Search for Optically Dark Infrared Galaxies without Counterparts of Subaru Hyper Suprime-Cam in the AKARI North Ecliptic Pole Wide Survey Field
Yoshiki Toba, Tomotsugu Goto, Nagisa Oi, Ting-Wen Wang, Seong Jin Kim, Simon C.-C. Ho, Denis Burgarella, Tetsuya Hashimoto, Bau-Ching Hsieh, Ting-Chi Huang, Ho Seong Hwang, Hiroyuki Ikeda, Helen K. Kim, Seongjae Kim, Dongseob Lee, Matthew A. Malkan, Hideo Matsuhara, Takamitsu Miyaji, Rieko Momose, Youichi Ohyama, Shinki Oyabu, Chris Pearson, Daryl Joe D. Santos, Hyunjin Shim, Toshinobu Takagi, Yoshihiro Ueda, Yousuke Utsumi, Takehiko Wada
DDraft version June 16, 2020
Typeset using L A TEX twocolumn style in AASTeX63
Search for Optically Dark Infrared Galaxies without Counterparts of Subaru Hyper Suprime-Cam inthe AKARI North Ecliptic Pole Wide Survey Field
Yoshiki Toba,
1, 2, 3
Tomotsugu Goto, Nagisa Oi, Ting-Wen Wang, Seong Jin Kim, Simon C.-C. Ho, Denis Burgarella, Tetsuya Hashimoto,
4, 7
Bau-Ching Hsieh, Ting-Chi Huang,
8, 9
Ho Seong Hwang, Hiroyuki Ikeda,
11, 12
Helen K. Kim, Seongjae Kim,
14, 15
Dongseob Lee, Matthew A. Malkan, Hideo Matsuhara,
9, 8
Takamitsu Miyaji,
17, 18
Rieko Momose, Youichi Ohyama, Shinki Oyabu, Chris Pearson,
21, 22
Daryl Joe D. Santos, Hyunjin Shim, Toshinobu Takagi, Yoshihiro Ueda, Yousuke Utsumi,
24, 25
Takehiko Wada, Department of Astronomy, Kyoto University, Kitashirakawa-Oiwake-cho, Sakyo-ku, Kyoto 606-8502, Japan Academia Sinica Institute of Astronomy and Astrophysics, 11F of Astronomy-Mathematics Building, AS/NTU, No.1, Section 4,Roosevelt Road, Taipei 10617, Taiwan Research Center for Space and Cosmic Evolution, Ehime University, 2-5 Bunkyo-cho, Matsuyama, Ehime 790-8577, Japan Institute of Astronomy, National Tsing Hua University, No. 101, Section 2, Kuang-Fu Road, Hsinchu City 30013, Taiwan Tokyo University of Science, 1-3, Kagurazaka Shinjuku-ku Tokyo 162-8601 Japan Aix Marseille Univ. CNRS, CNES, LAM Marseille, France Centre for Informatics and Computation in Astronomy (CICA), National Tsing Hua University, 101, Section 2, Kuang-Fu Road,Hsinchu, 30013, Taiwan Department of Space and Astronautical Science,The Graduate University for Advanced Studies, SOKENDAI, 3-1-1 Yoshinodai,Chuo-ku,Sagamihara, Kanagawa 252-5210, Japan Institute of Space and Astronautical Science, Japan Aerospace Exploration Agency, 3-1-1 Yoshinodai, Chuo-ku, Sagamihara, Kanagawa252-5210, Japan Korea Astronomy and Space Science Institute, 776 Daedeokdae-ro, Yuseong-gu, Daejeon 34055, Republic of Korea National Astronomical Observatory of Japan, 2-21-1 Osawa, Mitaka, Tokyo 181-8588, Japan National Institute of Technology, Wakayama College, Gobo, Wakayama 644-0023, Japan Department of Physics and Astronomy, UCLA, 475 Portola Plaza, Los Angeles, CA 90095-1547, USA University of Science and Technology, Daejeon 34113, Korea Korea Astronomy and Space Science Institute, Daejeon 34055, Korea Department of Earth Science Education, Kyungpook National University, Daegu 41566, Korea Instituto de Astronom´a sede. Ensenada, Universidad Nacional Aut´onoma de M´exico (UNAM), Km 107, Carret. Tij.-Ens., Ensenada,22060, BC, M´exico Leibniz Institut f¨ur Astrophysik Potsdam, An der Sternwarte 16, 14482 Potsdam, Germany a19
Department of Astronomy, School of Science, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-0033, Japan Institute of Liberal Arts and Sciences, Tokushima University, Minami Jousanjima-Machi 1-1, Tokushima, Tokushima 770-8502, Japan RAL Space, STFC Rutherford Appleton Laboratory, Didcot, Oxfordshire, OX11 0QX, UK Oxford Astrophysics, University of Oxford, Keble Rd, Oxford OX1 3RH, UK Japan Space Forum, 3-2-1, Kandasurugadai, Chiyoda-ku, Tokyo 101-0062, Japan SLAC National Accelerator Laboratory, 2575 Sand Hill Road, Menlo Park, CA 94025, USA Kavli Institute for Particle Astrophysics and Cosmology, Stanford University, 452 Lomita Mall, Stanford, CA 94035, USA
ABSTRACTWe present the physical properties of
AKARI sources without optical counterparts in optical imagesfrom the Hyper Suprime-Cam (HSC) on the Subaru telescope. Using the
AKARI infrared (IR) sourcecatalog and HSC optical catalog, we select 583 objects that do not have HSC counterparts in the
AKARI
North Ecliptic Pole (NEP) wide survey field ( ∼ ). Because the HSC limiting magnitudeis deep ( g AB ∼ . µ m and use it for Spectral Energy Distribution (SED) fitting with CIGALE to investigate the
Corresponding author: Yoshiki [email protected] a On sabbatical leave from UNAM. a r X i v : . [ a s t r o - ph . GA ] J un Toba et al. physical properties of
AKARI galaxies without optical counterparts. We also compare their physicalquantities with
AKARI mid-IR selected galaxies with HSC counterparts. The estimated redshiftsof
AKARI objects without HSC counterparts range up to z ∼
4, significantly higher than that of
AKARI objects with HSC counterparts. We find that: (i) 3.6 − µ m color, (ii) AGN luminosity,(iii) stellar mass, (iv) star formation rate, and (v) V -band dust attenuation in the interstellar mediumof AKARI objects without HSC counterparts are systematically larger than those of
AKARI objectswith counterparts. These results suggest that our sample includes luminous, heavily dust-obscuredSFGs/AGNs at z ∼ − James Webb Space Telescope era.
