arXiv:1706.07436v2 [astro-ph.GA] 8 Dec 2017 ecoeyte otednmcltm ftemre system. merger the words: of Key time dynamical the s to further tied we closely and be galaxies, non-interacting evolving activity AGN ularly luminous Our trigger average, galaxies. on do, non-interacting interactions over systems merging in side aaisudron egr r factor a are mergers undergoing galaxies signific galaxie statistically non-interacting select and to mergers/irregular-systems, machi us novel allowing a technique using tree signatures interaction galaxy identify edtrieta h otlmnu G ouain( population AGN luminous most the that determine we G hnnnitrcigglxe,adti sindependent < is z this and galaxies, non-interacting than AGN IEAlSysre,w outyslc cieglci nuc totaling galactic samples, active galaxy select trol g robustly last of we survey, the importance All-Sky throughout the WISE BHs on growing constraints in new (1:4) our present we Survey, ehns o uln uemsiebakhls(H.Harn (BH). holes black supermassive ( ne fueling resolution of for production rapid mechanism the a causing evolution, are and galaxies formation gas-rich between interactions and Collisions Abstract Accepted ; Received ∗ 3 2 1 rgeigado rsneo crtn uemsieblac supermassive accreting of presence th and and/or interactions triggering galaxy–galaxy between connections The Introduction 1 G D. Andy Hyper Survey the Suprime-Cam from view unprecedented an Hole Growth: Black Rapid Trigger Interactions Galaxy
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Publ. elann admfrs decision forest random ne-learning fteHprSpieCm(HSC) Suprime-Cam Hyper the of y etnHl,Pictn NJ Princeton, Hall, Peyton ty, ting fbt tla asadrdhf to redshift and mass stellar both of e AN n asmthdcon- mass-matched and (AGN) lei nig hwta galaxy–galaxy that show findings tr,adpsil evn as serving possibly and stars, w & iona at rz 16High 1156 Cruz, Santa lifornia, 10 EZANSON st,Mtuaa Ehime Matsuyama, rsity, 45 lx-aaymjrmergers major alaxy-galaxy sigteeqiiespatial exquisite the essing r s erg ATSUOKA smthdsamples, ss-matched − o:10.1093/pasj/xxx000 doi: 1 1 ytmtclyre- systematically ) Johnny , < i 21)0() 1–26 00(0), (2017) 22 3 Elinor , a.We mag. pect of - 1 2 Publications of the Astronomical Society of Japan, (2017), Vol. 00, No. 0 a connection between the encounters of two (or more) gas-rich Schawinski et al. 2011; Kocevski et al. 2012). Similarly, at galaxies with similar (1:< 4 5) stellar masses, and the accre- z < 1, large-scale galaxy interaction signatures such as merg- − tion of material onto at least one of the BHs present in these ers, and galaxy-scale bars and instabilities, do not appear to systems (e.g., Volonteri et al. 2003; Hopkins et al. 2005; Di significantly boost the likelihood of hosting a lower luminos- 43 −1 Matteo et al. 2005; Springel et al. 2005). Concurrent BH growth ity AGN (LAGN . 10 erg s ; Athanassoula 1992; Ho et al. and the rapid production of new stars (e.g., Somerville et al. 1997; Regan & Mulchaey 1999; Cisternas et al. 2015; Cheung 2008; Angl´es-Alc´azar et al. 2013) can naturally give rise to et al. 2015; Goulding et al. 2017). By contrast, others find ev- known correlations between the BH mass and galaxy proper- idence supporting a correlation between merging and AGN in ties, such as the bulge mass and stellar velocity dispersion (e.g., some more nearby galaxies (e.g., Koss et al. 2010; Ellison et al. Magorrian et al. 1998; Ferrarese & Merritt 2000; Tremaine 2011b; Silverman et al. 2011; Ellison et al. 2013; Satyapal et al. et al. 2002; G¨ultekin et al. 2009; McConnell & Ma 2013). 2014a; Hong et al. 2015; Weston et al. 2017). Furthermore, self-regulation of the AGN activity, due to so- Typically, studies have focused on selecting large samples called ‘quasar mode’ feedback processes, can serve as a violent of AGN, and then comparing the host galaxies of these AGN mechanism capable of disrupting the on-going star-formation to non-AGN systems. However, AGN activity is a stochastic by depositing energy back into the galaxy merger, heating the process that is believed to vary on timescales far shorter than gas and/or even expelling material out into the wider dark mat- changes related to galaxy-wide processes (e.g., morphology, ter halo. Indeed, AGN feedback is a widely accepted solution star-formation). AGN variability may therefore cause dilution for the formation of massive quiescent early-type galaxies, and of would-be significant correlations between average BH accre- the build-up of the red-sequence, in cosmological simulations. tion and on-going star-formation (e.g., Chen et al. 2013; Hickox A particularly persuasive argument for a connection between et al. 2014) and/or stellar mass (Yang et al. 2017). Further com- the most rapidly growing BHs and major-mergers is that galaxy plicating the observational view-point, the importance of galaxy interactions provide a simple solution to the ‘angular momen- interactions for triggering AGN may also be obscuration depen- tum problem’. In principle, growing a BH requires only a dent, as well as merger-stage dependent (e.g., Kocevski et al. source of cool gas to fuel the nucleus, supplies of which are 2015; Koss et al. 2016; Weston et al. 2017; Ricci et al. 2017). typically plentiful in the host galaxy. However, continuously More often than not, these previous investigations were ham- transporting significant quantities of this material from the gas pered by the ability to sample significant numbers of AGN and reservoirs in the host, down to scales in which it can accrete mergers that cover large dynamic ranges in AGN luminosity, onto the BH, while simultaneously dissipating the specific an- and for the more high-redshift studies, the ability to accurately gular momentum of the gas, is a non trivial issue. Models identify AGN or robustly detect the presence of galaxy inter- of galaxy–galaxy mergers show that tidal forces between the action signatures. Progress in the field can therefore be made galaxies can cause gas to be subject to substantial gravitational by bridging the gap between the low and high-redshift studies, torques, resulting in the efficient loss of angular momentum, through the construction of large samples of merging and non- ultimately causing substantial gas flow towards the BH, and ig- merging galaxies with deep high-resolution imaging to z 1, ∼ niting a powerful AGN (e.g., Barnes & Hernquist 1991; Mihos which simultaneously encompass statistically significant popu- & Hernquist 1996; Di Matteo et al. 2008; Angl´es-Alc´azar et al. lations of moderate to extremely luminous AGN. 2017). Despite the theoretical successes of AGN–galaxy co- Using dedicated telescopes, wide-format surveys such as the evolution models, observational evidence for a connection be- Sloan Digital Sky Survey (SDSS) carried out comprehensive tween merging galaxies, galaxy instabilities, and the enhanced multi-band imaging surveys of significant fractions of the sky. presence of AGN activity, is still inconclusive. These surveys have been incredibly successful in characteriz- 46 −1 Recent observations of luminous (LAGN > 10 erg s ) ing the properties of extremely large galaxy/AGN samples (e.g., dust reddened z 1–2 quasars have revealed these AGN to be Strateva et al. 2001; Vanden Berk et al. 2001; Strauss et al. ∼ overwhelmingly hosted by galaxy mergers (e.g., Urrutia et al. 2002; Eisenstein et al. 2005; Ross et al. 2013). Owing to mir- 2008; Glikman et al. 2012, 2015), possibly suggesting that the ror size and total integration times these surveys were neces- most luminous BH growth is increasingly likely to be trig- sarily limited to the relatively nearby Universe (z< 0.2), while gered by galaxy interactions (e.g., Treister et al. 2012; Fan et al. still encompassing large survey volumes of V 0.2(h−1Gpc)3. ∼ 2016). However, others do not observe a rise in the incidence Following in the footsteps of SDSS, the next generation of of mergers at the highest AGN luminosities (e.g., Schawinski wide-format imaging surveys, capable of providing SDSS-like et al. 2012; Villforth et al. 2014). Furthermore, at similar red- volumes and imaging quality out to z = 1 are beginning to 43 shifts, AGN with more moderate luminosities (LAGN 10 take shape. The on-going Hyper Suprime-Cam (HSC) survey ∼ − 1044 erg s−1) also appear no more likely to show interaction (Aihara et al. 2017b) is now providing an unprecedented new signatures than non-AGN systems (e.g., Cisternas et al. 2011; view of the Universe. The combination of the wide field of view Publications of the Astronomical Society of Japan, (2017), Vol. 00, No. 0 3 and large 8.2 meter mirror diameter provided by the Subaru Telescope gives the HSC survey exquisite sensitivity and re- solving power. Upon completion, the Wide survey layer of HSC will image 1400 deg2 in grizy to a depth of i 26 mag and ∼ ∼ with a typical i-band seeing of 0.5′′, less than half that of the ∼ median seeing in SDSS ( 1.4′′). Given that the angular diame- ∼ ter increases by only a factor 2.5 from z = 0.2 to z = 1, HSC ∼ is now allowing the exploration of galaxy