A&A 646, A29 (2021)https://doi.org/10.1051/0004-6361/202039401c© ESO 2021

Astronomy&Astrophysics

X-ray flux in SED modelling: An application of X-CIGALE in theXMM-XXL field

G. Mountrichas1, V. Buat2,3, G. Yang4,5, M. Boquien6, D. Burgarella2, and L. Ciesla2

1 Instituto de Fisica de Cantabria (CSIC-Universidad de Cantabria), Avenida de los Castros, 39005 Santander, Spaine-mail: gmountrichas@gmail.com

2 Aix Marseille Univ., CNRS, CNES, LAM, Marseille, Francee-mail: veronique.buat@lam.fr

3 Institut Universitaire de France (IUF), France4 Department of Physics and Astronomy, Texas A&M University, College Station, TX 77843-4242, USA5 George P. and Cynthia Woods Mitchell Institute for Fundamental Physics and Astronomy, Texas A&M University, College Station,

TX 77843-4242, USA6 Centro de Astronomía (CITEVA), Universidad de Antofagasta, Avenida Angamos 601, Antofagasta, Chile

Received 11 September 2020 / Accepted 18 November 2020

ABSTRACT

X-CIGALE is built on the spectral energy distribution (SED) code of CIGALE and implements important new features: the codeaccounts for obscuring material in the poles of AGNs and has the ability to fit X-ray fluxes. In this work, we used ∼2500 spectroscopicX-ray AGNs from the XMM-XXL-north field and examined the improvements the new features bring to the SED modelling analysis.Based on our results, X-CIGALE successfully connects the X-ray with the UV luminosity in the whole range spanned by our sample(log LX(2−10 keV) = (42−46) erg s−1). The addition of the new features generally improves the efficiency of X-CIGALE in theestimation and characterisation of the AGN component. Classification as type 1 or type 2 based on their inclination angle is improved,especially at redshifts lower than 1. The statistical significance of AGN fraction, fracAGN, measurements is increased, in particularfor luminous X-ray sources (LX > 1045 erg s−1). These conclusions hold under the condition that (mid-) IR photometry is available inthe SED fitting process. The addition of polar dust increases the AGN fraction and the efficiency of the SED decomposition to detectAGNs among X-ray selected sources. X-CIGALE estimates a strong AGN (fracAGN > 0.3) in more than 90% of the IR-selected AGNsand 75% of X-ray-detected AGNs not selected by IR colour criteria. The latter drops to ∼50% when polar dust is not included. Theability of X-CIGALE to include X-ray information in the SED fitting process can be instrumental in the optimal exploitation of thewealth of data that current (eROSITA) and future (ATHENA) missions will provide us.

Key words. galaxies: active – X-rays: galaxies – methods: data analysis – methods: observational – X-rays: general

1. Introduction

One of the major challenges in current astrophysical research isunderstanding the physical processes that operate on top of thedark matter distribution to produce luminous structures, such asstars and galaxies. Baryonic physics is highly complex, mainlybecause it includes electromagnetic interactions; for instance,the interplay between the cooling and heating of baryons (e.g.,gas of dust particles) via radiative processes, or the impact ofmagnetic fields on charged particles. However, such processesoperate on spatial scales many orders of magnitude smaller thanwhat can be achieved by state-of-the-art cosmological simula-tions. Thus, assumptions have to be made on the amplitude andimpact of the multitude of possible mechanisms that may affectthe formation and evolution of galaxies; for example, gas sup-ply, gas cooling and heating, and the impact of stellar winds onthe interstellar medium. These physical processes that govern thebirths and fates of galaxies are of extreme complexity. However,they are of great interest since galaxies play a pivotal role in thestructure of the Universe and are unique tracers of its evolution.

One manifestation of baryonic physics is the formation ofsupermassive black holes (SMBHs) at the centre of a galaxy.Tight correlations have been found between the mass of the

SMBH and the properties of its bulge (e.g., Magorrian et al.1998; Ferrarese & Merritt 2000). When material is accreted ontothese SMBHs, it triggers them, and the galaxy is called an activegalactic nucleus (AGN). The energy released during the accre-tion process is also an important source of heating for both theinterstellar (e.g., Morganti 2017) and intergalactic medium (e.g.,Kaastra et al. 2014). As a result, it has been hypothesised thatSMBHs and their activity play an important role in galaxy evo-lution (e.g., Brandt & Alexander 2015).

Active galactic nucleus emission can be observed at differentwavelengths from X-rays to radio, as different physical mecha-nisms produce radiation at different wavelengths. In particular,X-ray emission is a trademark of AGN activity. This emissionoriginates from photons produced by the accretion disc that arescattered by the hot corona and emit X-rays through inverseCompton scattering. This process dominates the X-ray emis-sion of the host galaxy and reflects the activity of the centralSMBH. Thus, X-rays are often used as a proxy of AGN power(e.g., Lusso et al. 2012; Yang et al. 2019). Important ongoing(e.g., eROSITA) and future (ATHENA) X-ray missions will usethe unique window that X-rays offer and provide us a wealthof data to study the tight connection of AGNs with their hostgalaxies.

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The multi-wavelength emission of galaxies can be studiedby constructing and modelling their full spectral energy distri-bution (SED). This method allows us to measure fundamen-tal properties of galaxies, such as their stellar mass M∗, starformation rate (SFR), dust mass, and attenuation, while at thesame time it breaks degeneracies that plague observations innarrow(er) wavelength ranges. To this end, a number of algo-rithms have been developed to perform this task via differ-ent approaches. A popular approach is based on the energybalance principle, that is, the energy emitted in the infrared(IR; i.e. 5−1000 µm) is equal to the energy absorbed in theUV/optical wavelengths; for example, CIGALE (Code Inves-tigating GALaxy Emission; Burgarella et al. 2005; Noll et al.2009; Boquien et al. 2019), ProSpect (Robotham et al. 2020),MAGPHYS (da Cunha et al. 2008), Prospector (Leja et al.2017), and BAGPIPES (Carnall et al. 2018).

As mentioned, AGNs play an important role in galaxyevolution, and their presence affects many parts of the electro-magnetic spectrum. Thus, a number of the aforementioned SEDfitting algorithms have added an AGN component to the fittingprocess (e.g., CIGALE, ProSpect) to separate AGNs and galaxyemission. SED algorithms that don’t include an AGN SED com-ponent and only account for low-luminosity/obscured AGNs arebiased against luminous/unobscured sources and underestimatethe contribution of the AGN IR emission to the total IR galaxyemission.

In a recent paper, Yang et al. (2020) presented a new branchof the CIGALE code, X-CIGALE. Compared to CIGALE,X-CIGALE is supplemented with the modelling of AGN X-rayemission and the inclusion of polar dust. Polar dust accounts forextinction of UV and optical radiation, that is commonly found,in particular, in X-ray selected AGNs (Bongiorno et al. 2012).SED fitting algorithms that include X-ray information could beinstrumental in the exploitation and interpretation of the largedatasets that X-ray surveys will provide.

The goal of this paper is to use the new capabilities of theX-CIGALE code on one of the largest X-ray samples avail-able (XMM-XXL; Pierre et al. 2016). XXL offers a significantlywider luminosity baseline that extends to higher luminositiescompared to the fields studied in Yang et al. (2020). Addition-ally, the size of the database allows us to draw statistically robustconclusions in our tests. The physical properties of the XXLAGNs and their host galaxies, using the CIGALE code, havebeen the topic of previous studies (e.g., Masoura et al. 2018,2020, 2021). In this work, we focused on the effect of the newfeatures of X-CIGALE on important SED fitting parameters. Ourmain purpose is to examine how reliably the algorithm connectsthe X-ray flux with the UV luminosity and the rest of the otherwavelengths, how accurately X-CIGALE can reproduce theX-ray properties of the AGNs, and what improvements the newadditions bring in its efficiency on SED decomposition.

2. Data

In this section, we describe the X-ray AGN sample used in ouranalysis and the methodology we followed to obtain optical andIR identifications.

2.1. The X-ray AGN sample

Throughout our work, we used spectroscopic X-ray AGNs fromthe XMM-XXL field (Pierre et al. 2016). XXL is an interna-tional project based around an XMM Very Large Programmesurveying two 25 deg2 extragalactic fields. It has a depth of

0 0 . 5 1 . 0 1 . 5 2 . 0 2 . 5 3 . 0 3 . 5 4 . 0 4 . 5 5 . 0

redshif t

0

2 0

4 0

6 0

8 0

1 0 0

1 2 0

1 4 0

1 6 0

Fig. 1. Redshift distribution of the X-ray AGN sample.