Keywords:
Active galactic nuclei (16); Infrared galaxies (790); Infrared photometry (792); Bayesianstatistics (1900) INTRODUCTIONIn the last two decades, it has become clear that dustystar-forming galaxies (SFGs) and active galactic nuclei(AGNs) play an important role in galaxy formation andevolution, and in co-evolution of galaxies and supermas-sive black holes (SMBHs) (see e.g., Goto et al. 2011;Casey et al. 2014; Hickox & Alexander 2018; Chen etal. 2020, and references therein). They are particularlynumerous in the high-z universe ( z > µ m color)correlates with AGN activity and nuclear obscuration inthe framework of galaxy merger events–a redder systemtends to have large AGN luminosity and hydrogen col-umn density (see also Ricci et al. 2017; Ellison et al.2019; Gao et al. 2020). Since dusty SFGs/AGNs oftenhave redder color in the optical and near-IR (NIR), theycould correspond to the maximum phase of SF/AGNactivity. They would thus be the crucial population tounderstanding how the galaxies and their SMBHs co-evolve behind a large amount of dust (see also Hopkinset al. 2008; Narayanan et al. 2010).Owing to the heavy extinction by so much dust, somehigh-z dusty AGNs/SFGs are optically faint or evenoptically “dark”. For example, dust-obscured galaxies(DOGs: Dey et al. 2008; Toba et al. 2015, 2017a;Noboriguchi et al. 2019) are characterized by a largemid-IR (MIR) – optical color; their optical flux densityis about 10 times fainter than that in the MIR band(see also Hwang et al. 2013a,b). They are consideredas dusty SFGs or AGNs at z ∼ z > AKARI , the first Japanese space satellite dedicated toIR astronomy (Murakami et al. 2007), has also a greatpotential to find such optically dark objects. In additionto all-sky surveys with MIR and far-IR (FIR) (Ishiharaet al. 2010; Yamamura et al. 2010),
AKARI performeddeep and wide observations of the North Ecliptic Pole(NEP) over a total area of 5.4 deg (Matsuhara et al.2006). The AKARI
NEP survey consists of two layers:NEP Wide (NEP-W; Lee et al. 2009; Kim et al. 2012)and NEP Deep (NEP-D; Wada et al. 2008; Takagi etal. 2010; Murata et al. 2013) . AKARI
NEP regionswere observed with the Infrared Camera (IRC: Onakaet al. 2007), using its nine filters that continuouslycover 2–25 µ m. They are called N2, N3, and N4 for theNIR bands, S7, S9W, and S11 for the shorter part ofthe MIR band (MIR-S), and L15, L18W, and L24 forthe longer part of the MIR bands (MIR-L). The effec-tive wavelength of the N2, N3, N4, S7, S9W, S11, L15,L18W, and L24 filters is about 2.4, 3.2, 4.1, 7.0, 9.0,11.0, 15.0, 18.0, and 24.0 µ m, respectively. These con-tinuous NIR-MIR filters that are critical to trace dustemission heated by AGNs and/or Polycyclic AromaticHydrocarbon (PAH) emission associated with SF activ-ity. This makes AKARI /NEP data quite powerful toselect AGNs and measure the SF activity up to z ∼ AKARI NEP-D is a part of NEP-W as shown in Figure 1. Buteach catalog in NEP-W and NEP-D was independently created.Therefore, we ensure a uniform survey depth even for objects inthe NEP-D as long as we use the NEP-W catalog.
KARI sources without HSC counterparts
AKARI
NEP-W has been observed withHyper Suprime-Cam (HSC; Miyazaki et al. 2018) (seealso Furusawa et al. 2018; Kawanomoto et al. 2018;Komiyama et al. 2018) on the Subaru telescope (PI:T.Goto). Oi et al. (2020) reduced the data and usedit to construct a five-band HSC catalog that contains3,251,792 sources. The 5 σ detection limit for g -, r -, i -, z -, and y -band is about 28.6, 27.3, 26.7, 26.0, and 25.6mag, respectively. Oi et al. (2020) cross-identified theHSC catalog with AKARI
NEP-W catalog and foundthat ∼ AKARI objects have HSC counterparts(hereafter AKARI–HSC objects) (see also S. J. Kim etal. 2020 in preparation). Using the AKARI–HSC ob-jects, Goto et al. (2019) derived their IR luminosity func-tion at 0 . < z < . z ∼
2. Recently, Ho etal. (2020) calculated photometric redshifts ( z photo ) ofAKARI–HSC objects, and demonstrated how the deepHSC data improve their accuracy. Wang et al. (2020)investigated the physical properties of AKARI–HSC ob-jects based on spectral energy distribution (SED) fittingwith CIGALE (Code Investigating GALaxy Emission:Burgarella et al. 2005; Noll et al. 2009; Boquien et al.2019) (see also Chiang et al. 2019).However, those works focused on AKARI sources thathave optical (i.e., HSC) counterparts. In this work, weshed light on the remaining objects;
AKARI sourceswithout HSC counterparts in the
AKARI
NEP-W. Sincethe limiting magnitude of the HSC is deep, these sourcesare expected to be extremely red SFGs/AGNs at highredshifts. We carefully select the candidates and inves-tigate their physical properties.The structure of this paper is as follows. Section 2 de-scribes the data set, sample selection of
AKARI sourceswithout HSC counterparts, and our SED modeling ofthem. In Section 3, we present the results of our SEDfitting and the derived physical quantities. In Section4, we compare the resultant physical properties with
AKARI sources that have HSC counterparts (Wang etal. 2020). We summarize the results of the work inSection 5. Throughout this paper, the adopted cosmol-ogy is a flat universe with H = 70.4 km s − Mpc − ,Ω M = 0.272, and Ω Λ = 0.728 (the Wilkinson MicrowaveAnisotropy Probe 7 cosmology: Komatsu et al. 2011),which are the same as those adopted in Wang et al.(2020). Unless otherwise noted, all magnitudes refer tothe AB system and a Salpeter (1955) initial mass func-tion (IMF) is assumed. https://cigale.lam.fr/2018/11/07/version-2018-0/ Table 1.
