J/ApJ/884/11ivo://CDS.VizieR/J/ApJ/884/11doi:10.26093/cds/vizier.18840011CDSGonzalez-Martin O.Masegosa J.Garcia-Bernete I.Ramos Almeida C.,Rodriguez-Espinosa J.M.Marquez I.Esparza-Arredondo D.,Osorio-Clavijo N.Martinez-Paredes M.Victoria-Ceballos C.Pasetto A.,Dultzin D.2021-07-05T09:08:49Z2021-04-13T10:14:24ZCDS support team

CDS, Observatoire de Strasbourg, 11 rue de l'Universite, F-67000 Strasbourg, France

cds-question@unistra.fractive-galactic-nucleiastronomical-modelsinfrared-astronomyspectroscopyx-ray-sourcesseyfert-galaxiesinterstellar-mediumextinctionThis is the second in a series of papers devoted to exploring a set of six dusty models of active galactic nuclei (AGN) with available spectral energy distributions. These models are the smooth torus by Fritz+ (2006MNRAS.366..767F), the clumpy torus by Nenkova+ (2008ApJ...685..147N and 2008ApJ...685..160N), the clumpy torus by Honig & Kishimoto (2010A&A...523A..27H), the two-phase torus by Siebenmorgen+ (2015A&A...583A.120S), the two-phase torus by Stalevski+ (2012MNRAS.420.2756S and 2016MNRAS.458.2288S), and the wind model by Honig & Kishimoto (2017ApJ...838L..20H). The first paper explores discrimination among models and the parameter restriction using synthetic spectra. Here we perform spectral fitting of a sample of 110 AGN drawn from the Swift/BAT survey with Spitzer/IRS spectroscopic data. The aim is to explore which is the model that describes better the data and the resulting parameters. The clumpy wind-disk model by Honig & Kishimoto provides good fits for ~50% of the sample, and the clumpy torus model by Nenkova+ is good at describing ~30% of the objects. The wind-disk model by Honig & Kishimoto is better for reproducing the mid-infrared spectra of type 1 Seyferts (with 60% of the type 1 Seyferts well reproduced by this model compared to the 10% well represented by the clumpy torus model by Nenkova+), while type 2 Seyferts are equally fitted by both models (roughly 40% of the type 2 Seyferts). Large residuals are found irrespective of the model used, indicating that the AGN dust continuum emission is more complex than predicted by the models or that the parameter space is not well sampled. We found that all the resulting parameters for our AGN sample are roughly constrained to 10%-20% of the parameter space. Contrary to what is generally assumed, the derived outer radius of the torus is smaller (reaching up to a factor of ~5 smaller for 10pc tori) for the smooth torus by Fritz+ and the two-phase torus by Stalevski+ than the one derived from the clumpy torus by Nenkova+ Covering factors and line-of-sight viewing angles strongly depend on the model used. The total dust mass is the most robust derived quantity, giving equivalent results for four of these models.2019ApJ...884...11Ghttps://cdsarc.cds.unistra.fr/viz-bin/cat/J/ApJ/884/11CatalogResearchIsServedByTAP VizieR generic serviceIsServedByConesearch servicerelated-toJ/ApJ/666/806 : SED of Spitzer quasars (QUEST) (Netzer+, 2007)J/ApJ/656/770 : Mid-IR spectrum of star-forming galaxies (Smith+, 2007)J/A+A/558/A149 : VLTI/MIDI AGN Large Program observations (Burtscher+, 2013)J/A+A/578/A74 : Nuclear obscuration in LINERs (Gonzalez-Martin+, 2015)J/ApJ/803/109 : Spitzer/IRS decompositon of AGN (Hernan-Caballero+, 2015)J/A+A/583/A120 : AGN torus models. SED library (Siebenmorgen+, 2015)J/ApJ/822/109 : MIR view of polar dust emission in local AGNs (Asmus+, 2016)J/ApJ/841/37 : Tori in AGNs through Spitzer/IRS obs. (Gonzalez-Martin+, 2017)J/ApJS/235/4 : The 105-month Swift-BAT all-sky hard X-ray survey (Oh+, 2018)https://cds.unistra.fr/vizier-org/licences_vizier.htmlhttps://vizier.cds.unistra.fr/viz-bin/VizieR-2?-source=J/ApJ/884/11https://vizier.iucaa.in/viz-bin/VizieR-2?-source=J/ApJ/884/11http://vizieridia.saao.ac.za/viz-bin/VizieR-2?-source=J/ApJ/884/11https://vizier.cds.unistra.fr/viz-bin/votable?-source=J/ApJ/884/11https://vizier.iucaa.in/viz-bin/votable?-source=J/ApJ/884/11http://vizieridia.saao.ac.za/viz-bin/votable?