∼6× 10−15 erg cm −2 s−1 in the [0.5−2] keV band for point-likesources, with an exposure time of about 10 ks per XMM point-ing. 8445 X-ray sources have been detected in the equato-rial subregions (XMM-XXL-N; Liu et al. 2016). 5294 of themhave SDSS counterparts. Spectroscopic redshifts are availablefor 2512 AGNs (Menzel et al. 2016) from SDSS-III/BOSS(Eisenstein et al. 2011; Smee et al. 2013; Dawson et al. 2013).These spectroscopic sources are used in our analysis. Their red-shift distribution is presented in Fig. 1.

2.2. IR Photometry

In addition to the optical (SDSS) photometry available for allour sources, we also searched for counterparts in the near-IR, mid-IR, and far-IR part of the spectrum. Mid-IR (all-WISE; Wright et al. 2010) and near-IR photometry from theVisible and Infrared Survey Telescope for Astronomy (VISTA;Emerson et al. 2006) were obtained using the likelihood ratiomethod (e.g., Sutherland & Saunders 1992) as implemented inGeorgakakis et al. (2011). The process is described in detail inGeorgakakis et al. (2017).

We also used catalogues produced by the HELP1 Collab-oration to complement our mid-IR photometry with Spitzer(Werner et al. 2004) observations, and we added far-IR coun-terparts. HELP provides homogeneous and calibrated multi-wavelength data over the Herschel Multi-tiered ExtragalacticSurvey (HerMES, Oliver et al. 2012) and the H-ATLAS sur-vey (Eales et al. 2010). The strategy adopted by HELP is tobuild a master list catalogue of objects as complete as pos-sible for each field (Shirley et al. 2019) and to use the near-IR sources from IRAC surveys as prior information for the IRmaps. The XID+ tool (Hurley et al. 2017), developed for thispurpose, uses a Bayesian probabilistic framework and workswith prior positions. At the end, a flux is measured, in a prob-abilistic sense, for all the near-IR sources of the master list. TheXMM-LSS field was covered by two Spitzer surveys, SpUDS(Spitzer UKIDSS Ultra Deep Survey, Caputi et al. 2011) andSWIRE/SERVS (Lonsdale et al. 2003). The prior positions aredefined with the SpUDS and SWIRE/SERVS surveys, and thefluxes are measured for the Spitzer MIPS/24 microns, HerschelPACS and SPIRE bands. In this work, only the MIPS and SPIRE

1 The Herschel Extragalactic Legacy Project (HELP; http://herschel.sussex.ac.uk/) is a European-funded project to analyseall the cosmological fields observed with the Herschel satellite. All theHELP data products can be accessed on HeDaM (http://hedam.lam.fr/HELP/).

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Table 1. Number of available spectroscopic sources in different photometric bands.

Total W1, W2/IRAC1, IRAC2 IRAC3 IRAC4 W3 MIPS1 Herschel All bands

2509 2342 1122 1135 1240 1298 1276 408

Notes. 2342/2509 (∼93%) of the sources, have optical and mid-IR photometry available (either W1, W2 or IRAC1, IRAC2). 1276 (∼51%) havefar-IR photometry available (and mid-IR).

Table 2. 5σ sensitivity (mag) of the photometric bands used in our analysis.

u, g, r, i, z J, H, K W1, W2, W3, W4 IRAC1, IRAC2, IRAC3, IRAC4 MIPS1 SPIRE

22.15, 23.13, 22.70, 22.20, 20.71 20.6, 19.8, 18.5 16.83, 15.60, 11.32, 8.0 24.09, 23.66, 21.90, 21.85 19.8 13.9, 13.5, 13.2

fluxes were considered, given the much lower sensitivity of thePACS observations for this field (Oliver et al. 2012).

The W1 and W2 photometric bands of WISE nearly overlapwith IRAC1 and IRAC2 from Spitzer. When a source had beendetected by both IR surveys, we only considered the photometrywith the highest signal-to-noise ratio (S/N). Similarly, when bothW4 and MIPS photometry is available, we only considered thelatter due to the higher sensitivity of Spitzer compared to WISE.The number of sources available in each photometric band of ourfinal sample is presented in Table 1. Table 2 shows the sensitivityof each photometric band used in our analysis.

3. Analysis

X-CIGALE requires the intrinsic (i.e. unabsorbed) flux of X-rayAGNs. In this section, we describe how we estimated the X-rayabsorption of the sources to infer their intrinsic X-ray flux. Wealso describe the modules and parameters used for the SED fit-ting process and present our final sample.

3.1. Estimation of the X-ray properties

To estimate the intrinsic X-ray flux of each source, we needto measure their hydrogen column density, NH, which quanti-fies the X-ray absorption. To this end, we used the number ofphotons in the soft (0.5−2.0 keV) and the hard (2.0−8.0 keV)bands that are provided in the Liu et al. (2016) catalogue. Wethen applied a Bayesian approach to estimate the hardness ratio,HR = H−S

H+S , of each source, where H and S are the counts inthe soft and hard bands, respectively. Specifically, we utilisedthe Bayesian Estimation of Hardness Ratios (BEHR; Park et al.2006) code, which accounts for the Poissonian nature of theobservations. These hardness ratio values are then inserted in thePortable, Interactive, Multi-Mission Simulator (PIMMS; Mukai1993) tool to estimate the NH of each source. In this process,we assume that the power law of the X-ray spectra has a fixedphoton index, Γ = 1.8 and the value of the galactic NH isNH = 1020.25 cm−2. The distribution of NH of our AGN sampleis presented in Fig. 2. Due to the Bayesian nature of our cal-culations, some log NH values are below 20.25 (i.e. the galacticabsorption).

The X-ray absorption that was estimated through PIMMS,and based on which the intrinsic X-ray flux is inferred, does notnecessarily correlate with the dust that the observed mid- andfar-IR emission exhibits. Although there are studies that havefound a correlation between optical/IR obscuration and X-rayabsorption (e.g., Civano et al. 2012), this correlation presentsa large scatter (e.g., Jaffarian & Gaskell 2020). This scatter is

19:5 20:0 20:5 21:0 21:5 22:0 22:5 23:0 23:5 24:0

logNH (cm¡2)

0

20

40

60

80

100

120

140

160

180

200

220

Fig. 2. Distribution of X-ray absorption, NH, of the X-ray AGN sample.

attributed to, for example, (i) the X-ray column density vari-ability (e.g., Reichert et al. 1985; Yang et al. 2016); (ii) absorb-ing material located at galactic scales (e.g., Malizia et al. 2020)that is mainly heated by star formation rather than AGN; (iii)the fact that different obscuration criteria are sensitive to dif-ferent amounts of obscuration (e.g., Masoura et al. 2020); and(iv) a dust-to-gas ratio that is different from the Galactic dust-to-gas ratio. Therefore, a source may present X-ray absorptionwithout being optically red (dust-free gas), be heavily X-rayabsorbed with broad UV/optical lines (e.g., Li et al. 2019), canbe optically red with absorbed AGN emission in its SED with-out being X-ray absorbed (e.g., Masoura et al. 2020), and canpresent higher X-ray absorption than what is expected from itsoptical extinction. Thus, we do not necessarily expect consis-tency between the X-ray absorption estimated through PIMMSwith that from dust, which is estimated by X-CIGALE by mod-elling the mid- and far-IR emissions.

3.2. SED fitting with X-CIGALE

The fitting capabilities of CIGALE were recently extended toX-rays with the development of X-CIGALE (Yang et al. 2020)in order to improve the characterisation of the AGN compo-nent. The X-ray emission is connected to the AGN emissionat other wavelengths via the αox−L2500 Å relation of Just et al.(2007), where L2500 Å is the intrinsic (de-reddened) UV luminos-ity, and αox is the spectral slope between UV (2500 Å) and X-ray(2 keV): αox = −0.3838 log (L2500 Å/L2 keV). The contribution ofX-ray binaries is also considered and modelled as a function

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Table 3. Models and the values for their free parameters used by X-CIGALE for the SED fitting of our galaxy sample.