Multi-wavelength Data Set.Catalog Band 5 σ detection limit (unit)(Number of sources)HSC g r i z y J H Spitzer /IRAC Ch1 6.45 ( µ Jy)(380,858) Ch2 3.95 ( µ Jy)
AKARI
NEP-W N2 15.42 ( µ Jy)(114,794) N3 13.30 ( µ Jy)N4 13.55 ( µ Jy)S7 58.61 ( µ Jy)S9W 67.30 ( µ Jy)S11 93.76 ( µ Jy)L15 133.1 ( µ Jy)L18W 120.2 ( µ Jy)L24 274.4 ( µ Jy)
Herschel /SPIRE 250 µ m 9.0 (mJy)(4,820) 350 µ m 7.5 (mJy)500 µ m 10.8 (mJy)2. DATA AND ANALYSIS2.1.
Data Set
To select
AKARI sources without HSC counterpartssecurely and investigate their physical properties, wecompile multi-wavelength data.
AKARI
NEP-W wasobserved by many facilities, and thus we have abundantdataset from ultraviolet (UV) to radio (see e.g., Kim etal. 2012; Oi et al. 2020, and references therein fora full description). We particularly utilized the follow-ing data sets in addition to the
AKARI
NEP-W catalog(Kim et al. 2012) and HSC catalog (Oi et al. 2020)that are summarized in Table 1. The area coverage ofeach dataset is summarized in Figure 1.We used NIR data provided by Jeon et al. (2014) whoconducted a deep imaging with J - and H - bands takenby FLoridA Multi-object ImagingNear-ir Grism Obser-vational Spectrometer (FLAMINGOS: Elston et al.2006) on the Kitt Peak National Observatory (KPNO)2.1 m telescope. This photometric catalog contains295,383 sources with a 5 σ detection limit of 21.6 and21.3 mag in the J - and H - bands, respectively.For MIR data, we used a catalog of Nayyeri et al.(2018) who provided 3.6 µ m (ch1) and 4.5 µ m (ch2) datataken with Infrared Array Camera (IRAC: Fazio et al. Toba et al.
Figure 1.
The footprint of each observation. Red, orange,and yellow shaded regions represent
AKARI
NEP-W (Kimet al. 2012), NEP-D (Murata et al. 2013), and IRAC/
Spitzer (Nayyeri et al. 2018), respectively. Blue, green, black, andmagenta line represent HSC/Subaru (Oi et al. 2020),FLAMINGOS/KPNO (Jeon et al. 2014), PACS/
Herschel (Pearson et al. 2019), and SPIRE/
Herschel (Pearson et al.2017, C. Pearson in preparation), respectively.
Spitzer Space Telescope (Werner etal. 2004). This photometric catalog contains 380,858sources with a 5 σ detection limit of 6.45 and 3.95 µ Jyin the 3.6 µ m and 4.5 µ m bands, respectively.Regarding the FIR data, we utilized a recent catalog of250, 350, and 500 µ m (Pearson et al. 2017, C. Pearsonin preparation). The data were taken with the Spectraland Photometric Imaging REceiver instrument (SPIRE:Griffin et al. 2010) on board the Herschel Space Observa-tory (Pilbratt et al. 2010). This SPIRE catalog contains4,820 sources with a 5 σ detection limit of 9.0, 7.5, and10.8 mJy at 250, 350, and 500 µ m, respectively (Barrufetet al. 2020). Note that AKARI
NEP-D was observedby the Photoconductor Array Camera and Spectrometer(PACS: Poglitsch et al. 2010) at 100 and 160 µ m, andthe catalog (including 1,384 and 630 sources detected by100 and 160 µ m, respectively) is available (Pearson etal. 2019). But since the survey footprint is not so large ( ∼ , see Figure 1), we did not use the catalog inthis work .Eventually, we used 21 photometric data in total; g -, r -, i -, z -, y -, J -, and H -bands and 2.4 (N2), 3.2 (N3),3.6 (ch1), 4.1 (N4), 4.5 (ch2), 7.0 (S7), 9.0 (S9W), 11.0(S11), 15.0 (L15), 18.0 (L18W), 24.0 (L24), 250, 350,and 500 µ m, even though some of them are often upperlimits (e.g., HSC five-bands).2.2. Sample Selection
The procedure for sample selection is summarized inFigure 2. The candidates of
AKARI sources withoutHSC counterparts were drawn from the
AKARI
NEP-W sample in Kim et al. (2012) who provided 114,794sources detected by the IRC. The 5 σ detection limit ofN2, N3, N4, S7, S9W, S11, L15, L18W, and L24 is 15.42,13.30, 13.55, 58.61, 67.30, 93.76, 133.1, 120.2, and 274.4 µ Jy, respectively. Note that we selected candidates andobtained their multi-wavelength information by cross-matching with several catalogs in which coordinates inthe
AKARI
NEP-W catalog were always referred. Inthis work, we attempt a cross-matching with a catalogby using a search radius that is much larger than a typi-cal size of point spread function (PSF) for objects in thecatalog. We then determined a search radius for cross-matching with a catalog as a 3 σ deviation from meanseparation (∆R.A. and ∆ Decl.) between AKARI
NEP-W and that catalog, in the same manner as S. J. Kimet al. (2020, in preparation).We first narrowed the sample to sources within thefootprint observed by the HSC. This is because the HSCdata do not cover quite the whole region of the
AKARI
NEP-W (see Figure 1), and hence some objects are justunobserved by the HSC. As a result, 109,734 objectswere left out. We then removed 84,076 objects with nm > The
AKARI
NEP-W was also partly observed in X-rays with
Chandta (Krumpe et al. 2015), in the ultraviolet with
GALEX (Buat et al. 2017), at 1.4 GHz with the Westerbork Radio Syn-thesis Telescope (White et al. 2010), and at 850 µ m with theSubmillimetre Common User Bolometer Array 2 on the JamesClerk Maxwell Telescope (H. Shim et al., 2020, in preparation).But we focus on physical properties of our sample based on theunifirm optical–IR data in this work. It provides the number of optical sources matched to an
AKARI source within 3 (cid:48)(cid:48) (see Kim et al. 2012).