morphologies with SDSS-like precision in SDSS-like survey volumes out to z 1. ∼ Here we harness the unprecedented sensitivity of the first 170 deg2 of the HSC survey combined with complementary all-sky data available from Wide-field Infrared Survey Explorer (WISE; Wright et al. 2010) to explore the incidence of mid- infrared (mid-IR; λ 3–100µm) identified AGN in merging ∼ galaxies out to z 0.9 as a function of the AGN host galaxy ∼ properties. In Section 2 we define our spectroscopic sample of massive galaxies that have been observed as part of the HSC survey. In Section 3, we outline the modeling of the spec- tral energy distributions for the sample to determine their rest- frame photometry and intrinsic properties, in order to match our galaxy sample in color and stellar mass, and we utilize the ALLWISE catalog to identify those galaxies containing lu- minous AGN. In Section 4, we describe our novel implemen- tation of a machine learning algorithm to identify interacting and non-interacting galaxies by harnessing the HSC imaging. In Section 5 we present the incidence of AGN in interacting and non-interacting galaxies, finding that AGN are, on aver- Fig. 1. Top: Distribution of spectroscopically identified galaxies in our par- age, at least a factor & 3 more abundant in merging systems, ent sample as a function of spectroscopic survey (SDSS-II Legacy, SDSS-III and the most luminous AGN at fixed stellar mass are preferen- BOSS, PRIMUS, VIPERs, GAMA and Wiggle-Z). Middle: Fractional con- tribution of a given spectroscopic survey to our parent galaxy sample as a tially found in merging galaxies. In Section 6 we discuss the function of redshift for all galaxies within our parent sample, and for the star- implication of our results, and outline a framework, linking the forming galaxy sample after applying the UVJ cut shown in Fig 3. Bottom: observed AGN fractions to the dynamical time of the merger Spectroscopic sample as a function of i-band magnitude. system. Our concluding remarks are presented in Section 7. All magnitudes are in the AB system, unless otherwise stated. The HSC survey consists of three survey layers: the Wide Throughout we assume a standard flat CDM cosmology with Λ 2 −1 −1 layer covers a solid angle of 1400 deg in grizy filters to a H0 = 70 km s Mpc and ΩM = 0.3. depth of r 26 mags (5σ, point source). The 27 deg2 Deep ≈ layer reaches r 27 mags, with the addition of three narrow- ≈ band filters at λ 3870,8160 and 9210A,˚ and the 3.5 deg2 2 Sample Selection in the HSC Survey ∼ Ultradeep layer is a further 1 mag fainter than Deep, al- 2.1 The Hyper Suprime-Cam Survey ≈ lowing detection of Lyα emitters to z 7. In addition, the ≈ The Hyper Suprime-Cam (HSC) Survey is an ambitious 300 HSC Survey fields were carefully constructed to overlap with night imaging survey undertaken as part of the Subaru Strategic existing multi-wavelength survey fields, e.g., millimeter data Program (SSP; Aihara et al. 2017b). The HSC survey is de- from the Atacama Cosmology Telescope (ACT); X-ray data signed to provide nested wide-field multi-band imaging over a from multiple XMM-Newton and Chandra; near-/mid-infrared total of 1400 deg2 using the HSC instrument on the Subaru imaging surveys such as VIKING/VIDEO; UKIDSS; Spitzer ∼ 8.2m telescope on Mauna Kea in Hawaii. HSC is constructed and WISE; and optical spectroscopic surveys such as SDSS of 116 (104 science detectors) Hamamatsu Deep Depletion Legacy/BOSS, PRIMUS (Coil et al. 2011), VIPERS (Guzzo 2K 4K CCDs, with a 1.77 deg2 field-of-view (FOV) and has an et al. 2014), GAMA ((Liske et al. 2015), Wiggle-Z (Drinkwater × ′′ instrumental Point-Spread Function (PSF) of D80 <0.2 (80% et al. 2010), COSMOS (Lilly et al. 2009), and DEEP2 (Newman enclosed light fraction) over the entire FOV across all imaging et al. 2013). For the specific region centers of the three layers filters. that make up the HSC-SSP survey, we refer the reader to Aihara 4 Publications of the Astronomical Society of Japan, (2017), Vol. 00, No. 0 et al. 2017b. 2.3 Selecting bright (i< 22 mag) galaxies in HSC The data used throughout this manuscript is based on an in- In this section we describe our selection techniques in or- ternal release of the Wide layer data, release S16A, and covers der to construct samples of interacting and non-interacting ∼ 170 deg2. Basic data processing, including bias and background galaxies with firm spectroscopic redshifts. In Section 5, we subtraction, flat-fielding, astrometric calibration, individual ex- use these galaxy samples to assess the importance of galaxy posure co-addition, and object detection was performed using interactions on the growth of BHs to z < 1. Our parent hscPipe v4.0.1, which is an HSC-specific derivative of the galaxy sample contains all objects with i < 22.3 Kron mag- Large Synoptic Survey Telescope (LSST) processing pipeline. nitudes in the S16A data release of the HSC survey. We For further details regarding hscPipe and the HSC-SSP data set the detect is tract inner, detect is patch inner, releases, see Bosch et al. (2017) and Aihara et al. (2017a). For detect is primary and is extended data flags on the sam- our analyses we use the source catalogs, images, and other rel- ple, as we require the most complete galaxy sample available evant data derived from the co-added HSC images produced by within the HSC database1. Our choice of flux limit derives hscPipe. The image co-adds are shifted to a common World from our ability to (1) recover the source morphologies and ro- Co-ordinate System (WCS), and have a pixel scale of 0.168′′ . bustly identify interacting galaxy features such as disturbances, irregular morphologies, tidal tails and bridges out to z < 0.9 (a detailed analysis of the HSC galaxy morphologies and com- 2.2 Outline of this manuscript parison to Hubble Space Telescope data will be presented in a future publication; Goulding et al. in prep.), (2) the complete- Our primary goal is to constrain the effects of gas-rich merging ness of spectroscopic catalogs within the survey regions; and of galaxies on the growth of BHs out to z . 1. To achieve this (3) the addition of a systematic uncertainty of 0.3 mags due ± we require: to difficulty in measuring photometry of merging systems. 1. deep, high spatial resolution optical imaging (from HSC) Each source extracted from the HSC database is then cross- ′′ of a large parent sample of galaxies spectroscopically con- matched to within < 1 with the publicly available spectro- scopic redshift (spec- ) catalogs pertaining to the survey sky- firmed to be in the redshift range 0.1 veys, and our ability to accurately determine the morphological iSDSS,Petro > 20.5 magnitudes, we supplement the observed- classifications, and identify low-surface brightness tidal-tails of frame optical data with HSC grizy Kron-magnitude photome- the systems from the WIDE-depth (i<25.9 mag) HSC imaging try. at higher redshifts. Typically, extended sources with i & 20.5 mags have HSC i- In Figure 1 we present the breakdown of the spectroscopic band photometry that is consistent ( 0.05 mags) with the pho- ± redshift distributions for our parent sample as a function of the tometry from SDSS. However, some sources with iSDSS,Petro > redshift survey from which the spec-z originated, and as func- 20.5 mag still have significantly discrepant photometric mea- tion of the source brightness in the i-band. It is clear that at surements between SDSS and HSC ( iHSC,kron iSDSS,Petro > | − | any given redshift our total sample is dominated by 2–3 of the 0.15 mags). Such a difference between the SDSS and HSC redshift surveys. For example, at lower redshifts (z< 0.3), our photometry is well beyond the typical statistical uncertainty sample is mainly composed of objects selected from the SDSS- quoted for the photometry in either survey (σSDSS 0.09; ∼ Legacy and GAMA surveys, while at redshifts 0.4 Fig. 2. Examples of FAST produced spectral energy distribution (SED) fits while harnessing available photometry SDSS (ugriz; filled circles), HSC (grizy; filled stars), UKIDSS LAS Y JHK (filled triangles) and WISE (W 1W 2; filled squares) and spectral redshifts. SED fits are shown with (solid line) and without (dashed line) the inclusion of the HSC grizy photometry. Top panel provides the normalized filter response used throughout our analysis. Lower panel provides the stacked rest-frame SEDs (solid lines) and the 1-σ spread in the SED models (dotted-lines) for the obscured-AGN (red) and the star-forming galaxies (blue) in three bins of stellar mass: log(M∗/M⊙) ∼ 10,10.5 and 11.0. Publications of the Astronomical Society of Japan, (2017), Vol. 00, No. 0 7 the WISE mid-IR photometry to build our AGN sub-samples using WISE color–color diagnostics. We use the publicly available IDL code, FAST (Kriek et al. 2011) to model the optical–IR SED of each object to derive physical properties, as well as the appropriate k-corrections re- quired to produce rest-frame photometry for each galaxy. FAST searches over a grid of models and uses χ2-statistics to de- termine the best solution. Throughout the