Parameter Model/values

Star formation history: delayed modele-folding time 100, 500, 1000, 5000Stellar age 500, 1000, 3000, 5000, 7000

Simple stellar population: Bruzual & Charlot (2003)Initial mass function Chabrier (2003)Metallicity 0.02 (Solar)

Galactic dust extinctionDust attenuation law Calzetti et al. (2000)Reddening E(B−V) 0.0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9

Galactic dust emission: Dale et al. (2014)α slope in dMdust ∝ U−αdU 1.0, 1.5, 2.0, 2.5, 3.0

AGN module: SKIRTORTorus optical depth at 9.7 microns τ9.7 7.0Torus density radial parameter p (ρ ∝ r−pe−q| cos(θ)|) 1.0Torus density angular parameter q (ρ ∝ r−pe−q| cos(θ)|) 1.0Angle between the equatorial plan and edge of the torus 40◦Ratio of the maximum-to-minimum radii of the torus 20Viewing angle 30◦ (type 1), 70◦ (type 2)AGN fraction 0.0, 0.01, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 0.99Extinction law of polar dust SMCE(B−V) of polar dust 0.0, 0.01, 0.02, 0.03, 0.05, 0.1, 0.2, 0.4, 0.6, 1.0, 1.8Temperature of polar dust (K) 100Emissivity of polar dust 1.6

X-ray moduleAGN photon index Γ 1.8Maximum deviation from the αox−L2500 Å relation 0.2LMXB photon index 1.56HMXB photon index 2.0

Notes. For the definition of the various parameters, see Sect. 3.2.

of SFR and stellar mass of the host galaxy. The clumpy two-phase torus model, SKIRTOR, based on 3D radiation-transfer(Stalevski et al. 2012, 2016) is used for the UV-to-far-IR emis-sion of the AGN with some modifications keeping the energybalance: the original emission of the accretion disc is updatedwith the spectral energy distribution of Feltre et al. (2012), anddust extinction and emission in the poles of type 1 AGNsare also considered (e.g., Bongiorno et al. 2012; Tristram et al.2014; Asmus et al. 2014; Asmus 2019). We refer the reader toYang et al. (2020) for a full description of X-CIGALE. Here, wedescribe the main steps followed to build the models and fit ourX-ray to far-IR data. The modules and input parameters we usedin our analysis are presented in Table 3.

3.2.1. Galaxy emission

For the sake of simplicity and since we did not study the SFRproperties of our sources in detail, we adopted a simple starformation history (SFH). The galaxy component is built usinga delayed SFH (SFR ∝ t × exp(−t/τ)). We checked that theaddition of a recent burst (e.g., Masoura et al. 2018; Małek et al.2018) did not change our results. The stellar emission is mod-elled using the Bruzual & Charlot (2003) template. A Chabrier(2003) initial mass function (IMF) was used, with a metallicityequal to 0.02. The stellar emission was attenuated with theCalzetti et al. (2000) attenuation law. The IR SED of the dustheated by stars was implemented with the Dale et al. (2014)template.

3.2.2. AGN emission

The AGN emission was modelled using the SKIRTOR template(Stalevski et al. 2012, 2016). A polar dust component modelledas a dust screen absorption and a grey-body emission was added.As in Yang et al. (2020), we adopted the Small Magellanic Cloud(SMC; Prevot et al. 1984) extinction curve with E(B−V) as a freeinput parameter (see Sect. 5 for the effects of E(B−V) on SEDfitting). The grey-body dust re-emission is parametrised with atemperature of 100 K and an emissivity index of 1.6. This emis-sion is supposed to be isotropic and thus contributes to the IRemission of both type 1 and type 2 AGNs. We discuss the effectof a modification of the dust temperature and of the extinctioncurve in Sect. 4.3. The contribution of the AGN to the total SEDis quantified by the AGN fraction, fracAGN, defined as the frac-tion of the total IR emission coming from the AGN. FollowingYang et al. (2020), two viewing angles are considered, 30◦ and70◦, for type 1 and type 2 AGNs, respectively.

The photon index Γ of the AGN X-ray spectrum was fixedto 1.8, for consistency with the value used for the estimationof NH (see Sect. 3.1). We adopted a maximal acceptable value|∆αox|max = 0.2 for the dispersion of the αox−L2500 Å relation(Risaliti & Lusso 2017). This value is also adopted in Yang et al.(2020) and corresponds to ≈2σ scatter in the αox−L2500 Å relation(Just et al. 2007). Nine values of αox were defined (from −1.9 to−1.1 with a step of 0.1), and X-CIGALE added an X-ray flux toeach pair (UV to far-IR AGN SED, αox), multiplying the numberof AGN models by 9.

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Table 4. Number of AGNs that satisfy our selection criteria (Sect. 3.3) for different configurations of the SED fitting process.

Total fX, polar dust No fX, polar dust fX, no polar dust No fX, no polar dust

2509 2348 2274 2337 2274

42 43 44 45 46

log LX; 2¡10 keV[erg s¡1] ¡ data

42

43

44

45

46

logL

X;2

¡10ke

V[e

rgs¡

1]¡

X¡

CIG

ALE

z < 11 < z < 2z > 2

20

21

22

23

24

logN

H(c

m¡

2)

with fX

42 43 44 45 46

log LX; 2¡10 keV[erg s¡1] ¡ data

42

43

44

45

46

logL

X;2

¡10ke

V[e

rgs¡

1]¡

X¡

CIG

ALE

z < 11 < z < 2z > 2

20

21

22

23

24

logN

H(c

m¡

2)

without fX

Fig. 3. Comparison of intrinsic X-ray luminosity in the 2−10 keV band estimated by the SED fitting with that from the input catalogue. Green,solid, and dashed lines present the 1:1 correspondence and the errors added in quadrature of the X-ray luminosity calculations from X-CIGALEand those quoted in the X-ray catalogue (Menzel et al. 2016), respectively. The grey dashed line shows the χ2 fit of the calculations. Symbols arecolour-coded based on their NH values. Left panel: results with the X-ray flux included in the SED. X-CIGALE calculations are consistent withthose from the input catalogue, at all luminosities spanned by our sample. There are 70 sources for which the LX calculation differs by more than0.5 dex from their input values (shown with error bars, see text for more details). Right panel: X-ray flux is not included in the SED. In this case,the scatter is significantly larger, while the error of the LX calculations from X-CIGALE, increases by ≈4×.

3.2.3. SED fitting and parameter estimation

The spectral models described above are normalised to the cre-ation of 1 M�. The first step of the fitting process was to scaleeach model to the observations by minimising its χ2 (Noll et al.2009; Boquien et al. 2019). After this scaling operation, exten-sive quantities such as luminosities were defined, including theintrinsic L2500 Å, which is an output of the SKIRTOR module:models that do not satisfy the αox−L2500 Å relation and its maxi-mum dispersion were discarded. The likelihoods of the remain-ing models were computed and parameter values were estimatedfrom their marginalised probability distribution function (like-lihood weighted mean and standard deviation). The best modelcorresponding to each observed SED is also an output of thecode.

3.2.4. Mock catalogues

The validity of a parameter estimation can be assessed throughthe analysis of a mock catalogue. When this option is chosen, thecode considers the best fit of each object and a mock catalogueis built. Each best flux is modified by injecting noise taken froma Gaussian distribution with the same standard deviation as theobserved flux. The mock data are then analysed in the same wayas the observed data, and the accuracy of the parameter estima-tion can be tested by comparing input (ground truth) and output(estimated) values. Our tests using the results from the mock cat-alogues are presented in the appendix.

3.3. Final sample

In our analysis, we compared the estimations of the fracAGN,namely the value of the best model and then mean of the

probability density function (PDF) distribution, to select theX-ray AGNs with the most reliable SED measurements. For thatpurpose, we applied the following criteria: we excluded sourcesfor which the best AGN fraction values are zero, while theirBayesian AGN fraction is greater than 0.4. Such a large dif-ference between the best and the Bayesian values, is a strongindicator that the code failed to accurately fit the SED since thePDF distribution is not consistent with the best model. We alsoexcluded AGNs with reduced χ2, χ2

red > 10 from the SED fit-ting process. These two criteria exclude ∼4−7% of our sources.Finally, we removed sources with LX < 1042 erg s−1 from ourfinal sample to minimise contamination from inactive galaxies.Table 4 presents the number of sources that satisfy our selectioncriteria for each configuration of the SED fitting process used inour analysis.