KARI sources without HSC counterparts AKARI
NEP Wide
HSC survey footprint?Yes109,734 No84,07625,658 Yes rejected5,060No rejected
AKARI witout HSC coumterparts583
Visual Inspection
Cross-matching withHSC with a search radius of 1.8" 20,446Yes rejectedNo5,212
Cross-matching with
Gaia
DR2 with a search radius of 1.6"No4,678S/N ≥ 5 and flag = 0 at least for one
AKARI band nfl = 0and 534Yes rejected
Figure 2.
Flow chart of the process to select
AKARI sourceswithout HSC counterparts. cross-identified the sample with the HSC catalog (Oi etal. 2020), which has a sensitivity roughly 5 times deeperthan optical catalogs used in Kim et al. (2012), provid-ing optically-faint
AKARI sources. By adopting a searchradius of 1.8 (cid:48)(cid:48) , 20,446 objects were cross-identified.For 25 , − ,
446 = 5 , AKARI sources, weremoved contaminants. Because objects with magni-tude brighter than ∼
16 may be saturated in the Mega-Cam/SNUCAM/HSC images, they were removed fromthose catalogs before the cross-matching with
AKARI
NEP-W catalog. Therefore, some
AKARI sources in oursample are expected to be bright stars/galaxies. In orderto remove bright objects, we cross-identified the samplewith
Gaia
DR2 (Gaia Collaboration 2016, 2018) usinga search radius of 1.6 (cid:48)(cid:48) . The
Gaia
DR2 catalog containspoint sources with a g -band magnitude of 3–16. As aresult, 534 bright objects were removed. We then ex-tracted AKARI objects with 5 σ detections in at leastone AKARI band and with clean photometry flag (i.e., flag bandname mag = 0). We also applied nfl = 0 toselect objects unaffected by cosmic rays and/or multi-plexer bleed trails in the NIR bands (see Section 2.2. inKim et al. 2012), which yielded 3,734 objects.Finally, we conducted a visual inspection to selectreliable candidates in which we supplementarily usedmulti-wavelength images in J - and H -bands (FLAMIN-GOS) and ch1 and ch2 (IRAC) in addition to HSC and AKARI images. Consequently, 583 objects were selectedas
AKARI sources without HSC counterparts. Figure 3shows examples of postage stamp images for our sample.Why were 3,734 – 583 = 3,151 objects removed? Onereason is that the edges of the fields of view (FoV) ofHSC may be degraded by artifacts. The
AKARI
NEP-W consists of 4-7 exposures by the HSC with a FoV of1.5 ◦ in diameter through dithering observations. There-fore, objects around the edges of each exposure framewould be missed from the HSC catalog created by theHSC pipeline (Bosch et al. 2018) although they exist onthe HSC image (see Oi et al. 2020, in details). Never-theless, we should note that the visual inspection mightbe highly dependent on the classifier. The number ofobjects selected in this work may have an uncertainty.Hence, the population census results such as numbercounts and volume density of AKARI sources withoutHSC counterparts should be addressed in future work,and we focus on an overview of their physical propertiesin this work. Kim et al. (2012) employed
SExtractor (Bertin & Arnouts1996) for source detection and photometry (see Section 3 in Kimet al. 2012, for more detail).
Toba et al.
Figure 3.
Examples of multi-wavelength images ( g , r , i , z , y , J , H , ch1, ch2, N2, N3, N4, S7, S9W, S11, L15, L18W, andL24, from top left to bottom right) for AKARI sources without HSC counterparts. R.A. and Decl. are relative coordinatewith respect to objects in the
AKARI
NEP-W catalog (Kim et al. 2012). White circles in the images also correspond to thecoordinate of
AKARI
NEP-W catalog.
KARI sources without HSC counterparts
AKARI sources without HSC counterparts,we compiled a multi-wavelength dataset up to 500 µ m. We cross-identified the sample with FLAMINGOS,IRAC/ Spitzer , and SPIRE/
Herschel . In our sample,
AKARI coordinates of each source were always used forthe cross-matching. We employed 1.9 (cid:48)(cid:48) , 2.3 (cid:48)(cid:48) , and 6.6 (cid:48)(cid:48) asthe search radius for cross-matching with FLAMINGOS,IRAC, and SPIRE, respectively. As stated, these searchradii were determined to be 3 σ deviations from meanseparation between AKARI objects without HSC coun-terparts and FLAMINGOS/IRAC/SPIRE catalogs. Ac-cordingly, 70, 338, and 35 objects were cross-identifiedwith FLAMINGOS, IRAC, and SPIRE, respectively.We found that 2/70, 4/338 and 2/35
AKARI ob-jects have two candidate counterparts for FLAMIN-GOS, IRAC, and SPIRE, respectively. In this study,we chose the closest object as the counterpart for suchcases. Among
AKARI -bands, 425/583 (73%), 109/583(19%), and 59/583 (10%) objects are detected in theNIR, MIR-S, and MIR-L, respectively, We note that 23objects with L24 detection naturally satisfy DOG crite-rion, ( r − [24 µ m]) AB > . r -band (26.7) and AKARI µ m (17.8), suggesting that our sample se-lection may preferentially select dusty galaxies (see alsoSection 4.2).2.3. SED Fitting with
CIGALE
We employed
CIGALE to conduct detailed SED model-ing in a self-consistent framework by considering the en-ergy balance between the UV/optical and IR. This codeenables us to handle many parameters, such as star for-mation history (SFH), single stellar population (SSP),attenuation law, AGN emission, dust emission, and ra-dio synchrotron emission (see e.g., Boquien et al. 2014;Buat et al. 2014, 2015; Boquien et al. 2016; Ciesla et al.2017; Lo Faro et al. 2017; Toba et al. 2019a; Burgarellaet al. 2020; Toba et al. 2020a). Note that one of thepurposes of this work is to compare the physical prop-erties between
AKARI sources with and without HSCcounterparts. Wang et al. (2020) constructed
AKARI
L18W-selected sample with HSC counterparts, and alsoconducted the SED fitting with
CIGALE to derive theirphysical properties. Therefore, to compare the resultantquantities under the same conditions, we chose the samemodels with the same parameter ranges as those Wanget al. (2020) adopted, except for AGN fraction in theAGN module and γ in the dust module (see below). Pa-rameter ranges used in the SED fitting are tabulated inTable 2.We used a delayed SFH model, assuming a single star-burst with an exponential decay (Ciesla et al. 2015, Table 2.