fitting we assume an exponentially declining star-formation history with SFR ∼ exp[ t/τ] and a characteristic time-scale of log(τ) = 7.0 − − 10.0 yr, a Chabrier initial mass function, assuming stellar ages in the range, log(age) = 8 10.1, and the high-resolution stel- − lar population synthesis models of Bruzual & Charlot (2003). Fig. 3. Rest-frame U–V versus V–J diagrams for all spec-z galaxies in our Furthermore, we use a Calzetti et al. (2000) dust reddening parent sample. Rest-frame AB photometry is derived from best-fit SED tem- plates produced from FAST. Column panels: galaxy sample split in three curve, and allow for extinction in the range AV = 0.2 4.0 and − redshift bins, 0.1 Fig. 4. WISE infrared [3.4]–[4.6] versus [4.6]–[12.0] Vega magnitude color-color diagram of HSC galaxies with WISE counterparts. All objects have detections in the [3.4] and [4.6] bands with S/N& 4 and in the [12.0] band with S/N& 2.5. Green dotted box shows the 2-color AGN selection region of Mateos et al. (2012) and black dashed line the 1-color AGN selection cut of Stern et al. (2012). Panels columns are split by redshift, using the same cuts as in Fig. 3, rows indicate galaxies separated according to their position in the UVJ diagram. In the 0.1 HSC spec-z sample are detected in 3-bands using WISE with Polletta et al. (2007) in our considered redshift range. These our S/N cuts, and an additional 44,389 galaxies are detected in tracks typically lie below or outside of the AGN selection re- only [3.4] and [4.6] WISE bands. gions used throughout, and hence, contamination to our AGN selection from non-AGN interlopers is likely to be minimal (see We use the two-color IR-AGN wedge defined by Mateos also Goulding et al. 2014). et al. (2012) to identify [3.4], [4.6] and [12.0] detected ob- Of the 41,990 galaxies in our matched HSC–WISE 3-band jects that have powerlaw-like continua, indicative of the pres- ∼ sample, 3,125 are selected as AGN using the Mateos et al. ence of a radiatively-efficient AGN. For galaxies that are not (2012) WISE selection method. An additional 665 galaxies are detected in the 3 WISE bands considered in Fig. 4, we also use selected as AGN using the Stern et al. single color cut i.e., a the single color cut of Stern et al. (2012) to identify additional total of 3,790 WISE-selected AGN. We find that if we separate AGN (dashed line in Fig 4). This has the advantage of allow- the galaxies based on their position in the UVJ diagram, the ing us to boost source statistics due to the relative insensitiv- WISE-AGN are over-whelmingly hosted in star-forming galax- ity of the longer wavelength WISE bands, as well as including ies. Indeed, only 4% of the WISE-AGN in our spec-z sample the abundance of heavily obscured AGN that reside in Ultra- ∼ are hosted in quiescent galaxies. This clear separation of mid-IR Luminous IR galaxies with [4.6]–[12.0]> 3.5 that may other- AGN residing in star-forming galaxies over quiescent galaxies wise be excluded by the 2-color wedge. Mid-IR AGN selections serves to highlight the previously observed connection between are suspected to be contaminated by low-metallicity strongly star-formation rate and BH accretion rate (e.g., Chen et al. 2013; star-forming dwarf galaxies in the low-redshift universe (e.g., Hickox et al. 2014). Hainline et al. 2016), and by hot strongly dust-obscured galax- ies beyond z > 2. In Fig. 4, we additionally show simulated Unlike more traditional color–magnitude diagrams (Strateva color-color tracks that are derived from the SED templates of et al. 2001; Baldry et al. 2004), the separation of star-forming 10 Publications of the Astronomical Society of Japan, (2017), Vol. 00, No. 0 and quiescent galaxies through UVJ diagnostics is relatively in- emission and broad-line region hidden from the line-of-sight by sensitive to dust extinction in the host. As a result, dusty star- an optically thick torus surrounding the central BH. However, forming galaxies are still robustly identified using UVJ while as this torus isotropically reradiates the AGN emission at IR they may otherwise be classified as quiescent or green-valley wavelengths, a mid-IR AGN selection results in a mixture of systems using color–magnitude diagrams. The observed sep- both Type-1 and Type-2 AGN. While the emission from both aration of AGN in Fig. 4 using UVJ may explain why many these AGN populations dominate their SEDs at mid-IR, the AGN have previously been believed to be an interesting popu- characteristic tail of the AGN accretion disk, which is typically lation of transitioning ‘green-valley’ galaxies that lie between observed in the UV/optical, remains absent for only the Type- the blue cloud and red sequence (Bell et al. 2004; Faber et al. 2 AGN. Hence, studies have revealed that a simple observed- 2007; Nandra et al. 2007; Hasinger 2008; Silverman et al. 2008; frame optical–IR color cut reliably separates unobscured Type- Mendez et al. 2013). In reality, it would appear that these lu- 1 AGN from their obscured counterparts (see Hickox et al. minous mid-IR AGN are merely hosted in dusty star-forming 2007; Hickox et al. 2011; Chen et al. 2015). systems with reddened optical colors. In Fig. 5 we present the distributions of our mid-IR se- The lack of mid-IR AGN observed in quiescent galaxies lected AGN sample in their observed-frame iSDSS [4.6]WISE − does not suggest that there are no accreting BHs in these sys- color. In a similar vein to Hickox et al. (2007), we find that tems. The vast majority of radio-loud AGN are known to be these optical–IR colors can be characterized by two distinct hosted in massive quiescent galaxies (e.g., Best et al. 2005; Gaussian distributions, peaking at iSDSS [4.6]WISE 0.5 and − ∼ Hickox et al. 2009; Goulding et al. 2014; Delvecchio et al. 1.9. Similar to Hickox et al. (2007), we cut our AGN sam- 2017), though the majority of these radio AGN lack the sig- ple into obscured and unobscured sub-samples using optical–IR natures of a radiatively efficient accretion disk, which would be color. We use a cut of iSDSS [4.6]WISE = 1.1, which is based − observed in the mid-IR. Furthermore, in galaxy group or cluster on the intersection of the Gaussian distributions. This serves environments, evidence of AGN feedback due to radio emission to maximize the number of AGN with the correct Type-1/2 from the BH present in the brightest cluster galaxy (so called, classification, while minimizing contaminants. We find 2,552 maintenance mode feedback) has long been established (e.g., and 1,238 sources with optical–IR colors that are red-ward and Best et al. 2005; Rafferty et al. 2006; McNamara & Nulsen blue-ward, respectively, of our iSDSS [4.6]WISE = 1.1 cut. − 2007; Kauffmann et al. 2008; Fabian 2012). In these systems, Inspection of the SDSS spectroscopy for a subset of the AGN powerful radio lobes inject mechanical energy back into the in- with iSDSS [4.6]WISE . 1.1 confirms the presence of broad − tracluster medium, which in turn prevents the efficient cooling Hβ, Hγ emission lines and/or a strong blue disk continuum. gas, and are believed to be responsible for restricting the forma- We further show in Fig. 5 that the obscured and unob- tion of new stars in quiescent galaxies. scured AGN do not follow similar distributions when con- Given the apparent paucity of mid-IR AGN in quiescent sidered in optical–IR color versus absolute i-band magnitude galaxies, the contribution of these systems to the rapid growth space. There is an additional population of low redshift, low- of BHs must be negligible in comparison to the AGN present luminosity (Mi > 20.5 mags), extremely blue objects with − in star-forming galaxies. Hence, for all further analyses pre- iSDSS [4.6]WISE . 0.5 that are not mirrored in the obscured − − sented here, we neglect the inclusion of quiescent galaxies in AGN population. These may be a set of AGN hosted in very our spec-z sample, as identified using the UVJ diagnostic dia- low-mass galaxies (e.g., Satyapal et al. 2014b; Secrest et al. gram, due to the systematic lack of mid-IR AGN in these sys- 2015; Sartori et al. 2015) or a population of low-metalicity blue 9 tems. Furthermore, by removing relatively quiescent systems dwarf galaxies (M∗ . 5 10 M⊙) with powerful young star- × through our UVJ selection, our morphological analyses that are burst regions. These starbursts produce red colors in WISE that designed to identify merging features (described in Section 4) are similar in practice to emission from AGN (e.g., Hainline are not subject to degeneracies arising from the existence of et al. 2016). extremely long-lived stellar shells that are readily identified in By contrast, there appears to be a population of luminous early-type systems located within dense environments, and are obscured AGN at Mi . 