4. X-CIGALE performance

4.1. X-ray luminosity

First, we compared the intrinsic X-ray luminosities of the AGNsestimated by X-CIGALE with those from the input catalogue.The results are presented in Fig. 3. X-CIGALE estimates areconsistent with those from the input catalogue at all luminosi-ties spanned by our sample. A χ2 fit (grey dashed line) giveslog LX,X-CIGALE = (0.962 ± 0.023) log LX,data + 1.694 ± 0.085.Green dashed lines present the errors added in quadrature ofthe X-ray luminosity calculations from X-CIGALE and thosequoted in the X-ray catalogue (Menzel et al. 2016). There are70 outliers, that is, sources for which LX calculations dif-fer by more than 0.5 dex from their input values (shown witherror bars). The vast majority of sources for which X-CIGALE

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25 26 27 28 29 30 31 32

log L2500ºA [ergs¡1Hz¡1]

22

23

24

25

26

27

28

logL

2ke

V[e

rgs¡

1H

z¡1]

This sample

Just et al: 2007

Lusso et al: 2016

Fig. 4. L2 keV vs. L2500 Å relation. Blue line presents the Just et al.(2007) relation that X-CIGALE uses to connect the X-ray flux with theUV (2500 Å) luminosity. Green line shows the L2 keV vs. L2500 Å fromLusso & Risaliti (2016). For comparison, we also plot the fit from ourcalculations (grey line).

underestimates the LX value by more than 0.5 dex are AGNs withhigh X-ray absorption (NH > 1022.5−23.0 cm−2). 82% of thesesources also lack far-IR photometry, and 57% do not have IRcoverage above 8 µm. Thus, a possible explanation for the dis-crepant LX values could be that our NH estimations are not accu-rate and/or the lack of available IR photometry does not allowX-CIGALE to properly fit these SEDs. Lowering the LX dif-ference threshold to 0.3 dex to characterise a source as an out-lier leads to the same conclusions. On the other hand, thosesources for which X-CIGALE overestimates their value by morethan 0.5 dex (<0.4% of the total sample) do not present anysigns of X-ray absorption (NH < 1021.5 cm−2). The quality ofthe SED fitting of these systems is good, based on the χ2

red val-ues (χ2

red / 2). SED analysis reveals that the AGN emission isobscured in the optical wavelengths. The optical/mid-IR criteriaof Yan et al. (2013) do not classify these AGNs as optically redsources. Furthermore, their optical spectra present broad lines.Thus, there are no indications to corroborate with X-CIGALEthat these AGNs are absorbed. Therefore, we do not find a plau-sible explanation for these outliers. However, they only represent<0.4% of our sample (∼1% if we lower the threshold of the LXdifference to 0.3 dex).

In the right panel of Fig. 3, we plot the X-ray luminosity cal-culations of X-CIGALE when the X-ray flux is not included in theSED vs. the LX from the input catalogue. In this case, the scatteris significantly larger compared to the left panel, while the aver-age error of the LX estimates from X-CIGALE, increases by ≈4×(green lines). When there is no fX in the SED, the LX estimatesshould not be taken at face value. X-CIGALE uses the AGN mod-ule SKIRTOR to output plausible L2500 Å values. Since there isno X-ray information to further constrain the L2500 Å parameterby connecting it to the observed X-ray flux, the algorithm pro-vides LX estimations, using the full range of αox (allowed by theJust et al. 2007 relation and the αox dispersion) and weighs overthese possible values. Thus, although the code provides LX esti-mates, these values should be taken with caution.

4.2. The efficiency of X-CIGALE to connect the X-ray-UVluminosity

As mentioned, αox, L2500 Å and L2 keV are the three parametersthat are important for X-CIGALE to connect the X-rays with the

UV, and thus the other wavelengths during the SED fitting pro-cess. In Appendix A.1, we assess the efficiency of X-CIGALEto constrain these three parameters using the mock analysis (seeSect. 3.2.4). In this section, we compare the algorithm’s calcula-tions of L2 keV and L2500 Å.

Figure 4 compares the L2 keV and L2500 Å luminosities withthe observed relations of Just et al. (2007) (blue line), used by thecode as input (see Sect. 3.2) and Lusso & Risaliti (2016) (greenline). The grey line presents the fit on our measurements. Thereis an overdensity of sources above the Just et al. (2007) relation(at high luminosities) that is due to selection bias. XMM-XXLhas a low exposure time and therefore our X-ray sample is biasedtowards high-luminosity sources.

4.3. The effect of the extinction law and temperature of polardust

In Sect. 3.2.2, we mentioned that for the polar dust estimation anSMC extinction curve is adopted and the grey-body dust temper-ature is set to 100 K. The effect of the addition of polar dust in theSED fitting is discussed in Sect. 5.4. In this section, we examinewhether the adoption of different extinction curves and dust tem-peratures affect the polar dust contribution (through EB−V) andthe AGN fraction calculations.

Apart from the SMC extinction curve, X-CIGALE includesthe choice of the empirical extinction curves of Calzetti et al.(2000) and Gaskell et al. (2004). Figures 5a and b present thedifference of the polar dust estimations between the SMC andthe Calzetti et al. (2000), and the SMC and Gaskell et al. (2004)curves, respectively. Both distributions are highly peaked, andtherefore the choice of the extinction curve does not affect thepolar dust measurements. In Figs. 6a and b, we repeat the sametest for the AGN fraction values. Again, the adoption of differentextinction curves does not affect the fracAGN.

In Figs. 5c, d, and 6c, d, we test whether different tempera-tures for the grey-body dust re-emission affect the polar dust andAGN fraction calculations, respectively. Specifically, we plotthe difference of the aforementioned parameters using values of100 K (the value used throughout our analysis) as well as 75 Kand 50 K. All distributions are highly peaked at zero. We obtainsimilar results when we increase the temperature to 200 K. Weconclude that the choice of the parameters defining the grey-body re-emission does not affect our results.

4.4. The effect of Herschel photometry

Far-IR data combined with mid-IR photometric bands are knownto improve the SFR estimations of galaxies hosting an AGN(Hatziminaoglou et al. 2009; Stanley et al. 2018; Masoura et al.2018) since they constrain the AGN contribution to the IR lumi-nosity of the host galaxy more efficiently. Masoura et al. (2018)used 608 X-ray AGNs in XXL with Herschel detection andfound that SFRs estimated without far-IR photometry are sys-tematically underestimated compared to Herschel SFRs (seetheir Fig. 4). In this section, we examine the effect of Herschelphotometry on our estimations. Specifically, we wish to testwhether the absence of Herschel photometry affects the AGNfraction and polar dust estimates of X-CIGALE.

For this part of our analysis, we restricted our sample to onlythose AGNs with reliable (S/N > 3) SPIRE photometry (in addi-tion to the criteria mentioned in Sect. 3.3). This maximises anylikely effect on the SED fits. This reduces our X-ray dataset to328 AGN. We ran X-CIGALE twice: one time using Herschelphotometry and a second time without (both runs use the X-rayflux of the sources in the fitting process).

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Fig. 5. Distribution of polar dust measurement differences for differ-ent extinction laws (panels a and b) and grey-body dust temperatures(panels c and d). All distributions are highly peaked at zero, which indi-cates that polar dust calculations are not sensitive to the choice of theseparameters.

Figures 7a and b present the difference of the polar dust andAGN fraction distributions, from the two runs, respectively. Wenotice that the addition of Herschel photometry slightly reducesthe AGN fraction measurements. Although, the distribution ofthe difference of the AGN fraction estimations peaks at zero,there is a tail at negative values. Specifically, there are 73 sources(≈22% of this sample) for which the addition of Herschel pho-tometry reduces their AGN fraction value by more than 0.15(which is equivalent to the error of the two fracAGN estimationsadded in quadrature, see next sentence). This is also quantified

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Fig. 6. Same as in the previous plot, but for the AGN fraction. Differ-ent extinction laws (panels a and b) and grey-body dust temperatures(panels c and d) do not affect the fracAGN measurements.

by the mean fracAGN values, fracAGN,Herschel = 0.36 ± 0.08 andfracAGN,no Herschel = 0.43 ± 0.12. We also notice that includingfar-IR photometry increases the statistical significance of theestimations from 3.6σ to 4.5σ (the significance is defined asthe Bayesian value over the error). The addition of Herschelphotometry does not affect polar dust measurements. We alsoexamined whether the addition of Herschel photometry affectsother parameters estimated by the SED fitting and specificallythe X-ray luminosity and the L2500 Å. The distribution of the dif-ference between the LX and L2500 Å parameters with and withoutfar-IR photometry is presented in Figs. 7c and d. Both distri-butions are highly peaked at zero with small tails at both sides.