Parameter Ranges Used in the SED Fitting with
CIGALE
Parameter ValueDelayed SFH τ main (Myr) 5000.0 τ burst (Myr) 20000 f burst A ISMV ) -2, -1.7, -1.4, -1.1, -0.8, -0.5,-0.2 ,0.1, 0.4, 0.7, 1.0slope ISM -0.9, -0.7, -0.5slope BC -1.3, -1.0, -0.7AGN Emission (Fritz et al. 2006) R max /R min τ . β -0.5 γ θ ψ f AGN q PAH U min α γ z photo τ main ) and the late starburst population( τ burst ), while we parameterized the age of the main stel-lar population in the galaxy.We utilized the stellar templates with solar metallicityprovided from Bruzual & Charlot (2003) assuming theSalpeter (1955) IMF, and the standard default nebularemission model included in CIGALE (see Inoue 2011).Dust attenuation is modeled by using the Charlot &Fall (2000) with two different power-law attenuationcurves that are parameterized by the power law slopeof the attenuation in the interstellar medium (ISM) andbirth clouds (BC). We also separately parameterized the V -band attenuation in the ISM ( A ISMV ).For AGN emission, we used models provided by Fritzet al. (2006). In order to avoid a degeneracy of AGN
Toba et al. templates in the same manner as in Ciesla et al. (2015)and Toba et al. (2019b), we fixed certain parametersthat determine the number density distribution of thedust within the dust torus, i.e., ratio of the maximumto minimum radii of the torus ( R max /R min ), density pro-file along the radial and the polar distance coordinatesparameterized by β and γ (see equation 3 in Fritz et al.2006), and opening angle ( θ ). We parameterized the op-tical depth at 9.7 µ m ( τ . ) and ψ parameter (an anglebetween equatorial axis and line of sight) that corre-sponds to our viewing angle of the torus. We furtherparameterized AGN fraction ( f AGN ), that is the con-tribution of AGN to the total IR luminosity (Ciesla etal. 2015). Note that we adopted a discrete interval for f AGN compared with Wang et al. (2020) in which f AGN is a key parameter to investigate the dependences of thefractional AGN contribution on IR luminosity and red-shift. Our sample is basically detected in only several
AKARI bands (and the remaining bands give upper-limits). This may often not be enough to constrain the f AGN precisely. Hence we reduced the number of possi-ble values of f AGN considered.Dust grain emission is modeled by Draine et al.(2014). The model is parameterized by the mass fractionof PAHs ( q PAH ), the minimum radiation field ( U min ),and the power-low slope of the radiation field distribu-tion ( α ) (see Equation 4 in Draine et al. 2014). We alsoparameterized the fraction illuminated with a variableradiation field ranging from U min to U max ( γ ) althoughWang et al. (2020) fixed this at γ = 1. We confirmedthat parametrizing γ gives a better fit to the FIR partof the spectra.We also parameterized redshift to estimate photomet-ric redshift ( z photo ), because by definition, our sampleis optically too faint to obtain spectroscopic redshifts.Since the optically-dark galaxies tend to be located at z > z photo , which reduces the com-puting time. Aside from being an excellent SED-fittingtool, CIGALE is known to be a good estimator of z photo ,since it utilizes a large number of models covering thewhole SED including the MIR-FIR regime. For example,Ma(cid:32)lek et al. (2014) calculated z photo for AKARI sourcesin the
AKARI
Deep Field South. They demonstratedthat accuracy of the z photo by using the normalized me-dian absolute deviation defined as σ ∆ z/ (1+ z spec ) = 1.48 × median( | ∆ z | /(1+ z spec )) in the same manner as Ilbertet al. (2006). The resultant σ ∆ z/ (1+ z spec ) is 0.056, lowerthan what obtained by a software using mainly opticalto NIR data (Ilbert et al. 2006) (see also Barrufetet al. 2020). On the other hand, the above AKARI
Figure 4.
Normalized histogram of [3.6]-[4.5] color (in Vegamagnitude) of
AKARI sources with (black) and without(red) HSC counterparts. sources have optical counterparts, i.e., they are moder-ately bright in the optical, and the sample is limited tothe local universe ( z < . z photo (see Section 4.6.1).In order to ensure the reliability of the derived physicalquantities including z photo , we extracted AKARI sourceswith 5 σ detections in at least 5 bands among the NIR-FIR, which yields 142/583 objects for SED fitting. If thesignal-to-noise ratio (S/N) at a certain band is greaterthan 5.0, we used the photometry at that band. Oth-erwise, we put 10 σ upper limits that are drawn fromeach catalog (see Section 2.1). RESULTS3.1.
Comparison of IRAC Color
Before the SED fitting, we check the IRAC ch1 andch2 color ([3.6] - [4.5] in Vega magnitudes) of our sam-ple objects. Figure 4 shows IRAC color of 338
AKARI sources without HSC counterparts. We also plottedIRAC colors of
AKARI sources with HSC counterpartswhere we used a multi-wavelength merged
AKARI cat-alog provided by S. J. Kim et al. (2020, in preparation).We removed stars with stellarity parameter (that wasmeasured from CFHT r -band or Maidanak R -band im-ages) greater than 0.8 (see Kim et al. 2012). This left42,264 objects for the comparison. CIGALE can handle SED fitting of photometric data with upperlimits when using the method presented by Sawicki (2012). Thismethod computes χ by introducing the error function (see Equa-tions (15) and (16) in Boquien et al. 2019). KARI sources without HSC counterparts Figure 5.