23.8 mags that are not present in − unrelated to gas-rich mergers. our Type-1 AGN sample. At these luminosities, Type-1 AGN will most likely saturate the HSC detector for relatively nearby 3.3.3 Separation of Obscured & Unobscured AGN systems and/or appear similar to bright point sources at higher AGN identifications made at mid-IR wavelengths are relatively redshifts. These systems are therefore preferentially removed independent of obscuration. Following simple AGN unification, from our sample during our initial catalog selection by setting Type-1 AGN are those where the accretion disk can be viewed the is extended flag. Such dominance of the AGN over the almost directly, with very little intervening gas or dust, while host galaxy would hinder and bias our determination of the host a Type-2 AGN is viewed edge-on, and therefore has the disk galaxy properties during the SED-fitting process (e.g., stellar Publications of the Astronomical Society of Japan, (2017), Vol. 00, No. 0 11 smooth, elliptical isophotes, while late-type galaxies tend to be more disk-dominated with flatter light-profiles – it has been shown that even simple one or two-dimensional decompositions of the light profiles are capable of separating galaxies by their Hubble-type (e.g., Kormendy et al. 2009; Simard et al. 2011). In order to analyze the size, morphology and stellar-light distribution of the galaxies in our sample we begin by fitting a single 2-dimensional Sersic profile (S´ersic 1963) using GAL- FIT (Peng et al. 2002) to the HSC i-band images. We extracted 100 100 kpc postage stamps from the co-added data products × produced by hscPipe, along with the associated variance im- age and data mask. Point spread function (PSF) images are ex- tracted from the pipeline products on a source-by-source basis. Within hscPipe, the PSF images are computed using the PSFEx software (Bertin 2011) from 41 41 pixel images of nearby stars × to determine the size and ellipticity of the PSF for each visit. These PSFs are then co-added to replicate the average PSF of Fig. 5. Observed optical–IR color versus absolute magnitude diagram used the co-added image. The median PSF size for our sample is ′′ for separating our mid-IR selected AGN into obscured and unobscured sub- 0.6 . See Bosch et al. (2017) for further details on the com- ∼ samples (gray-scale contours). In i–[4.6] color, the AGN sample shows a putation of the PSF images. distinct bimodality, with unobscured Type-1 AGN exhibiting bluer colors of i − [4.6] < 1.1. Overlaid are the Type-2 AGN shown with rainbow colors to To measure a background level for each image, we used the represent the source spectroscopic redshift. Right panel provides the his- full HSC catalog, which is sensitive to sources with i 27 mags, ∼ togram of the optical-IR color (black solid line) that is well characterized by to identify and mask all objects that lie within the postage stamp the summation of two Gaussians, a type-1 AGN population (blue dashed) image based on their catalog shape measurements and their and an obscured AGN population (red dashed). The dotted lines is a simple cut that separates the AGN populations with minimum contamination. Kron radii. We additionally applied the byte mask to those pixels previously flagged by hscPipe as erroneous. We fit a simple 2-dimensional linear profile to the non-masked pixels to masses are known to be over-estimated for Type-1 AGN) and assess any overall background gradient within the image and during our morphological analysis presented in the next sec- determine a mean background level in each pixel. We fill all ar- tion. Hence, to ensure the most unbiased measurements of the eas within the background image that were previously masked AGN host galaxies, we select only the 2,552 obscured AGN 9 with Poisson noise determined by the mean background level with M∗ > 5 10 M⊙ for all further analyses that compare the × predicted for the individual masked pixels. The measured back- AGN/galaxy properties. ground level was included as an input to GALFIT, and held fixed throughout the fitting procedure. 4 Identifying interacting and merging To create an input mask image for GALFIT, we masked all galaxies within HSC images sources within the 100 100 kpc postage stamp that had inte- × In this section we harness the exquisite sensitivity and spatial grated magnitudes at least 3 mag fainter than the target galaxy resolution afforded to us by the HSC survey to provide a ba- (i.e., a factor 1:15 fainter in flux). All areas identified in the sic morphological classification for each galaxy in our spec-z hscPipe bad-pixel mask were also masked, and all bright point sample. Using parametric and non-parametric metrics, com- sources were masked with shapes based on the ellipticities and bined with a novel implementation of a Random Forest Machine radii of the co-added PSF. For all remaining unmasked extended Learning algorithm, we separate our spec-z galaxy sample into objects within the image, we included an additional Sersic pro- subsamples of major-mergers, minor-mergers and irregulars, file into the GALFIT fit centered at the position of the additional and non-interacting galaxies. galaxy. Hence, during the GALFIT fitting procedure, will si- multaneously model all bright galaxies with the postage stamp image. Our choice to mask objects determined to be at least a 4.1 Profile fitting with GALFIT factor 15 fainter than the target objects allows us to simultane- ∼ Image analysis techniques have been developed to produce ously model all components of possible major or minor mergers parametric measures that are capable of separating galax- to at least mass ratios of 1 : 10 (i.e., allowing for variability in ies by their morphological type. Using a-priori knowledge the mass-to-light ratio). of a galaxy’s structural properties – early-type galaxies have We next extracted a sub-image of 50 50 kpc centered × 12 Publications of the Astronomical Society of Japan, (2017), Vol. 00, No. 0 Fig. 6. Four examples of the imaging analysis described in Section 4 performed on our spec-z HSC galaxy sample. Large panels are K-corrected (pseudo- restframe) 3-color images; smaller inset panels are the best-fit Sersic model calculated using GALFIT (upper) and the residual image (i – model) with red color gradients for increasingly positive residuals and blue gradients for increasingly negative (lower). Labels provide the measures of asymmetry (Aimg), smoothness/clumpiness (Simg), concentration index (Cimg) and Gini index (Gimg) calculated from the i-band image, as well the asymmetry (Aresid), smooth- ness/clumpiness (Sresid) and Residual Flux Fraction (RFF) calculated from the residual image. Interaction probability (Pmerge) determined from our imple- mentation of a Random Forest Machine Learning algorithm in also given (see Section 4.2). around the target galaxy along with the respective mask and 4.2 Automated Merger Detection using Supervised variance images. This sub-imaging approach has the advan- Machine Learning tage of limiting the computation time with GALFIT, while also 4.2.1 Parametric & non-parametric morphology indicators maintaining that any large (unrelated) sources, which may have significantly overlapping isophotes with the region immediately Many image analysis techniques have been developed to auto- surrounding the target galaxy but may have centroids outside the matically separate merging systems from non-interacting and/or sub-image, will still be appropriately masked or have a Sersic isolated galaxies, to varying degrees of success and accuracy. profile assigned during the fitting process. The source image, These methods often make use of parameterizing the structures variance image and PSF model were all used as inputs for GAL- present in the image of a given galaxy. In the previous section, FIT. we applied a 2-dimensional Sersic profile to HSC postage stamp In the upper-right sub-panels of Fig. 6 we provide examples images, which was a simple parametric approach for modeling of the best-fit 2-dimensional Sersic profile that were fit to the the galaxy light distribution. target galaxy. For each galaxy, we extract the best fit parame- A tangential approach is to use non-parametric indices, ters for the Sersic profile, namely the Sersic index, n, and the which have been developed to assess the distribution of light characteristic effective radius, Re. Re is provided as a pixel within an image in order to separate/quantify a galaxy’s Hubble length within GALFIT, which we convert to a physical scale class and/or interaction stage (e.g., Patton et al. 2000, 2002; Lin in kiloparsecs for all further analyses. In the next section, we et al. 2004; De Propris et al. 2007; Lin et al. 2008; Robaina use the Sersic parameters and Sersic-profile subtracted images et al. 2010; Bluck et al. 2012; Glikman et al. 2015 for a recent (residuals) to compute metrics in order to identify interacting review see Conselice 2014). Typical non-parametric indices and non-interacting galaxies. make use of the light concentration, asymmetry and smooth- Publications of the Astronomical Society of Japan, (2017), Vol. 00, No. 0 13 ness/clumpiness (hereafter, CAS measurements; see Bershady 4.2.2 Training a Random Forest Classifier et al. 2000; Conselice 2003), as well as other measures involv- The goal of automated classification frameworks is to determine