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Fig. 7. Distribution of the difference of polar dust, AGN fraction andX-ray and UV luminosity measurements, with and without Herschelphotometry. Polar dust and luminosity calculations are not affected bythe existence of far-IR photometry. However, addition of far-IR pho-tometry reduces the fracAGN. This is quantified by the mean values,fracAGN,Herschel = 0.36 ± 0.08 and fracAGN,no Herschel = 0.43 ± 0.12.

Thus, the inclusion of Herschel photometry does not seem toaffect the estimations of these two parameters.

5. Advantages of X-ray flux and polar dustcomponents

In this section, we examine the advantages that the introductionof the two new features of X-CIGALE (i.e. the X-ray flux andpolar dust) bring to the SED fitting process.

5.1. The effect of the X-ray flux on the AGN fractionmeasurements

One of the strengths of SED decomposition is that it allows dis-entangling AGN emission from that of the host galaxy emission.This is a crucial issue since the reliability of any further analy-sis depends on how reliably the SED fitting code can performthis task. The goal of this part of our analysis is to examineif the introduction of the X-ray information in the SED allowsX-CIGALE to improve its efficiency in estimating robust AGNfractions.

In Appendix A.2, we assess the accuracy of X-CIGALE inthe estimation of the AGN fraction. Here, we study how the addi-tion of the X-ray flux in the fitting process affects the AGN frac-tion measurements. In Fig. 8 (left panel), we plot the distributionof the AGN fraction with (black shaded area) and without (bluehistogram) the X-ray flux (polar dust is included in both runs).The mean fracAGN value increases from 0.46±0.16 to 0.49±0.14with the addition of fX. To further examine the effect of the X-ray flux in the AGN fraction estimates, in the right panel we plotthe difference of the AGN fraction with and without X-ray fluxfor different luminosity bins. The distributions peak at zero in allcases. However, at high X-ray luminosities (LX > 1043 erg s−1)a tail starts to appear at positive values, that is, the AGN frac-tion tends to be higher when the X-ray flux is included in thefitting process. This tail becomes more prominent at the highestluminosity bin. Specifically, for AGN with LX > 1045 erg s−1 themean AGN fraction increases from 0.58 ± 0.16 without X-rayflux to 0.65± 0.13 with X-ray flux. The opposite trend is presentfor low luminosity AGNs (1042 < LX < 1043 erg s−1). In thiscase, the mean fracAGN reduces from 0.28 ± 0.13 to 0.23 ± 0.09with the X-ray flux.

The statistical significance of the AGN fraction measure-ments improves with the inclusion of the X-ray flux, as canbe seen by the numbers presented above. This is also illus-trated in Fig. 9, where we plot the difference of the signifi-cance, ∆σ, of the AGN fraction estimations with and withoutX-ray information, for sources with LX > 1045 erg s−1 and dif-ference in fracAGN > 0.2 (shaded histogram) and sources withLX < 1043 erg s−1 and difference in fracAGN < −0.2. Therefore,we conclude that the addition of the X-ray flux in the SED fit-ting results in more robust AGN fraction measurements, whileits effect on fracAGN is small and depends on the AGN power.

5.2. The effect of X-ray flux on the estimation of the L2500 Åand AGN type

Another important parameter of the SED fitting is the intrin-sic L2500 Å luminosity. In addition to the reasons mentioned inSect. 3.2, L2500 Å is also a proxy of the AGN power. Here, westudy the effect of the X-ray flux on the estimation of L2500 Å.Figure 10 (left panel) compares the L2500 Å estimations with andwithout X-ray flux in the SED for type 1 and 2 sources. Theclassification is based on their inclination angle values, as calcu-lated by X-CIGALE. 2264 sources satisfy our selection criteria,described in Sect. 3.3, in both runs (with and without X-ray flux).In Fig. 10, we only consider the 1941 sources (86%) for whichthe inclination angle from the best fit has the same value in thetwo runs. For both types, we observe a scatter, but no systematiceffect between the two estimations.

Of 2264 AGNs, 1810 are classified as type 1 with the X-rayflux, but 47 (∼2.5%) become type 2 without the X-ray flux.On the other hand, from the 454 sources classified as type 2with fX, 276 (∼60%) change to type 1 without X-ray flux.

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Fig. 8. Left: distribution of the AGN fraction, with X-ray flux (black shaded area) and without X-ray flux (blue histogram). Right: distributionof the difference of the AGN fraction, with and without X-ray flux, for different luminosity bins. All distributions are normalised to unity. A tailappears at positive values, i.e., the AGN fractions are higher when X-ray flux is included in the SED fitting, for AGNs with LX < 1043 erg s−1 thatbecomes more prominent for the most luminous sources (LX < 1045 erg s−1). On the other hand, there is a tail at negative values, i.e. the AGNfractions are lower with X-ray flux, for the fainter AGNs (LX < 1043 erg s−1, blue line).

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Fig. 9. Difference of the significance, ∆σ, of the AGN fraction esti-mations with and without X-ray information, for sources with LX >1045 erg s−1 and difference in fracAGN > 0.2 (shaded histogram) andsources with LX < 1043 erg s−1 and difference in fracAGN < −0.2. Thestatistical significance of the AGN fraction measurements improves sig-nificantly with the inclusion of the X-ray flux, especially for the mostluminous AGNs.

The 323 (47+276) sources with different classifications in thetwo runs are shown in the right panel of Fig. 10. X-CIGALEincreases L2500 Å when the type changes from type 1 to type 2with the inclusion of the X-ray flux and lowers L2500 Å whenthe type changes from type 2 to type 1 with fX. This result isexpected, considering the way that X-CIGALE works. (mid-) IR emission is considered anisotropic, that is, it dependson the viewing angle. The same source will have lower IRflux when viewed edge on (type 2) compared to face on (seeFig. 4 in Stalevski et al. 2012). On the other hand, X-ray fluxis considered isotropic. Therefore, a type 2 source will have ahigher LX,intrinsic

LIR(and

L2500 Å,intrinsic

LIR) than a type 1 source. For a given

observed IR emission, X-CIGALE will decrease/increase the

intrinsic accretion power (and thus the intrinsic L2500 Å) depend-ing on whether the source is type 1/2.

Our analysis revealed that the inclusion of the X-ray infor-mation does not significantly affect the classification of sourcesthat are type 1 based on the run with fX, but it does change thecharacterisation of the majority of the AGNs classified as type 2with X-ray flux. To investigate further, the different classificationfor the 276 AGNs classified as type 2 when the X-ray informa-tion is included in the SED, but classified as type 1 without fX,we compare their X-CIGALE classification with that from opti-cal (SDSS) spectra (Menzel et al. 2016). Among those sourcesthat lie at z < 1, ∼85% are classified as narrow-line AGNs(NLAGN2; see Sect. 3.3.2 in Menzel et al. 2016), meaning theoptical spectrum agrees with the classification of X-CIGALEwhen using the X-ray flux. On the other hand, at z > 1.5,∼85% of the AGNs present broad lines in their optical spectra(BLAGN1), meaning their classification does not agree with thatof X-CIGALE when the X-ray flux is included. We concludethat, at z < 1, the addition of the X-ray information increases thereliability of the SED fitting regarding the classification type ofa source. At high redshifts, however, X-CIGALE seems to mis-classify some sources: among the 703 AGNs at z > 1.5 in ourtotal sample, 103 (≈15%) are classified as type 2 with X-ray fluxand as type 1 without.