Examples of the SED fitting. The black points are photometric data. The contribution from the stellar, AGN,and SF components to the total SED are shown as blue, yellow, and red lines, respectively. The black solid line represents theresultant best-fit SED. The inserted panel shows the probability density distribution of redshift.
We find that [3.6]-[4.5] colors of
AKARI sources with-out HSC counterparts (i.e., our sample) are systemat-ically redder than those of
AKARI sources with HSCcounterparts. Blecha et al. (2018) reported that W1(3.4 µ m) and W2 (4.6 µ m) color (almost same as IRACch1 and ch2 color) taken with the the Wide-field InfraredSurvey Explorer ( WISE : Wright et al. 2010), is a goodindicator of AGN activity and nuclear obscuration. TheAGN luminosity and hydrogen column density peak dur-ing the galaxies’ coalescence, where W1–W2 color rangesfrom 0.8 –1.6 (see Figure 1 in Blecha et al. 2018). Thissuggests that a fraction of objects in our sample couldcorrespond to luminous, obscured AGN phase.3.2.
Result of SED Fitting
Figure 5 shows examples of the SED fitting with
CIGALE . We confirm that 91/142 ( ∼ χ < . ∼ χ < .
0. This means that the data are mod-erately well fitted with the combination of the stellar,AGN, and SF components by
CIGALE . We also confirmthat the probability distribution function (PDF) of red-shift does not have prominent secondary peaks for ∼ z photo is about20% (see also Section 4.6.1). Hereafter, we will focus ona subsample of 112 AKARI galaxies with reduced χ oftheir SED fitting smaller than 5.0 in the same manneras Toba et al. (2019b). DISCUSSIONSWang et al. (2020) constructed an 18 µ m (L18W)-selected AKARI sample with HSC counterparts amongwhich 443 objects have
Herschel detections (SPIRE 250 µ m or PACS 100 µ m) and spectroscopic redshifts (Shimet al. 2013; Oi et al. 2017; Kim et al. 2018; Shogakiet al. 2018). In order to derive their physical proper-ties such as AGN luminosity and SFR, they performedSED fitting with CIGALE . What is the difference in thephysical properties between AKARI objects with and We parametrized γ in the dust emission model as described inSection 2.3. For a fair comparison, we re-performed the SED fit-ting with parametrization of γ for the sample provided by Wanget al. (2020). Toba et al.
Figure 6.
Histogram of the stellar mass of
AKARI sourceswith (blue) and without (yellow) HSC counterparts. The redline represents stellar mass of our sample with L18W andSPIRE detections. Green line represents our sample with∆BIC > without HSC counterparts? To ensure the relatabilityof the SED fitting, we will use 317 sources with reduced χ < . Stellar Mass
Figure 6 shows the stellar mass for
AKARI sourceswith and without HSC counterparts. We find that thestellar mass of our sample galaxies is systematicallylarger than that of
AKARI sources with HSC counter-parts (Wang et al. 2020). The average stellar mass of
AKARI sources with and without HSC counterparts islog ( M ∗ /M (cid:12) ) = 10.8 and 11.3, respectively. A two-sidedKolmogorov-Smirnov (K-S) test rules out a hypothesisthat two samples are drawn from the same distributionat > M ∗ /M (cid:12) )is 11.7, also significantly larger than sample in Wang etal. (2020), which is also confirmed by the K-S test with > Dust Attenuation in the ISM
Figure 7.
Histogram of the V -band attenuations in theISM of AKARI sources with (blue) and without (yellow)HSC counterparts. The red line represents A ISMV of oursample with L18W and SPIRE detections. Green line rep-resents our sample with ∆BIC > < log ( M ∗ /M (cid:12) ) <
12 are plotted in each sample.
We then compare the V -band attenuation in the ISM( A ISMV ) for
AKARI sources with and without HSC coun-terparts. Before the comparison, we should note that thedust attenuation may depend on the stellar mass (e.g.,Buat et al. 2012, 2015), and the stellar mass distribu-tions in the two samples are different as discussed inSection 4.1. Therefore, we extracted objects in an over-lapped stellar mass range, i.e., 10 < log ( M ∗ /M (cid:12) ) < AKARI sample with/without HSC counterparts(see Figure 6).Figure 7 shows the V -band attenuation in the ISM( A ISMV ) for
AKARI sources with and without HSC coun-terparts. These objects are stellar-mass matched sam-ples. The average A ISMV of AKARI sources with andwithout HSC counterparts is 1.26 and 5.16 mag, respec-tively. We find that the attenuation of our sample issystematically much larger than that of
AKARI sourceswith HSC counterparts (Wang et al. 2020), which issupported by the K-S test with > AKARI objects in Wang et al. (2020) and this studyare different (see Section 4.3). Given an overlapped red-shift range (0 . < z < .
0) between two samples (seeFigure 8), the average A ISMV of sample in Wang et al.(2020) and this work is 1.77 and 5.23 mag, respectively.This suggests that our sample is intrinsically affected bydust extinction compared with
AKARI with HSC coun-terparts.
KARI sources without HSC counterparts Figure 8.
IR luminosity contributed from AGN as a func-tion of redshift. Blue and yellow points represent
AKARI sources with and without HSC counterparts, respectively.Yellow and red circles represent our sample with L18W andSPIRE detections. Yellow and green circles represent oursample with ∆BIC > AGN Luminosity as a Function of Redshift
Figure 8 shows the IR luminosity contributed fromAGN as a function of redshift. We find that
AKARI objects without HSC counterparts tend to be located inhigher redshifts compared to those with HSC counter-parts: the mean redshift of
AKARI objects with andwithout HSC counterparts is z ∼ AKARI
NEP-W catalog (Kim et al. 2012). Thus
AKARI sources are expected to lie in the same sequence on theredshift–luminosity plane, regardless of HSC detection.We confirm that samples in Wang et al. (2020) andthis work are continuously distributed in that plane, in-dicating that
CIGALE securely estimated redshift and lu-minosity. Indeed, given the overlapped redshift range of0 . < z < .