ing the Gini index and the second-order moments of the light a model that describes some in-hand data for a set of objects distributions (see Abraham et al. 2003; Lotz et al. 2004, 2008). whose ‘science classification’ is known a-priori. This model In the same spirit as these non-parametric indices, stud- is then applied a new set of objects, whose classifications are ies have now begun to develop new metrics that implic- unknown, and then used to predict a class or probability of itly incorporate parametric measurements, resulting in hybrid a given classification for each new object. Several forms of parametric/non-parametric indices. For example, the residual data-driven automated classification schemes have been used to flux fraction (RFF; Hoyos et al. 2011, 2012) measures the fluc- solve an abundance of astrophysical problems, such as Gaussian tuation of counts in residual images of galaxies once a simple mixture models, Bayesian networks, neural networks, and sup- best-fit Sersic profile has been subtracted. Residual images in- port vector machines (e.g., Goldstein et al. 2015; Moolekamp crease the contrast of concentrated structures, as well as en- & Mamajek 2015; Williams et al. 2016; Melchior & Goulding hance low-surface brightness features. Taken together, analysis 2016; Avestruz et al. 2017). A conceptually simple, extremely of the residuals may better reveal interaction signatures between efficient, and yet powerful classification method, which is be- galaxies that may otherwise be missed in the original images. coming popular throughout astronomy, is that of decision-tree Previous studies have determined that simple cuts on asym- learning. metry and smoothness (A> 0.35 and A>S; Conselice 2003) Decision trees are supervised non-parametric classifiers that or with the Gini and M20 parameters (G> 0.14 M20 +0.33; remain efficient even when attempting to capture complicated − × Lotz et al. 2004) can produce a reliable ( 50%) separation feature-based structures. They naturally handle multiple classi- ∼ of galaxies undergoing mergers in relatively nearby massive fication schemes, and are relatively robust to outliers. However, systems. With the advent of new generations of telescopes tree models tend to have high variance. Due to the hierarchical and deep surveys, like HSC, we are now able to resolve faint structure of the trees, even small changes in the top levels of a merger signatures in large galaxy samples that were previously training tree, induced by random selection of the variables used too weak to identify. However, as sensitivity to low surface to split nodes, can produce vastly different trees on subsequent brightness material increases, it becomes necessary to fine-tune nodes. Also, while large trees will, by design, always fit the our selection algorithms to identify features of interest, particu- training data very well, a specific large tree may not generalize larly as long-lived tidal debris, low surface brightness galaxies, well to test data. This process is akin to over-fitting in simple and the outer parts of spiral galaxies may all trigger the same regression. indicators (e.g., Greco et al. 2017). Noise in the final classifications can be reduced by con- Progress can be made by considering all of the information sidering multiple decision trees for a given dataset, so-called that can be extracted from a combination of each of these differ- ‘Random Forests’. Random Forest classifiers fit multiple deci- ent parametric and non-parametric structure measures. Here we sion trees to bootstrap subsamples of a given training set. The use a novel implementation of a Machine Learning technique to final classification for an object is then the average of the clas- provide a statistical measure of the interaction state of a given sifications produced by the individual bootstrap decision trees, system. which naturally provides a (pseudo-)probability for the classifi- As morphology “features” for our machine learning algo- cation (driven by the input training data) while controlling for rithm, we measure the CAS parameters for each galaxy in over-fitting of the data. our HSC spec-z sample, as well as the Gini and RFF indices. To build our training sample, we visually classified the For the precise formulation of these parameters we refer the 50 50 kpc k-corrected 3-band HSC images for a random sam- × reader to Section 2.3 of the review by Conselice (2014) and ple of 5,900 galaxies in our spec-z sample that were deemed Hoyos et al. (2012). We measure each of these indices on the to be star-forming based on their position in the UVJ dia- 50 50 kpc i-band postage stamp galaxy images. Following gram. The specific visual classification scheme involved the × Hoyos et al. (2012), we also compute the asymmetry and identification of (1) irregular/disturbed/torqued morphologies, smoothness/clumpiness parameters on the residual flux images (2) double-nuclei/late-stage merger, (3) evidence for interac- (i.e., the i-band image after subtraction of the best-fit Sersic tion with a distinct companion galaxy, (4) regular morpholo- model for the galaxy determined following the method outlined gies with no evidence for recent interaction, or (5) too-small to in the previous section). These non-parametric indices are com- conclusively identify. To normalize the responses of the seven bined with the parametric measurements of the best-fit Sersic expert classifiers, we averaged the individual visual classifica- profiles to provide a suite of morphological parameters (here- tions for a test subsample of 600 galaxies, and then weighted after, ‘features’) that we use to determine the interaction state the responses accordingly for the remaining visual classifica- of the galaxy through ‘automated classification’. tions. For galaxies that were clearly undergoing or had recently 14 Publications of the Astronomical Society of Japan, (2017), Vol. 00, No. 0 Fig. 7. Example of a decision tree within our implementation of a Random Forest machine learning algorithm. The Random Forest is constructed from our representative sample of 5,900 visually classified galaxies. Each decision tree is formed from a bootstrap resampling of a subsample of 4,500 visually classified galaxies and is trained to identify objects based on three morphological classifications (1: non-interacting [orange]; 2: major/late-stage merger [purple]; 3: minor-merger/irregular [green]). Nodes are gradient color-coded depending on the purity of the classification decision (light colors have low purity, dark colors have high purity). Publications of the Astronomical Society of Japan, (2017), Vol. 00, No. 0 15 Fig. 8. Distributions of probabilistic merger-state classifications assigned by our implementation of Random Forest Machine Learning algorithm to 1,400 visually classified galaxies. The Random Forest was trained on an independent sample of 4,500 visually classified galaxies randomly selected from our main HSC spec-z sample. Left: probability of being an isolated galaxy (PIsolated); Center: probability of being an irregular galaxy or a minor-merger (Pirregular/minor−merger ); Right: probability of being a major merger (PMajor−Merger ). Distributions are split as a function of their visual classification (isolated; irregular/minor-merger; major merger), see Section 4.2 for further details. Table 1. RandomForestClassifier Initiation Parameters Examples of four systems determined to be major-mergers from their visual classifications are shown in Fig. 6. Each Parameter Value of these systems are clearly at different stages of merging. input features Cimg; Aimg; Simg; Gimg; In terms of the interaction classification outlined by Veilleux Ares; Sres; RFF; Re; n et al. (2002), these galaxies would be classified as IIIa:wide bi- n estimators 1000 nary (right column), IIIb:close binary (top-left) and IV:Merger criterion gini (bottom-left).6 These four examples exhibit relatively wide max features √9 ranges in parameters such as their smoothness/clumpiness max depth 15 ∼ 0.1 0.8 (typical values in the range -0.5–1.5), but have nar- min samples split 12 − row ranges in Gini ( 0.6) and RFF ( 0.3 0.4). The role of bootstrap True ∼ ∼ − our Random Forest implementation will be to search for corre- warm start False lations between the visual classifications and the specific val- class weight balanced ues/ranges of these features. Our visually-classified training sample was split to provide an input of 4,500 galaxies used to construct the decision trees, undergone an interaction, our expert classifiers were in strong and an independent subsample of 1,400 galaxies to test the out- agreement that at least one of the interaction classifications was put classifications of the Random-Forest classifier. We used the valid. However, we noted significant variance among the ex- publicly available Python-based RandomForestClassifier perts when attempting to separate these different signatures of code provided as part of the scikit-learn package (Pedregosa galaxy–galaxy interactions. As such, we elected to consolidate et al. 2011) to build the decision trees. The input features for the our visual classifications for interacting systems, as we deter- decision tree construction (see Table1) were the concentration mined that this provided a cleaner separation between interact- ing and non-interacting galaxies.5 ers. Interacting galaxies with “(2) double-nuclei/late-stage mergers” were additionally considered major-mergers, while irregular morphologies were 5 For our visually classified training sample we split the galaxies determined considered to be minor-mergers. to be “(3) evidence for interactions with a companion” by the flux ratio 6 We note that sources with the interaction classification of ‘I:first approach’ of the two interacting systems. In accordance with previous studies, we would be considered as ‘non-interacting’ in our visual classifications as the used a demarcation of 1:4 in flux ratio to denote major and minor merg- galaxy disks have not yet been perturbed. 