We further investigated plausible reasons for the misclassi-fication of sources at high redshift when fX is added to the fit-ting process. The vast majority of these sources lack photometryabove 5 microns. In the absence of IR photometry and withoutconsidering the X-ray flux, the introduction of a type 1 compo-nent gives a flexibility to fit the UV/optical data, while a type 2component does not contribute to this emission. Thus, withoutfX, X-CIGALE is more likely to classify high redshift sourcesas type 1, with the risk of over-fitting the UV/optical data. Theinclusion of X-ray flux provides an additional constraint for atype 1 template (via the Just et al. 2007 relation), whereas thecode can freely scale a type 2 template to match any given X-rayflux. Thus, with fX, the code will preferentially classify sourcesas type 2. In this configuration (SED with only UV/optical data),

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Fig. 10. Comparison of L2500 Å values with and without X-ray flux in the fitting process. Left panel: comparison of sources that are classified as thesame type, with and without X-ray flux in the SED fitting process. Blue symbols present the results for type 1 sources (i = 30◦) and red symbolsfor type 2 sources (i = 70◦). Right panel: comparison of L2500 Å values for sources with different types from the two runs (with and without X-rayflux). X-CIGALE decreases/increases the intrinsic accretion power (and thus the intrinsic L2500 Å) depending on whether the source is type 1/2.

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Fig. 11. X-ray AGN fraction vs. X-ray luminosity estimated by X-CIGALE. The X-ray AGN fraction is defined as the ratio of theX-ray AGN emission to the total X-ray emission of the galaxy(AGN + binaries + hot gas). As expected, for the vast majority ofsources the AGN X-ray emission contributes more than 90% of the totalX-ray emission of the galaxy.

the difference between a type 1 and a type 2 component is notmeaningful.

We conclude that the misclassification of AGNs at high red-shifts is mostly due to lack of available IR photometry, whichmakes the type assignation almost unconstrained. Future surveys(JWST; Gardner et al. 2006) could provide mid-IR photometryfor these distant AGNs.

5.3. Ability to quantify the AGN contribution to the totalgalaxy X-ray emission

Star-forming galaxies may emit X-rays that originate from X-raybinaries, supernovae remnants, and hot gas (e.g., Fabbiano 1989).

Previous studies have shown that X-ray luminosity can be used asa proxy of SFR (e.g., Ranalli et al. 2003; Laird et al. 2005). How-ever, in X-ray-luminous AGNs, like those used in this study, theX-ray emission predominately originates from the supermassiveblack hole, and therefore these systems are usually identified andexcluded by studies that use the X-ray emission to trace star for-mation (e.g., Persic & Rephaeli 2002; Laird et al. 2005).

As already mentioned, X-CIGALE can also model the X-rayemission that originates from low- and high-mass X-ray bina-ries and hot gas. Thus, in a similar manner to the fracAGN thatquantifies the contribution of the AGN IR emission to the totalgalaxy IR emission, we define the X-ray AGN fraction as theratio of the X-ray AGN emission to the total X-ray emissionof the galaxy (AGN + binaries + hot gas). In Fig. 11, we plotthe X-ray AGN fraction as a function of the X-ray luminosity,estimated by X-CIGALE. As expected, for the vast majority ofsources the AGN X-ray emission contributes more than 90% ofthe total X-ray emission of the galaxy. Therefore, X-CIGALEmodels the X-ray emission and its various components to offerthe capability of estimating the SFR of galaxies not only fromIR indicators but also from X-rays, in those systems that are notAGN dominated.

5.4. Polar dust and AGN fraction

Another important feature of X-CIGALE is polar dust, quanti-fied with EB−V, as a free parameter to account for dust extinctionin all AGNs, regardless of their classification into type 1 and 2(see Sect. 3.2.2). In Appendix A.3, we test whether X-CIGALEcan successfully constrain EB−V. Here, we study the effect ofadding polar dust as a free parameter to the SED fitting resultson the AGN fraction measurements.

Figure 12 presents the distribution of the AGN fraction with(black shaded area) and without (blue histogram) polar dust. Inboth runs, X-ray flux is included. The addition of polar dust tothe fitting process increases the AGN fraction. The mean fracAGNis 0.37±0.12 without polar dust and 0.49±0.14 with the additionof polar dust. A similar increase is observed when the two runsdo not include fX. This trend is also similar regardless of theAGN type, classified based on the inclination angle. The increase

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Fig. 12. Distribution of AGN fraction, with (black shaded area) andwithout (blue histogram) polar dust. The addition of polar dust to thefitting process increases the AGN fraction.

of the AGN fraction is expected since the introduction of polardust allows X-CIGALE to account for obscuring material in thepoles of AGNs.

5.5. Polar dust and AGN type

Repeating the same exercise as for the inclusion of the X-rayflux, we examine whether there are sources for which the classi-fication, based on the estimated inclination angle, changes withthe introduction of polar dust ( fX is not included in these runs).Our analysis shows that only 0.01% of the AGNs classified astype 2 with polar dust, change classification without it. How-ever, 14% of the sources characterised as type 1 when polar dustis included change to type 2 without it. The addition of attenu-ation by polar dust provides X-CIGALE with the flexibility toattribute part of the absorption to polar dust.

We compare the sources that change from type 1 to type 2when we ignore polar dust with the classification from opticalSDSS spectra. ∼70% of the AGNs present broad lines in theoptical continuum. We conclude that the introduction of polardust as a free parameter in the SED fitting process improves theaccuracy of X-CIGALE in the source type classification.

5.6. Polar dust and AGN detection efficiency

X-ray detection provides the most reliable method to iden-tify AGNs. Although X-rays have proven efficient in penetrat-ing the intervening absorbing material (e.g., Luo et al. 2008),even hard X-rays are absorbed by huge amounts of gas anddust. Thus, X-ray-selected AGNs are biased against the mostheavily absorbed sources. On the other hand, mid-IR surveysare less affected by extinction. The material that absorbs AGNradiation even at hard X-ray energies, is heated by the AGNand re-emits at infrared wavelengths. Therefore, mid-IR-selectedAGNs can detect AGNs that are missed by X-ray surveys (e.g.,Georgantopoulos et al. 2008; Fiore et al. 2009). Spitzer was thefirst infrared survey used to select AGNs via colour selec-tion criteria (e.g., Stern et al. 2005; Donley et al. 2012). Later,these techniques were adapted and used for WISE. For instance,Mateos et al. (2012) used an AGN selection method based onthree WISE colours. Assef et al. (2018) used the W1 and W2

criteria of Stern et al. (2012) and extended them to fainter mag-nitudes. However, IR selection techniques are biased againstlow-luminosity AGNs. In a recent study, Pouliasis et al. (2020)showed that SED decomposition can provide a complementarytool to the X-ray and IR selection techniques, since it makesuse of large wavelength ranges to efficiently disentangle accre-tion from star formation (Ciesla et al. 2015; Dietrich et al. 2018;Małek et al. 2018).

Our AGN sample consists of X-ray selected sources withhigh X-ray luminosity (LX > 1042 erg s−1), therefore the con-tamination from non-AGN systems should be minimal. Thus, wewish to examine whether the addition of polar dust in the fittingprocess affects the efficiency of the SED decomposition to find astrong AGN component (fracAGN) and compare it with the effi-ciency of IR colour-selection criteria to detect AGN candidatesamong X-ray sources.

In Fig. 13, we plot the W1−W2 vs. W2 for the X-ray AGNsin our sample. With magenta and grey, we mark the sources thatare selected as IR AGN candidates with a confidence level of75% and 90%, respectively, using the Assef et al. (2018) crite-ria. Circles present all the X-ray AGNs in our dataset, whichwere detected by WISE and colour-coded based on the AGNfraction measurements of X-CIGALE. To estimate them, weincluded the polar dust as a free parameter, but their X-ray fluxwas ignored. There are 1956 sources with WISE counterpartsthat also fulfill our selection criteria (Sect. 3.3). Of 1956, 689(∼35%) were detected by Assef et al. (2018) with 90% confi-dence (889/1956 ≈ 45%, with 75% confidence). 671 out ofthe 689 sources (∼97%) have fracAGN > 0.2 (631/689 ≈ 92%with fracAGN > 0.3), meaning the SED decomposition revealsstrong AGN contribution in the IR emission of the system. InAppendix A.2, we examine how many of the sources with mea-sured fracAGN > 0.2 (and fracAGN > 0.3) have true fracAGN <0.2, that is, the AGN fraction has been miscalculated and theAGN contribution to the IR emission of the galaxy is weakerthan assumed. Our analysis shows <10% contamination fromsuch systems (Fig. A.3). Among IR-selected AGNs with 75%confidence, the numbers are 851/889 ≈ 96% with fracAGN > 0.2(795/889 ≈ 90% with fracAGN > 0.3). Therefore, X-CIGALEfinds a strong AGN component in more than 90% of the IR-selected AGNs. However, as we notice in Fig. 13, there is alarge number of fainter AGNs that are missed by IR colour-selection criteria, but the SED fitting finds strong AGN IR emis-sion. Specifically, 85% of the sources have fracAGN > 0.2 and74% have fracAGN > 0.3. Thus, even considering the more strictcriterion of fracAGN > 0.3, SED decomposition manages todetect a larger fraction of X-ray selected AGNs than IR colourcriteria (74% vs. 45%), while at the same time it also uncov-ers >90% of the IR colour-selected AGNs. Repeating the sameexercise without the inclusion of polar dust in the SED fittingprocess yields 71% sources with fracAGN > 0.2 and 55% withfracAGN > 0.3 among the non-IR detected AGNs. Thus, the addi-tion of polar dust allows SED decomposition to uncover a largerfraction of X-ray AGNs. The results are summarised in Table 5.On the other hand, the addition of the X-ray flux only marginallyimproves the AGN detection efficiency. Specifically, among thenon-IR-selected AGN, modelling the SEDs with X-ray flux andpolar dust yields 87% of the sources with fracAGN > 0.2 and75% with fracAGN > 0.3, compared to 85% and 74%, respec-tively, using only the polar dust in the fitting process.