0, the mean AGN luminosity of our sam-ple is log( L IR (AGN) /L (cid:12) ) ∼ AKARI sources with-out HSC counterparts is also larger than those withHSC counterparts, with a mean log ( L IR /L (cid:12) ) of 11.4and 12.2, respectively. Actually, about 65% and 15%of our sample is ultra-luminous IR galaxies (ULIRGs:Sanders & Mirabel 1996) and hyper-luminous IR galax-ies (HyLIRGs: Rowan-Robinson 2000) with L IR greaterthan 10 and 10 L (cid:12) , respectively. This is in goodagreement with a fact that dusty galaxies with extremeoptical–IR color tend to be luminous in the IR (e.g., Figure 9.
Histograms of AGN fraction for
AKARI sourceswith (blue) and without (yellow) HSC counterparts. The redline represents f AGN of our sample with L18W and SPIREdetections. Green line represent f AGN of our sample withBIC > Tsai et al. 2015; Toba & Nagao 2016; Toba et al. 2018;Fan et al. 2020; Toba et al. 2020b).4.4.
AGN fraction
Figure 9 shows the AGN fraction ( f AGN ), L IR (AGN)/ L IR . The mean AGN fraction of AKARI sources withand without HSC counterparts is 0.06 and 0.22, respec-tively, indicating our sample tends to harbor AGNs. Wealso find that f AGN of objects with L18W and SPIRE de-tection is 0.16, smaller than average of all objects in oursample. On the other hand, given the overlapped red-shift range (0 . < z < . f AGN of AKARI sources with and without HSC counterparts is 0.12 and0.23, respectively. This could indicate that one of thereasons for the difference in f AGN between the two sam-ples may be the difference in sample selection and red-shift. Other possible uncertainties caused by a limitednumber of MIR data are discussed in Section 4.6.2.4.5.
Star Formation Rate
Finally, we compare the SFR in two samples as shownin Figure 10. The SFR is converted from dust luminosityusing a relation provided by Kennicutt (1998) (see alsoHirashita et al. 2003).We find that the SFR of our sample is systematicallyhigher than that of
AKARI with HSC objects. Theaverage SFR of
AKARI objects with and without HSCcounterparts is about 91 and 758 M (cid:12) yr − , respectively.If we focus on our sample with L18W and SPIRE de-tections, the mean SFR is 2240 M (cid:12) yr − because ofrequirement of FIR detection. These results are consis-tent with previous works reporting that SFRs of dustygalaxies tend to be high (e.g., Ikarashi et al. 2017; Tobaet al. 2017b; Yamaguchi et al. 2019; Fan et al. 2020).2 Toba et al.
Figure 10.
Histograms of SFR for
AKARI sources with(blue) and without (yellow) HSC counterparts. The red andgreen lines represent the SFR of our sample with L18W andSPIRE detections, and with ∆BIC > On the other hand, if we compare SFRs of sample in thiswork and Wang et al. (2020) in an overlapped redshiftrange (0 . < z < . AKARI withand without HSC counterparts is 319 and 207 M (cid:12) yr − ,respectively. This indicates that the observed differencein SFR may be due to the redshift difference.4.6. Reliability of the SED Analysis
Influence of Dataset without Optical Photometry onthe SED-based Photometric Redshift
We showed that our estimate of z photo is expectedto be secure in some ways (see Sections 2.3 and 4.3),but these arguments are indirect. Although Ma(cid:32)lek etal. (2014) demonstrated the accuracy of z photo basedon CIGALE for an
AKARI sample at z spec < .
25, theyutilized optical data points for z photo estimation, andthe redshift range for the sample differs from our sam-ple. We need to investigate how the lack of opticaldata affects the accuracy of z photo for AKARI objectsat z > .
8. Hence, we estimate z photo of AKARI ob-jects with z spec in the band-merged catalog (S. J. Kimet al. 2020, in preparation). In order to calculate the z photo under the same condition as we performed so far,we extracted objects with z spec > . σ upper limits. We then executed theSED fitting with CIGALE in the exactly same manner aswhat we described in Section 2.3.Figure 11 shows the relative difference between z photo and z spec , i.e., ( z spec − z photo ) / (1 + z spec ) as a functionof z spec . The mean and standard deviation of ( z spec − z photo ) / (1 + z spec ) is 0 . ± .
45, and the accuracy of
Figure 11.
Relative difference between z photo and z spec ,( z spec − z photo ) / (1 + z spec ) as a function of z spec .The red solidline represents z photo − z spec = 0 and the dotted lines repre-sent ( z spec − z photo ) / (1 + z spec ) = ± .
2. The inserted panelshows the histogram of ( z spec − z photo ) / (1 + z spec ). z photo , σ ∆ z/ (1+ z spec ) is 0.23, which are worse than thosereported by Ma(cid:32)lek et al. (2014). We find that ∼ | ( z spec − z photo ) | / (1 + z spec ) < .
2. Thismay be partly because the AKARI–HSC objects at z > . z photo is harder to estimate precisely, comparedwith other galaxy types. Actually, ∼
90% of objectsplotted in Figure 11 are spectroscopically confirmed type1 AGNs, which may induce a large deviation of ( z spec − z photo ) / (1 + z spec ).4.6.2. Bayesian Information Criterion
An another potential issue raised by the SED fittingwith a limited number of data points in the MIR may bean uncertainty of AGN contribution to the total SED, al-though we used upper-limits at a MIR band if an objectis not detected at that band. To test the requirementto add an AGN component to the SED fitting, we com-pute the Bayesian information criterion (BIC) for twofits that are derived with and without AGN component.The BIC is defined as BIC = χ + k × ln( n ), where χ is non-reduced chi-square, k is the number of degreesof freedom (DOF), and n is the number of photometricdata points used for the fitting, respectively. We thencompare the results of two SED fittings without/withAGN module by using ∆BIC = BIC woAGN – BIC wAGN .The resultant ∆BIC tells whether the AGN model is re-quired to give a better fit with taking into account thedifference in DOF (e.g., Ciesla et al. 2018; Buat et al.2019, see also Aufort et al. 2020).Figure 12 shows the histogram of ∆BIC for AKARI sources without HSC counterparts. If ∆BIC is largerthan two, this indicates that adding the AGN compo-
KARI sources without HSC counterparts Figure 12.