16 Publications of the Astronomical Society of Japan, (2017), Vol. 00, No. 0 (Cimg), asymmetry (Aimg), smoothness/clumpiness (Simg) and Gini (Gimg) indices measured from the HSC i-band images, the RFF, asymmetry (Ares) and smoothness/clumpiness (Sres) in- dices measured from the residual (galaxy–Sersic model) image, and the Sersic index and Re measured from the best-fit model. To avoid importance bias of a particular input feature, we first normalize the distributions of each feature to have mean zero and unity variance before inputting to the Random Forest generator. The Random Forest is initiated with the parameters shown in Table 1, and then trained to identify galaxies based upon the three morphological classifications assigned during our visual classifications: 1: non-interacting (inclusive of Stage I pre-mergers); 2: major-merger (inclusive of Stage II–IV merg- ers); 3: minor-mergers (inclusive of Stage V irregulars). The final assigned classification is then the average of the ‘votes’ from each of the 1000 decision trees, i.e., the fraction of trees that assign a classification of ‘isolated’ is Pisolated. An example of one of the 1000 decision trees in the Random Fig. 9. Fraction of sources in our test set of 1,400 visually classified galax- Forest is shown in Fig. 7. After experimentation, the branches ies as a function of the probability of a particular system being a merger. are pruned to not allow depths beyond 15 nodes, though most Merger probabilities are computed during the implementation of a python- based Random Forest Machine Learning algorithm trained on an indepen- branches terminate before this as we set a minimum threshold of dent set of 4,500 visually classified galaxies in our main HSC spec-z sam- > 12 sources for a new node to be created. In the example pre- ple. Dashed, dotted and solid lines are those galaxies visually classified to sented in Fig. 7 we find that in the initial node of the tree (left- be major mergers (flux-ratio > 1 : 4), minor mergers 1 : 4 − 10, and ma- most box in the diagram), that a relatively neutral cut in RFF jor or minor mergers, respectively. Red lines provide the fraction of objects with a given Pmerge that are determined to not be the given merger classi- (0.0076 in normalized units) ultimately results in a strong over- fication (i.e., for major mergers, contaminant populations are non-interacting all distinction between interacting and non-interacting galaxies. galaxies and minor-mergers). All subsequent nodes leading upwards and away from the initial node (i.e., training objects with RFF 0.0076) are, in general, ≤ the least useful with I 0.03. colored orange, denoting non-interacting galaxies. By contrast, ∼ subsequent nodes leading downwards and away from the initial 4.2.3 Testing the Random Forest Classifier node (i.e., training objects with RFF> 0.0076) are more likely To assess the ability of our Random Forest for providing reli- to result in nodes containing interacting galaxies (colored either able probabilistic classifications to the remainder of our HSC purple:major-merger or green:irregular/minor-merger). spec-z galaxy sample, we applied the trained Random Forest to Furthermore, in Fig. 7 we show that minor-mergers are dif- our ‘test sample’ of 1,400 visually classified galaxies that were ficult to distinguish from major mergers and non-interacting not used during the training of the classifier. In Fig. 8 we pro- galaxies. The minimum node value (i.e., the number of con- vide the distributions of the classification probabilities for our necting nodes required to reach a node from the initial [left- test sample separated by their visual classifications. For each most] node) of an irregular/minor-merger classification is 4, of classification probability, the distribution of the true visual with the majority of the irregular/minor-merger leaves not be- classified objects peak at higher probability values. Indeed, it is ing identified until node > 7. From a decision tree stand-point, clear from the Pisolated histograms that we can cleanly recover minor-mergers then become a sub-category of the more domi- a sample of isolated galaxies with a cut of Pisolated > 0.7, with nant isolated and major-merger classifications, making their ro- little or no contamination from interacting galaxies. However, bust identification complex. this of course does not recover the full population of isolated Using our training visual classification sample, we addition- systems, as this population of objects begins to mix significantly ally calculated the importance of the input features that went with objects towards lower values of Pisolated. This is also mir- into producing our Random Forest classifier. The importance rored in the distributions of Pminor−merger and Pmajor−merger. can be thought of as the fraction of useful information that is In Fig. 9 we further explore contamination to a major merger used by the classifier during the construction of a decision tree, sample when applying a threshold in Pmerger. As expected, we with the sum of importances, I, over all features equaling unity. find galaxies assigned to have high values of Pmerger by the The most important features, averaged over all trees, were Sres, Random Forest are increasingly more likely to actually be major Aimg and RFF, each with I 0.17 0.21, while the Cimg was mergers based on their visual classification. We find that while ∼ − Publications of the Astronomical Society of Japan, (2017), Vol. 00, No. 0 17 a threshold of Pmerger > 0.33 would provide a sample that is 5.1 Incidence of AGN in interacting and 90% complete towards major mergers, 30% of the sample non-interacting galaxies ∼ ∼ would be contaminated by non-interacting galaxies and minor- AGN activity is a highly stochastic process, with changes in ac- mergers/irregulars. Based on our Random Forest and limited cretion rate that typically occurs on time-scales that are much training sample, we cannot yield a truly pure sample of major shorter than longer lived galactic processes, such as changes in mergers that is more than 39% complete. However, in the ∼ stellar mass, star-formation rate, or even merger-stage. Thus, it range 0.32 < Pmerger < 0.67 it is clear we suffer from only mild is more robust to probe the average AGN property (i.e., averag- contamination ( 10%), and only 1/3 of the sample contam- ∼ ∼ ing over BH accretion variability) as a function of the longer ination arises from isolated galaxies. Hence, a cut of Pmerger > timescale galactic process (see Hickox et al. 2014). Hence, 0.32 yields a relatively clean sample of interacting systems (i.e., we now investigate the average incidence of AGN based on minor+major merger), while a cut of Pmerger > 0.46 yields host-galaxy interaction stage by harnessing three galaxy sam- a sample of major-mergers that is over > 75% complete and ples: 1) major mergers; 2) all interacting galaxies (including suffers less than 10% contamination, the majority of which ∼ major merger, minor mergers, and irregular systems), and 3) arises due to minor-mergers and irregulars, which themselves non-interacting galaxies. may have somewhat ambiguous visual classifications given the almost arbitrary demarcations that are made between the visual 5.1.1 Selecting galaxies & AGN in bins of interaction type classes. Finally, we also tested for any effect to the classifica- In Section 4.2 we determined that a threshold of tions due to the presence of an unobscured AGN, which may Pmajor−merger > 0.46 in our trained Random Forest clas- have been incorrectly classified as a Type-2 AGN from our IR– sifier recovers 75% of the major-mergers present in our optical color-cut. While we do include a PSF model during our ∼ visually-classified test sample. A sample of major-mergers GALFIT analysis, we found that the presence of a Type-1 AGN defined by such a cut suffers contamination at the 7% still marginally steepens the Sersic index, significantly increases ∼ level from minor-mergers and irregular galaxies, and < 3% the concentration index, and lowers the asymmetry value. These from non-interacting galaxies. Applying