smette A., Duschl W. J., 2014, MNRAS, 439, 1648 Assef R. J. et al., 2010, ApJ, 713, 970 Barnes J. E., Hernquist L., 1992, ARA&A, 30, 705 Bertin E., Arnouts S., 1996, A&AS, 117, 393 Cameron E., 2011, PASA, 28, 128 Capelo P. R., Volonteri M., Dotti M., Bellovary J. M., Mayer L., Governato F., 2015, MNRAS, 447, 2123 Elvis M. et al., 2009, ApJS, 184, 158 Falocco S. et al., 2015, A&A, 579, A115 Grogin N. A. et al., 2011, ApJS, 197, 35 Hernquist L., Mihos J. C., 1995, ApJ, 448, 41 Hopkins P. F., Kocevski D. D., Bundy K., 2014, MNRAS, 445, 823 Kocevski D. D. et al., 2012, ApJ, 744, 148 Kocevski D. D. et al., 2015, ApJ, 814, 104 (K15) Kochanek C. S. et al., 2012, ApJS, 200, 8 Koekemoer A. M. et al., 2011, ApJS, 197, 36 Kozłowski S. et al., 2010, ApJ, 716, 530 Kozłowski S., Kochanek C. S., Ashby M. L. N., Assef R. J., Brodwin M., Eisenhardt P. R., Jannuzi B. T., Stern D., 2016, ApJ, 817, 119 Lacy M. et al., 2004, ApJS, 154, 166 Lynden-Bell D., 1969, Nature, 223, 690 MacLeod C. L. et al., 2010, ApJ, 721, 1014 Menci N., Gatti M., Fiore F., Lamastra A., 2014, A&A, 569, A37 Morse J. A., Raymond J. C., Wilson A. S., 1996, PASP, 108, 426 Nandra K. et al., 2015, ApJS, 220, 10 Park S. Q. et al., 2008, ApJ, 678, 744 P´erez-Gonz´alez P. G. et al., 2008, ApJ, 675, 234 Richards G. T. et al., 2002, AJ, 123, 2945 Ruan J. J., Anderson S. F., Dexter J., Agol E., 2014, ApJ, 783, 105 Sarajedini V., Koo D., Klesman A., 2009, Am. Astron. Soc. Meeting Abst. #213. 238 Shlosman I., Frank J., Begelman M. C., 1989, Nature, 338, 45 Shlosman I., Begelman M. C., Frank J., 1990, Nature, 345, 679 Smitka M. T., 2016, PhD thesis, Texas A&M University Stern D. et al., 2005, ApJ, 631, 163 Ueda Y. et al., 2008, ApJS, 179, 124 Urry C. M., Padovani P., 1995, PASP, 107, 803 Vazquez B. et al., 2015, ApJ, 801, 127 Veilleux S., 2002, in Green R. F., Khachikian E. Y., Sanders D. B., eds, ASP Conf. Ser. Vol. 284, IAU Colloq. 184: AGN Surveys. Astron. Soc. Pac., San Francisco, p. 111 (astro-ph/0201118) Xue Y. Q. et al., 2011, ApJS, 195, 10 This paper has been typeset from a TEX/LATEX file prepared by the author. MNRAS 476, 1111–1119 (2018)Downloaded from https://academic.oup.com/mnras/article-abstract/476/1/1111/4817554 by sacani@gmail.com on 28 April 2018

×