Histogram of ∆BIC = BIC woAGN –BIC wAGN for
AKARI sources without HSC counterparts. The red dottedline corresponds to ∆BIC = 2. nent provides a better fit than without adding it (Liddle2004; Stanley et al. 2015) (see also Ciesla et al. 2018;Buat et al. 2019 who set a higher threshold for ∆BIC).Otherwise, there is no significant difference between twofits with/without AGN model. We find that only 31%of object fits satisfy ∆BIC >
2. This suggests it may bedifficult to constrain the AGN activity for many cases,and thus AGN fraction may have a large uncertaintygiven a limited number of photometric detections in theMIR regime. On the other hand, Figures 6–10 also showobjects with ∆BIC >
2. We find that there is no sys-tematic difference between objects with ∆BIC > Mock Analysis
Finally, we check whether or not the derived phys-ical properties in this work can actually be estimatedreliably, given the uncertainty of the photometry. Weconduct a mock analysis that is a procedure providedby
CIGALE (see e.g., Buat et al. 2012, 2014; Ciesla et al.2015; Lo Faro et al. 2017; Boquien et al. 2019; Toba etal. 2019b, for more detail).Figure 13 shows the differences in z photo , V -band at-tenuation in the ISM ( A V ), stellar mass, SFR, and L IR (AGN) derived from CIGALE in this work and thosederived from the mock catalog as a function of red-shift. The mean and standerd deviations of ∆ z photo ,∆ A ISMV , ∆ log M ∗ , ∆ log SFR, and ∆ log L IR (AGN) are∆ z photo = 0 . ± .
47, ∆ A ISMV = − . ± . M ∗ = 0 . ± .
37, ∆ log SFR = 0 . ± .
43, and∆ log L IR (AGN) = 0.11 ± z < . Figure 13.
The differences in z photo , V -band attenuationin the ISM, stellar mass, SFR, and L IR (AGN) derived fromCIGALE in this work and those derived from the mock cat-alog. (a) ∆ z photo , (b) ∆ A ISMV , (c) ∆ log M ∗ , (d) ∆ log SFR,and (e) ∆ log L IR (AGN) as a function of redshift. The rightpanels show a histogram of each quantity. The red dottedlines are the ∆= 0. fitting especially for objects at z < . z > . SUMMARYIn this paper, we report the physical properties of
AKARI sources that do not have optical counterpartsin the HSC/Subaru catalog (Oi et al. 2020). Theparent sample is drawn from IR sources in the
AKARI
NEP-W field (Kim et al. 2012). By using
AKARI ,HSC,
Gaia , FLAMINGOS/KPNO and IRAC/
Spitzer catalogs and images, we select 583 objects as optically-dark IR sources without HSC counterparts. Thanks tothe continuous filters of
AKARI in the MIR and multi-wavelength data up to the FIR (SPIRE/
Herschel ), wesuccessfully pin down their optical–FIR SEDs, even ifflux densities in some bands are upper limits. We com-4
Toba et al. pare the physical properties derived by the
CIGALE
SEDfitting between
AKARI objects without HSC counter-parts and mid-IR selected
AKARI objects with HSCcounterparts (Wang et al. 2020). We find that
AKARI sources without HSC counterparts have sys-tematically redder 3.6 − µ m color compared with AKARI sources with HSC counterparts. With all thecaveats discussed in Section 4.6 in mind, we find thatour sample tends to be located at high redshifts upto z ∼
4, and has larger AGN luminosity, SFR, and V -band dust attenuation in the ISM, compared with AKARI sources with HSC counterparts. Although thisis partly due to the Malmquist bias, these results indi-cate that
AKARI objects without HSC counterparts areheavily dust-obscured SFGs/AGNs at z ∼ James Webb Space Telescope ( JWST : Gardner et al. 2006) is approaching, and
AKARI
NEP should be an attractive field for
JWST (e.g., Jansen & Windhorst 2018). Since the
AKARI
NEP has multi-wavelength data from X-ray to radio, inwhich the filter sets of
AKARI are similar to those of
JWST , this work establishes a benchmark for forthcom-ing dusty SFGs/AGNs studies with
JWST and providesspecially interesting optically dark IR sources for
JWST study. ACKNOWLEDGMENTSWe gratefully acknowledge the anonymous referee fora careful reading of the manuscript and very helpfulcomments.This research is based on observations with AKARI,a JAXA project with the participation of ESA.This work has made use of data from the Euro-pean Space Agency (ESA) mission
Gaia
Gaia
Gaia
Multilateral Agreement.This work is based on observations made with theSpitzer Space Telescope, which is operated by the JetPropulsion Laboratory, California Institute of Technol-ogy under a contract with NASA. Support for this workwas provided by NASA through an award issued byJPL/CaltechHerschel is an ESA space observatory with science in-struments provided by European-led Principal Investi-gator consortia and with important participation fromNASA.Numerical computations/simulations were carried out(in part) using the SuMIRe cluster operated by the Ex-tragalactic OIR group at ASIAA.This work is supported by JSPS KAKENHI Grantnumbers 18J01050 and 19K14759 (Y.Toba), JP18J40088(R.Momose), and 17K05384 (Y.Ueda). Y.Toba andT.Goto acknowledge the support by the Ministry ofScience and Technology of Taiwan, MOST 108-2112-M-001-014- and 108-2628-M-007-004-MY3. R.Momoseacknowledges a Japan Society for the Promotion of Sci-ence (JSPS) Fellowship at Japan. T.Hashimoto is sup-ported by the Centre for Informatics and Computationin Astronomy (CICA) at National Tsing Hua University(NTHU) through a grant from the Ministry of Educa-tion of the Republic of China (Taiwan). T.Miyaji issupported by CONACyT 252531 and UNAM-DGAPA(PASPA and PAPIIT IN111319).
Facilities:
AKARI , Subaru, KPNO:2.1m,
Spitzer , Herschel . Software:
IDL, IDL Astronomy User’s Library(Landsman 1993),
CIGALE (Boquien et al. 2019),
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