this threshold in are each due to the galaxy light being partially contaminated Pmajor−merger provides a clean sample of 4,449 gas-rich major by the AGN. Irrespective of whether the source was visually mergers at 0.1 5 Results previous section, we invoke a threshold of Pmerger > 0.32 or Pminor−merger > 0.40 to define the set of interacting galaxies, Despite the theoretical successes of BH–galaxy co-evolution which yields an interacting sample of 5,594 systems. The non- models to explain observed present-day galaxy populations, ob- interacting star-forming galaxies are defined by Pisolated > 0.7, servational evidence for the presence of an on-going merger and which provides a sample of 12,513 galaxies, with 1% con- ≪ the concurrent rapid growth of BHs, which is now a required tamination from interacting systems. ingredient of galaxy formation simulations, remain elusive. In In Fig. 11 we show the distributions of the three samples this section we use the morphological/interaction probabilities in their WISE [3.4]–[4.6] color and stellar mass, separated by derived using our implementation of a Random Forest machine three redshift bins (0.1 Fig. 10. Examples of K-corrected 3-color 50×50 kpc HSC images of the 4,449 gas-rich major-merger candidates at 0.1 Pmajor−merger > 0.46 based on our Random Forest Classifier described in Section 4.2. These merger candidates cover a wide-range in interaction state, stellar mass ratio and redshift. 9 IR continua, and thus has a relatively high [3.4]–[4.6] color(& (Moustakas et al. 2013) and M∗ 3 10 M⊙ (Davidzon ∼ × 0.7). The AGN were previously identified based on their 2 or 3 et al. 2013), respectively. We note that while these surveys band mid-IR colors, as measured from their WISE photometry claim completenesses toward star-forming galaxies of M∗ 9 ∼ (see Section 3.3); crucially, the AGN selection was performed 5 10 M⊙, we ultimately do not require our sample to be com- × independently of the morphology and interaction state of the plete in stellar mass, as the AGN fractions we present in the pro- galaxy. ceeding sections are compared in relatively between interacting and non-interacting galaxies. Previous observations have suggested that the AGN frac- tion (above some particular threshold in AGN luminosity) rises steeply as a function of stellar mass (e.g., Xue et al. 2010; 5.1.2 WISE Completeness Corrections Aird et al. 2012; Bongiorno et al. 2012; Mullaney et al. 2012). In each of our considered redshift bins, our star-forming parent 9 Thus, it is important to consider stellar mass matched sam- sample is roughly mass complete at around M∗ 5 10 M⊙. ∼ × ples when assessing the relative incidence of AGN in our However, as we advance in redshift, we systematically miss morphological/interaction-state samples. Here we provide the low-mass galaxies in our sample due to the flux limit of the quoted mass-completeness limits towards star-forming galaxies WISE survey. This is observed in Fig. 11 as a deficit of sources of the spectroscopic surveys, from which our main spec-z sam- in the 0.3 Fig. 11. Lower Panels: WISE [3.4]–[4.6] color (vega) as a function of stellar mass (in logarithmic units of M⊙) for three morphologically selected samples – non-interacting galaxies (Pisolated > 0.7; orange; bottom row), major mergers (Pmajor−merger > 0.46; purple; center row), and all interacting galaxies (major-merger+minor-merger+irregulars; Pmajor−merger > 0.32 W Pminor−merger > 0.4; red; top row). For merging/interacting systems, reported stellar masses are those of the most massive galaxy in the pair. Panels columns are split by redshift, using the same cuts as in Fig. 3. Galaxies identified to host IR-luminous Type 2 AGN using the diagnostics presented in Figs. 4 and 5 are shown with star symbols and colors denoting morphological classification. 9 Dotted lines illustrate the AGN selection boundaries for galaxies with M∗ & 5 × 10 M⊙ and [3.4]–[4.6] > 0.8 (Stern et al. 2012) Top Panels: Histograms of AGN fraction as a function of stellar mass constructed in redshift bins matched to the lower panel. AGN fractions in a given redshift bin are separated by morphological classification – non-interacting galaxies (orange dots); major mergers (purple dashed); all interacting galaxies (red solid). The AGN fractions are also corrected for incompleteness of non-WISE detections in the two highest redshift bins. We find in each redshift bin that galaxies undergoing mergers are a factor ∼ 2 − 3 more likely to contain AGN than non-interacting galaxies, and this is independent of stellar mass. 20 Publications of the Astronomical Society of Japan, (2017), Vol. 00, No. 0 If we compare these fractions of WISE non-detections to our atively complete towards WISE detections, our computed cor- lowest redshift bin (0.1 5.2 Testing for observational bias and heterogeneity in the SDSS-BOSS survey, and hence, the fAGN presented in in our spec-z sample Fig. 11 at 0.3 presented in Fig. 11. Similar to our analysis in Section 5.1, by measuring changes in fAGN between interacting and non- interacting galaxies, we naturally control for BH accretion vari- ability, which likely occurs on shorter timescales than merger events. AGN bolometric luminosities for our sample are computed by fitting powerlaw slopes to the mid-IR photometry of the AGN, and using the best-fit slope to predict the rest-frame 6µm continuum luminosity, which is shown to be a robust indicator of LAGN (e.g., Lutz et al. 2004; Fiore et al. 2009; Chen et al. 2017). The AGN considered here cover a relatively wide range, 43 46 −1 with luminosities of LAGN 3 10 –2 10 erg s . ∼ × × In each M∗ bin, we find that the AGN fraction decreases substantially for the major-merger and interacting galaxy sam- ples by factors of 0.7 2 after removing the upper-quartile of ∼ − the most luminous AGN, i.e. ∆fAGN = fAGN fAGN,−UQ − ∼ 0.01 0.03. While the decrease in fAGN for the non-interacting − galaxies was in some cases consistent with 0. This provides Fig. 12. Lower Panels: AGN fraction as a function of stellar mass after re- ∼ moval of the upper quartile (fAGN,−UQ) of sources with the highest LAGN tentative evidence that the most luminous AGN do preferen- (solid histograms). Hashed regions provide the AGN fraction determined af- tially reside in merging galaxies. ter randomly removing 25% (fAGN,−Rand25) of the AGN in a given stellar mass bin, irrespective of LAGN. This fraction is recomputed 10,000 times We can give these results a stronger statistical footing by using a jack-knife re-sampler, and the bounds provide the 90th percentile simulating the ∆fAGN had we not preferentially removed the range of the samples. Color coding is the same as Fig 11. Upper Panels: upper quartile of the most luminous AGN, but instead we Residual AGN fraction between the fAGN,−UQ and fAGN,−Rand25 (i.e., had randomly removed 25% of the AGN from a given M∗ ∆fAGN = fAGN,−Rand25 − fAGN,−UQ) as a function of stellar mass. In general, we find that the most luminous AGN systematically reside in bin (i.e., independent of LAGN). This is achieved through the interacting/merging galaxies, hence their positive residual signatures. 10,000 Jack-knife re-samplings of fAGN for each morphol- Conversely, fewer luminous AGN reside in non-interacting galaxies, as we ogy/interaction sample after removing a random set of 25% show that fAGN,−UQ is systematically larger than the expected null result of the AGN in each M∗ bin at each redshift (fAGN,−Rand25). value, fAGN,−Rand25. In Fig. 12 we plot the residual between fAGN,−Rand25 and − . The hashed regions represent the 90th percentiles lower BH masses. fAGN, UQ of the Jack-knife samples. Across all three redshift ranges, We can test such a scenario using our interacting and non- we show a general trend of positive for the in- interacting galaxy samples by assessing whether the most bolo- ∆fAGN teracting galaxies (i.e., − − ), metrically luminous AGN are preferentially hosted in interact- fAGN, Rand25,mergers > fAGN, UQ and negative residuals for the non-interacting galaxies (i.e., ing galaxies over non-interacting galaxies. If the most lumi- − − − ). Hence, we have nous AGN are biased regarding the morphologies/interaction fAGN, Rand25,non interacting < fAGN, UQ good statistical evidence at the % level, that the most lu- state of the host, then the AGN fractions for interacting/non- > 90 minous AGN systematically reside in the interacting galaxies at interacting galaxies, presented in Fig. 11, should have an addi- fixed stellar mass. tional dependency on LAGN at fixed stellar mass. However, and crucially, if indeed the most luminous AGN are systematically In Fig. 12, we further show that at z > 0.3 the ∆fAGN ap- more likely to reside in merging galaxies, then fAGN,interacting pears to diverge with increasing M∗, suggesting that a larger and fAGN,non−interacting would not have the same dependency fraction of the most luminous AGN reside in interacting galax- on LAGN, at fixed stellar mass. ies at higher M∗, with an additional marginal preference for We test this hypothesis by measuring the change