In Fig. 14, we show a repetition of the same exercise usingthe Spitzer colour-selection criteria of Donley et al. (2012). 968AGNs in our sample were detected by Spitzer and follow ourselection criteria (Sect. 3.3). 409/968 (42%) are IR-selected

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Fig. 13. W1−W2 vs. W2 for X-ray AGNs in our sample (circles). Sources selected as IR AGNs with 75% and 90% confidence, using the criteriaof Assef et al. (2018), are in magenta and grey, respectively. The remaining sources of our X-ray AGN sample are presented with circles, colourcoded based on the AGN fraction measurements of X-CIGALE. ∼97% of IR selected AGN have fracAGN > 0.2 (∼92% with fracAGN > 0.3). Thispercentage drops to 71% (55% with fracAGN > 0.3) without polar dust.

Table 5. Polar dust and AGN detection efficiency of the SED decomposition.

SED configuration X-ray AGNs IR-detected AGNs Non-IR-detected AGNs75% Assef 90% Assef

1956 889/1956 (45%) 689/1956 (35%) 1067/1956 (55%)

Polar dust (fracAGN > 0.2) 851/889 (96%) 671/689 (97%) 906/1067 (85%)Polar dust (fracAGN > 0.3) 795/889 (90%) 631/689 (92%) 789/1067 (74%)

W/out polar dust (fracAGN > 0.2) 740/889 (83%) 591/689 (86%) 757/1067 (71%)W/out polar dust (fracAGN > 0.3) 603/889 (68%) 484/689 (70%) 586/1067 (55%)

Notes. The second column presents the number of X-ray AGNs detected by WISE. The third and fourth columns show the number of X-raydetected AGNs that are IR selected, based on the Assef et al. (2018) criteria, with confidence level 75% and 90%, respectively. The last columnpresents the number of X-ray AGNs that are not IR selected, but for which X-CIGALE finds a strong AGN component, i.e., high AGN fraction.

AGNs, based on the criteria presented by Donley et al. (2012).98% of these IR-selected AGNs have fracAGN > 0.2 (92% withfracAGN > 0.3). Among the 968 sources with Spitzer counter-parts, ∼80% have fracAGN > 0.2 and ∼67% have fracAGN > 0.3.These numbers drop to ∼65% and ∼49%, when polar dust is notincluded in the SED fitting process.

From this part of the analysis, we conclude that the additionof polar dust improves the ability of the algorithm to disentan-gle the AGN and host galaxy IR emission, and thus increasesthe efficiency of the SED decomposition to detect a strong AGNcomponent.

6. Summary

In this work, we used the SED fitting code X-CIGALE tomodel the SEDs of ∼2500 X-ray AGNs in the XMM-XXL

field. X-CIGALE has some important features to model theAGN emission. It accounts for polar-dust extinction that is com-monly found, especially in the case of X-ray-selected AGNs andincludes X-ray data in the SED fitting process.

Our analysis shows that the estimated L2500 Å and L2 keV fol-low the adopted Just et al. (2007) relation, as well as other sim-ilar relations in the literature (Lusso & Risaliti 2016). The SEDfitting results are not sensitive to the choice of the extinctionlaw used and the temperature of polar dust. This result holdsfor our X-ray dataset and the available photometry. The additionof far-IR (Herschel) photometry does not statistically affect thepolar dust and AGN fraction measurements; however, it slightlyincreases the statistical significance of the latter (from 3.6σ to4.5σ).

About half of the sources identified as type 2 with the addi-tion of X-ray flux are found to be type 1 in the absence of

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G. Mountrichas et al.: Application of X-CIGALE in the XMM-XXL field

¡0:5 ¡0:4 ¡0:3 ¡0:2 ¡0:1 0 0:1 0:2 0:3 0:4 0:5 0:6

log10 (IRAC3=IRAC1)

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¡0:5

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AC2)

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IR selectedAGN(Donley et 2012)0:05

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GN

Fig. 14. Colour-colour distribution of X-ray AGNs in our sample (circles) using Spitzer photometry. Circles are colour-coded based on the AGNfraction estimations of X-CIGALE. Sources selected as IR AGN using the colour criteria of Donley et al. (2012), are marked with purple squares.

X-ray information. Visual inspection of randomly selected opti-cal (SDSS) spectra, revealed that for ∼85% of the AGNs that lieat z < 1, the optical spectrum agrees with X-CIGALE’s classi-fication when using fX. However, at higher redshifts (z > 1.5)lack of available IR photometry does not allow the algorithm toclassify sources in a robust manner. Inclusion of polar dust alsoimproves the agreement of the AGN type classification betweenX-CIGALE and optical spectra. We conclude that the additionof the X-ray flux and polar dust in the fitting process improvesthe accuracy of the classification of AGNs as type 1 or 2.

Compared to IR selection techniques (Donley et al. 2012;Assef et al. 2018), X-CIGALE recovered >90% of the IR-selected AGNs. Among the X-ray-detected AGNs that are notIR selected, SED decomposition attributes a large AGN com-ponent to the IR emission of the system (fracAGN > 0.2) for∼85% of them. This number drops to ∼70% when polar dust isnot included in the SED modelling. Thus, the addition of polardust improves the efficiency of the SED decomposition to detectAGN.

One of the most important strengths of SED fitting is thatit allows the disentangling of the IR emission of AGNs fromthat of the host galaxy, quantified by fracAGN in X-CIGALE.The new features of X-CIGALE improve the AGN fraction mea-surements. Specifically, the addition of the X-ray flux improvesthe statistical significance of the AGN fraction measurements,in particular for luminous (LX > 1045 erg s−1) sources (Fig. 9).The addition of polar dust increases the fracAGN estimations,since the AGN contributes more to the IR emission of thesystem.

The conclusions of our analysis hold under the condition that(mid-) IR photometry is available in the SED fitting process. Alack of IR photometry may result in unreliable X-ray luminositycalculations, lower and less robust AGN fraction estimates, andAGN type misclassifications.

Acknowledgements. The authors thank the anonymous referee for their com-ments. The authors thank Raphael Shirley and Yannick Roehlly for their helpto retrieve IR fluxes on the XMM-LSS field. GM acknowledges support by theAgencia Estatal de Investigación, Unidad de Excelencia María de Maeztu, ref.MDM-2017-0765. MB acknowledges FONDECYT regular grant 1170618 XXLis an international project based around an XMM Very Large Programme sur-veying two 25 deg2 extragalactic fields at a depth of ∼6× 10−15 erg cm −2 s−1