-source=J/ApJ/884/11GETtext/xml+votablehttps://tapvizier.cds.unistra.fr/TAPVizieR/tapCone search capability for table J/ApJ/884/11/table1 (Observational details of the AGN sample)https://vizier.cds.unistra.fr/viz-bin/conesearch/J/ApJ/884/11/table1?https://vizier.iucaa.in/viz-bin/conesearch/J/ApJ/884/11/table1?http://vizieridia.saao.ac.za/viz-bin/conesearch/J/ApJ/884/11/table1?GETtext/xml+votable180.050000true10.7200431-23.54103140.0055555555555555566/953 1008 1701 2221 2564 4215 4439 4540 5423 6212 6458 6752 7157 7246 7386 7552 7659 7728 7741 7869 8029 8216 8299 8922 9612 10912 10977 11101 11158 11388 11416 11898 13720 16690 17525 17727 18484 18794 19296 19373 19376 19738 20489 20556 21036 21450 21996 22626 22890 22912 22975 23323 24123 24748 24842 25182 25610 25735 25742 25790 25907 26041 26300 26393 26547 27721 27767 28075 28350 28535 28593 28609 29795 34104 34318 34913 35246 35284 35623 35899 36158 36216 36785 38268 38653 38696 38704 38868 40221 40818 41586 41982 42016 42027 43262 43634 43677 43806 43877 44551 45735 45883 46615 46811 46987 47047 47120 47154 47169 49042https://cdsarc.cds.unistra.fr/viz-bin/moc/J/ApJ/884/11?format=asciiX-rayInfrareddefaultJ/ApJ/884/11/table1Observational details of the AGN sampleSimbadNameSimbad column added by the CDSmeta.idcharTypeAGN optical class (1)src.classcharrecnoRecord number assigned by the VizieR team. Should Not be used for identification.meta.recordintSeq[1/110] Sequential running number identifiermeta.idintNameObject Namemeta.id;meta.maincharprimaryDist[3.8/168.6] DistanceMpcpos.distance;pos.heliocentricfloatScale[0.1/2.9] Spatial scale obtained with the short-low Spitzer/IRS spectral modulekpcinstr.scalefloatlogLx[40.8/44.5] Log of Swift BAT 14-195keV intrinsic X-ray luminosity (Oh+ 2018, J/ApJS/235/4)log(solLum)phys.luminosity;em.X-rayfloatSwiftDisplay data from the 105-month Swift-BAT (Oh+, 2018, J/ApJS/235/4) within 3'meta.ref.urlcharNEDNED column added by the CDSmeta.ref.urlchar_RARight Ascension (J2000) from SIMBAD (not part of the original data)degpos.eq.ra;meta.main_DEDeclination (J2000) from SIMBAD (not part of the original data)degpos.eq.dec;meta.mainJ/ApJ/884/11/table4Spectral fits for the AGN samplerecnoRecord number assigned by the VizieR team. Should Not be used for identification.meta.recordintNameObject Namemeta.id;meta.maincharprimaryModelModel Name (1)meta.id;stat.param;meta.modelledcharClass[0/2] Classification (2)meta.code.classintpAGN[1.1/100] AGN model contribution (3)%stat.param;meta.modelledfloatpStellar[0/70] Stellar model contribution (3)%stat.param;meta.modelledfloatpISM[0/98.6] ISM model contribution (3)%stat.param;meta.modelledfloatchi2[0.39/192.6] Reduced {chi}^2^ ({chi}^2^/dof)stat.fit.chi2floatE(B-V)[0/10.2]? Color excess for the foreground extinctionmagphot.color.excessfloatnullableb_E(B-V)[0/10.1]? E(B-V) Lower confidence interval on E (B-V)magstat.fit.paramfloatnullableB_E(B-V)[0/10.4] E(B-V) Upper confidence interval on E (B-V)magstat.fit.paramfloatP1[0.2/89.5]? Model Parameter 1 (4)stat.paramfloatnullableb_P1[0/90]? Lower bound on confidence interval or limit on P1 (5)stat.fit.paramfloatnullableB_P1[0/89.7]? Upper bound on confidence interval or limit on P1 (5)stat.fit.paramfloatnullableP2[1.3/78.3]? Model Parameter 2 (4)stat.paramfloatnullableb_P2[1.1/80]? Lower bound on confidence interval or limit on P2 (5)stat.fit.paramfloatnullableB_P2[1/78.7]? Upper bound on confidence interval or limit on P2 (5)stat.fit.paramfloatnullableP3[-2.93/76.3]? Model Parameter 3 (4)stat.paramfloatnullableb_P3[-2.94/77.7]? Lower bound on confidence interval or limit on P3 (5)stat.fit.paramfloatnullableB_P3[-3/77]? Upper bound on confidence interval or limit on P3 (5)stat.fit.paramfloatnullableP4[-1.92/237]? Model Parameter 4 (4)stat.paramfloatnullableb_P4[-1.93/999]? Lower bound on confidence interval or limit on P4 (5)stat.fit.paramfloatnullableB_P4[-2/262]? Upper bound on confidence interval or limit on P4 (5)stat.fit.paramfloatnullableP5[0.3/143.2]? Model Parameter 5 (4)stat.paramfloatnullableb_P5[0.1/149.3]? Lower bound on confidence interval or limit on P5 (5)stat.fit.paramfloatnullableB_P5[0/149.5]? Upper bound on confidence interval or limit on P5 (5)stat.fit.paramfloatnullableP6[-2.3/195.5]? Model Parameter 6 (4)stat.paramfloatnullableb_P6[-2.4/289.1]? Lower bound on confidence interval or limit on P6 (5)stat.fit.paramfloatnullableB_P6[-2.5/224.3]? Upper bound on confidence interval or limit on P6 (5)stat.fit.paramfloatnullableP7[0.2/0.41]? Model Parameter 7 (4)stat.paramfloatnullableb_P7[0.17/0.5]? Lower bound on confidence interval or limit on P7 (5)stat.fit.paramfloatnullableB_P7[0.1/0.45]? Upper bound on confidence interval or limit on P7 (5)stat.fit.paramfloatnullableP8[0.18/0.7]? Model Parameter 8 (4)stat.paramfloatnullableb_P8[0.16/0.7]? Lower bound on confidence interval or limit on P8 (5)stat.fit.paramfloatnullableB_P8[0.19/0.74]? Upper bound on confidence interval or limit on P8 (5)stat.fit.paramfloatnullable