in fAGN the most luminous AGN residing in major-mergers. At z< 0.3 between those values presented in Fig. 11, and the fAGN cal- we find consistent (at the 90th percentile) residual fAGN val- culated after removing the upper-quartile of the most lumi- ues between the interacting and non-interacting galaxies in the 9 ∗ nous AGN present in a given M∗ bin (hereafter, fAGN,−UQ) highest M bin, suggesting no statistically significant prefer- For consistency, we use the same bins of M∗ and z as those ence for luminous AGN between the interaction classifications. We note that this result may also be driven by the small number 9 Our choice of selecting the upper 25% of the AGN luminosities is somewhat of AGN in non-interacting galaxies at high M∗ in our sample, arbitrary. However, this ensures that we remove the majority of AGN with 45 −1 preventing us from measuring a strong systematic difference. LAGN > 10 erg s , which is consistent with the upturn in the merger fraction as a function of LAGN (e.g., Fan et al. 2016 and refs. therein). However, overall we show that fewer luminous AGN reside in Publications of the Astronomical Society of Japan, (2017), Vol. 00, No. 0 23 non-interacting galaxies, with a strong preference for the most Indeed, the fraction of AGN is seen to fall dramatically at large rapidly growing BHs to be generally hosted in major mergers. projected separations (e.g., Ellison et al. 2013; Satyapal et al. 2014a; Ricci et al. 2017). However, new episodes of signifi- cant AGN activity may then be re-ignited on subsequent pas- 6 Discussion sages until coalescence of the galaxies. In such a scenario, the 6.1 Evidence for stochastic BH growth during AGN activity would seemingly occur sporadically throughout major-merger events the merger, but with the overall AGN light curve being strongly correlated with the merger’s dynamical time. Based on our population analysis (Section 5.1), we can ro- bustly conclude that, on average, those galaxies that are cur- Within our proposed framework, during the merger there rently undergoing or have recently undergone some form of may be multiple periods of observable AGN activity, and seem- a merger/interaction are a factor 2 7 more likely to be ingly non-AGN activity. Including a close connection with the ∼ − rapidly growing their central BHs than more isolated and non- dynamical time forces the non-AGN phases to last substantially interacting star-forming galaxies. Furthermore, whilst we have longer during first and second passage, but with the non-active not attempted to address the question of whether all luminous phases become shorter-lived as the galaxies begin to coalesce. AGN events must be triggered by mergers (such a study would In accordance with the results of previous investigations, com- require a thorough understanding of incompleteness effects), parisons of the merger rates of AGN to merger rates in non- AGN should produce similar fractions, as the AGN is not al- our results clearly indicate a systematic enhancement of fAGN in major mergers over non-interacting galaxies and/or even ways ‘active’ during the entire merger event. As the merger be- minor-mergers. This could suggest that significant BH growth gins to reach coalescence, the probability to observe the AGN phase(s) are linked specifically to a major merger scenario. increase dramatically as the AGN episodes occur more fre- quently, and may result in the maximal growth phase of the BH, Previous studies that have investigated merger–AGN con- as predicted in merger simulations. Such a scenario would si- nections have produced mixed results (e.g. Gabor et al. 2009; multaneously explain the apparent increase in merger rate with Cisternas et al. 2011; Schawinski et al. 2011; Kocevski et al. AGN luminosity (e.g., Urrutia et al. 2008; Glikman et al. 2012; 2012; Treister et al. 2012; Ellison et al. 2013; Villforth et al. Treister et al. 2012), and our finding of the most luminous AGN 2014; Kocevski et al. 2015), often finding that the host galaxies preferentially residing within merging systems. of AGN are similar to those of non-AGN (i.e., no strong en- hancement in merger fractions of AGN over non-AGN). These Furthermore, in Figure 11, we show that . 10% of major- results are seemingly at odds with our population analysis that mergers contain luminous (obscured mid-IR) AGN, suggesting shows, on average, AGN are markedly more likely to occur dur- that the total AGN duty-cycle over the course of the merger may ing a major-merger than in an isolated galaxy. We suggest that only be 50 100 Myrs. Moreover, in Figure 11, we show that ∼ − these apparent differences can be reconciled by considering the luminous AGN activity still occurs during isolated, secular evo- relative time-scales of the luminous AGN activity, the dynam- lution phases (i.e., a major-merger may not be an absolute re- ical time of a major-merger, and the time spent as an isolated quirement for luminous AGN activity to occur). Taken together, galaxy. these two results suggest that merger-AGN studies primarily se- Motivated by galaxy merger simulations, here we outline a lected on the basis of AGN activity would need to be large in number in order to observe the relatively small 10% enhance- framework that closely ties BH accretion rate variability to the ∼ dynamical time of the galaxy merger. Given that AGN activity ment in mergers relative to non-mergers in an AGN-selected is known to vary on timescales much shorter than galaxy pro- sample (e.g., Villforth et al. 2014). However, we show that cesses (e.g., Novak et al. 2011; Gabor & Bournaud 2013), and when considering a time-averaged look at the AGN duty cycle, on average, BH accretion rate is linked to available gas supply the probability of luminous AGN occurring in major-mergers (e.g., Hickox et al. 2014), we make the ansatz that the act of is clearly enhanced over non-interacting galaxies by at least a factor 3. merging further enhances AGN variability as galaxy merging ∼ strongly affects the inflow of gas, which can serve to fuel the Overall, our results suggest that, on average, mergers do AGN. For example, on first pericentric passage, gravitational trigger AGN significantly more often than in secularly evolv- torques may be sufficient to induce a short period of rapid BH ing galaxies above a particular luminosity threshold (LAGN & growth (< 50 Myrs). After first passage, interaction signatures, 1044 erg s−1), but that the BH does not necessarily need to such as tidal tails, may still be evident as the galaxies move to be growing at a significant rate throughout the entire merger maximum separation (lasting 200 400 Myrs). However, in- phase. However, given that we have focused on the growth of ∼ − ternally, the galaxies may partially relax, limiting fuel to the obscured AGN only, we have implictly not considered a pos- BH, and causing accretion to slow, and the wide-separation sible evolutionary scenario between Type 2 and Type 1, which merger may no longer be observationally identified as an AGN. may also be linked with merger stage. This is beyond the scope 24 Publications of the Astronomical Society of Japan, (2017), Vol. 00, No. 0 of this investigation, but may be possible with future wide- galaxies over non-interacting systems. At any given stellar format high-resolution imaging capable of identifying Type-1 mass bin, we found that the upper-quartile of the most lu- AGN, while also providing the ability to extract galaxy prop- minous AGN preferentially reside in merging galaxies over erties such as morphology. Finally, at lower BH masses and/or non-interacting galaxies. We use these results to suggest that lower AGN luminosities, secular processes may be more impor- a major merger between two galaxies is sufficient to induce tant for driving BH growth, with major-mergers becoming sub- a flow of cool gas towards the central BH in one or both dominant. Indeed, an enhancement in AGN fraction of only a galaxies, and this is systematically more likely to trigger a factor 0.5 2 is seen in Seyfert-like luminosity z< 0.1 galaxy significant AGN event that in an isolated galaxy alone. ∼ − pairs in SDSS (e.g., Ellison et al. 2011a). 4. To place our findings into the wider context of AGN–galaxy co-evolution, and reconcile our conclusions with seemingly contradictory results within the recent literature, we outline 7 Summary & Conclusions a coherent framework that closely ties the variable AGN In this paper, we have investigated the effect of the merging of light curve to the dynamical time of the merger event. Our gas-rich galaxies on the growth of BHs out to z . 1. We have proposed framework requires that AGN accretion undergoes used the exquisite imaging quality afforded to us by the HSC in- several distinct peaks in luminosity over the lifetime of the strument on the Subaru Telescope to identify merging and non- merger, with BH fueling linked to the close passage and in- interacting galaxies across the first 170 deg2 of the HSC survey. teraction of the merging galaxies. 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