in the [0.5−2] keV band for point-like sources. The XXL website is http://irfu.cea.fr/xxl/. Multi-band information and spectroscopic follow-up ofthe X-ray sources are obtained through a number of survey programmes, sum-marised at http://xxlmultiwave.pbworks.com/. This research has madeuse of data obtained from the 3XMM XMM-Newton serendipitous source cat-alogue compiled by the 10 institutes of the XMM-Newton Survey Science Centreselected by ESA. This work is based on observations made with XMM-Newton,an ESA science mission with instruments and contributions directly fundedby ESA Member States and NASA. Funding for the Sloan Digital Sky Sur-vey IV has been provided by the Alfred P. Sloan Foundation, the U.S. Depart-ment of Energy Office of Science, and the Participating Institutions. SDSS-IVacknowledges support and resources from the Center for High-PerformanceComputing at the University of Utah. The SDSS website is www.sdss.org.SDSS-IV is managed by the Astrophysical Research Consortium for theParticipating Institutions of the SDSS Collaboration including the BrazilianParticipation Group, the Carnegie Institution for Science, Carnegie MellonUniversity, the Chilean Participation Group, the French Participation Group,Harvard-Smithsonian Center for Astrophysics, Instituto de Astrofísica deCanarias, The Johns Hopkins University, Kavli Institute for the Physics andMathematics of the Universe (IPMU)/University of Tokyo, Lawrence BerkeleyNational Laboratory, Leibniz Institut für Astrophysik Potsdam (AIP), Max-Planck-Institut für Astronomie (MPIA Heidelberg), Max-Planck-Institut fürAstrophysik (MPA Garching), Max-Planck-Institut für Extraterrestrische Physik(MPE), National Astronomical Observatories of China, New Mexico StateUniversity, New York University, University of Notre Dame, ObservatórioNacional/MCTI, The Ohio State University, Pennsylvania State University,Shanghai Astronomical Observatory, United Kingdom Participation Group, Uni-versidad Nacional Autónoma de México, University of Arizona, University ofColorado Boulder, University of Oxford, University of Portsmouth, University ofUtah, University of Virginia, University of Washington, University of Wisconsin,Vanderbilt University, and Yale University. This publication makes use of dataproducts from the Wide-field Infrared Survey Explorer, which is a joint projectof the University of California, Los Angeles, and the Jet Propulsion Labora-tory/California Institute of Technology, funded by the National Aeronautics andSpace Administration. The VISTA Data Flow System pipeline processing and

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science archive are described in Irwin et al. (2004), Hambly et al. (2008) andCross et al. (2012). Based on observations obtained as part of the VISTA Hemi-sphere Survey, ESO Program, 179.A-2010 (PI: McMahon). We have used datafrom the 3rd data release. This work is based [in part] on observations made withthe Spitzer Space Telescope, which was operated by the Jet Propulsion Labora-tory, California Institute of Technology under a contract with NASA The projecthas received funding from Excellence Initiative of Aix-Marseille University –AMIDEX, a French ‘Investissements d’Avenir’ programme.

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Appendix A: Mock catalogue analysis

As mentioned in Sect. 3.2.4, X-CIGALE offers the option to cre-ate and analyse mock catalogues based on the best-fit modelof each source of the dataset. Here, we use this to assess theefficiency of X-CIGALE in estimating important parameters.Throughout this section, the Bayesian estimates of the mock val-ues are presented.

A.1. The efficiency of X-CIGALE in estimating αox and L2500 Å

In Fig. A.1, we present a test of X-CIGALE’s reliability in esti-mating the αox parameter using the results of the mock catalogue.Specifically, we compare the true values of the parameters fromthe best fit of the dataset to the Bayesian values of the parameterobtained from the fit of the mock catalogue. The distribution ofthe difference of the two values clearly peaks at zero and ∼95%of the sources are within ±0.1. This demonstrates that the algo-rithm can reliably recover this parameter.

In the same fashion, Fig. A.2 presents the efficiency ofX-CIGALE to constrain L2500 Å with (left panel) and without(right panel) the X-ray flux. When the X-ray flux is includedin the SED, L2500 Å is well constrained. When there is no X-rayinformation in the SED, the scatter is larger. This is expectedbased on how X-CIGALE calculates this parameter in theabsence of the X-ray flux (see Sect. 3.2.2).

A.2. The efficiency of X-CIGALE in estimating the AGNfraction

Here, we examine the accuracy of X-CIGALE in the estimationof the AGN fraction. In Fig. A.3, we plot the AGN fraction val-ues estimated by the fit of the mock catalogue vs. the true values(i.e. the values from the best fit of the data). Circles present themean measurements, and their standard deviation is also plotted.Median values are shown by triangles. There is a good agree-ment between the estimated and true values.

In Fig. A.4, we plot the difference of the AGN fraction valuesestimated by fitting the mock catalogue from the true values. Wepresent the results when the X-ray flux has been included in theSED (shaded histograms) and without including the X-ray flux(green lines). The results are split into X-ray luminosity bins.Based on our analysis, X-CIGALE can successfully recover theAGN fractions, regardless of whether the X-ray flux is includedin the SED fitting or not, at all X-ray luminosities.

In Sect. 5.6, we examine the fraction of X-ray AGNs forwhich a strong AGN component is measured (i.e. fracAGN >0.2). In Fig. A.5, we test the level of contamination in these

¡0:4 ¡0:3 ¡0:2 ¡0:1 0 0:1 0:2 0:3 0:4

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0

100

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15001600

Fig. A.1. Efficiency of X-CIGALE to constrain the αox parameter usingthe mock catalogue (see Sect. 3.2.4). The distribution peaks at zero,with 95% of the sources lying within ±0.1. This demonstrates that thealgorithm can successfully estimate αox.

calculations, that is, the percentage of sources for which the trueAGN fraction is <0.2, albeit the measured fracAGN > 0.2. Usinga threshold of measured (mock) fracAGN > 0.2, the contamina-tion is ≈10%, which drops to ∼6.5% if we use a higher thresholdof fracAGN > 0.3. For this calculation, we ran X-CIGALE withpolar dust and without X-ray flux, in accordance with the con-figuration used in Sect. 5.6.

A.3. The efficiency of X-CIGALE in the estimation of polardust contribution

To check whether X-CIGALE can successfully constrain theeffect of dust extinction by polar dust, in Fig. A.6 we plot theBayesian estimations of the EB−V parameter for the polar dustvs. its exact value. The scatter (1σ variations) is substantial. Fur-thermore, for EB−V < 0.5 the fit is not sensitive to an incrementalincrease in the reddening parameter. The sensitivity is better athigher values of EB−V, but the parameter is still not well deter-mined. The picture remains the same regardless of whether theX-ray flux is taken into account in the SED fitting. These resultssuggest that it is redundant to use in the SED configuration pro-cess, values of EB−V within small intervals, and that the esti-mated values of the parameter are not well constrained.

26 27 28 29 30 31 32 33

log L2500ºA;data [ergs¡1Hz¡1]

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33

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2500º A

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2500º A

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s¡1H

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Fig. A.2. Comparison of L2500 Å valuesestimated by X-CIGALE for the mocksources with the true values (data). Leftpanel: when X-ray flux is included in theSED, L2500 Å is well constained. Rightpanel: X-ray flux is not included inthe data. Although X-CIGALE recoversthe parameter successfully, the scatter islarger compared to the results with theX-ray flux.

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0 0:1 0:2 0:3 0:4 0:5 0:6 0:7 0:8 0:9 1:0fracAGN (data)

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Fig. A.3. AGN fraction values estimated by the fit of the mock catalogue vs. the true values (from the fit of the data). Circles present the meanmeasurements and their standard deviation is also plotted. Median values are shown by triangles.

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Fig. A.4. Difference of the AGN fraction values estimated by fitting the mock catalogue from the true values. We present the results when theX-ray flux has been included in the SED (shaded histograms) and without including the X-ray flux (green histograms). The results are split intoX-ray luminosity bins. X-CIGALE can successfully recover the AGN fractions, regardless of whether the X-ray flux is included in the SED fittingor not, at all X-ray luminosities.

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0 0:1 0:2 0:3 0:4 0:5 0:6 0:7 0:8 0:9 1:0fracAGN;data

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Fig. A.5. Binned measurements of true AGN fraction values (data) for fracAGN,mock > 0.2 (green histogram) and fracAGN,mock > 0.3 (blackhistogram) from the mock analysis. For sources with fracAGN,mock > 0.2, there is ≈10% contamination (i.e. sources for which the AGN componentis low: fracAGN,data < 0.2). For sources with fracAGN,mock > 0.3, the contamination is ∼6.5%.

0:01 0:02 0:05 0:1 0:2 0:5 1 2EB¡V (data)

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)

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¡V

(mock

)

without fX

Fig. A.6. Polar dust estimations from mock sources vs. input (data) values. Although the 1σ deviation is large, mock values increase with as thedata values do. However, the algorithm is not sensitive to incremental parameter increases. The trends are similar regardless of whether the X-rayflux is included (left panel) or not (right panel) in the fitting process.

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