J/ApJ/887/18ivo://CDS.VizieR/J/ApJ/887/18doi:10.26093/cds/vizier.18870018CDSKaur A.Falcone A.D.Stroh M.D.Kennea J.A.Ferrara E.C.2024-10-07T20:11:08Z2021-06-04T10:35:26ZCDS support team

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

cds-question@unistra.frgamma-ray-astronomyx-ray-sourcesactive-galactic-nucleipulsarsApproximately one-third of the gamma-ray sources in the third Fermi-LAT catalog are unidentified or unassociated with objects at other wavelengths. Observations with the X-Ray Telescope on the Neil Gehrels Swift Observatory (Swift-XRT) have yielded possible counterparts in ~30% of these source regions. The objective of this work is to identify the nature of these possible counterparts, utilizing their gamma-ray properties coupled with the Swift derived X-ray properties. The majority of the known sources in the Fermi catalogs are blazars, which constitute the bulk of the extragalactic gamma-ray source population. The galactic population on the other hand is dominated by pulsars. Overall, these two categories constitute the majority of all gamma-ray objects. Blazars and pulsars occupy different parameter space when X-ray fluxes are compared with various gamma-ray properties. In this work, we utilize the X-ray observations performed with the Swift-XRT for the unknown Fermi sources and compare their X-ray and gamma-ray properties to differentiate between the two source classes. We employ two machine-learning algorithms, decision tree and random forest (RF) classifier, to our high signal-to-noise ratio sample of 217 sources, each of which corresponds to Fermi unassociated regions. The accuracy scores for both methods were found to be 97% and 99%, respectively. The RF classifier, which is based on the application of a multitude of decision trees, associated a probability value (P_bzr_) for each source to be a blazar. This yielded 173 blazar candidates from this source sample, with P_bzr_>=90% for each of these sources, and 134 of these possible blazar source associations had P_bzr_>=99%. The results yielded 13 sources with P_bzr_<=10%, which we deemed as reasonable candidates for pulsars, seven of which result with P_bzr_<=1%. There were 31 sources that exhibited intermediate probabilities and were termed ambiguous due to their unclear characterization as a pulsar or a blazar.2019ApJ...887...18Khttps://cdsarc.cds.unistra.fr/viz-bin/cat/J/ApJ/887/18CatalogResearchIsServedByTAP VizieR generic serviceIsServedByConesearch servicerelated-toVIII/65 : 1.4GHz NRAO VLA Sky Survey (NVSS) (Condon+ 1998)VIII/81 : Sydney University Molonglo Sky Survey (SUMSS V2.1) (Mauch+ 2008)J/ApJS/188/405 : Fermi-LAT first source catalog (1FGL) (Abdo+, 2010)J/MNRAS/424/L64 : AGN/pulsar distinction for 2FGL sources (Mirabal+, 2012)J/ApJS/199/31 : Fermi LAT second source catalog (2FGL) (Nolan+, 2012)J/ApJS/206/12 : Blazars with {gamma}-ray counterparts. I. (D'Abrusco+, 2013)J/MNRAS/432/1294 : Fermi unassociated sources ATCA observations (Petrov+, 2013)J/ApJS/218/23 : Fermi LAT third source catalog (3FGL) (Acero+, 2015)J/ApJ/810/14 : Third catalog of LAT-detected AGNs (3LAC) (Ackermann+, 2015)J/ApJ/820/8 : 3FGL sources statistical classif. (Saz Parkinson+, 2016)J/MNRAS/462/3180 : 3FGL Blazar of Unknown Type classification (Chiaro+, 2016)J/A+A/602/A86 : Blazar candidates among Fermi/LAT 3FGL (Lefaucheur+, 2017)J/MNRAS/470/1291 : Classifying 3FGL with ANN (Salvetti+, 2017)J/ApJ/854/99 : The Einstein@Home gamma-ray pulsar survey. II. (Wu+, 2018)J/ApJS/247/33 : Fermi LAT fourth source catalog (4FGL) (Abdollahi+, 2020)J/MNRAS/493/1926 : 4FGL blazar classification neural network (Kovacevic+, 2020)https://cds.unistra.fr/vizier-org/licences_vizier.htmlhttps://vizier.cds.unistra.fr/viz-bin/VizieR-2?-source=J/ApJ/887/18https://vizier.iucaa.in/viz-bin/VizieR-2?-source=J/ApJ/887/18http://vizieridia.saao.ac.za/viz-bin/VizieR-2?-source=J/ApJ/887/18https://vizier.cds.unistra.fr/viz-bin/votable?-source=J/ApJ/887/18https://vizier.iucaa.in/viz-bin/votable?-source=J/ApJ/887/18http://vizieridia.saao.ac.za/viz-bin/votable?-source=J/ApJ/887/18GETtext/xml+votablehttps://tapvizier.cds.unistra.fr/TAPVizieR/tapCone search capability for table J/ApJ/887/18/table1 (Classification with machine learning)https://vizier.cds.unistra.fr/viz-bin/conesearch/J/ApJ/887/18/table1?https://vizier.iucaa.in/viz-bin/conesearch/J/ApJ/887/18/table1?http://vizieridia.saao.ac.za/viz-bin/conesearch/J/ApJ/887/18/table1?GETtext/xml+votable180.050000true0.386667-41.9230560.0055555555555555566/50 172 267 897 1973 2667 2808 3234 3272 3338 3369 3616 3619 3629 3700 3915 4260 8485 9032 10194 10202 10971 11689 12179 12313 12410 12443 12476 12736 12871 13034 13056 13324 13334 13720 13929 14150 14185 14219 14274 14320 15488 15783 15910 15994 15996 16137 17095 17154 17819 18666 18852 18985 19525 19545 19673 20110 20473 21164 21593 21710 21808 21898 22239 22683 22894 23009 23105 23825 23835 24064 24512 24641 24655 24789 24836 25095 25142 25370 25387 25403 25468 25525 25781 25820 25865 26125 26156 26513 27381 27423 27772 28037 28290 28560 28736 28772 30154 30195-30196 30256 30284 30518 30573 31011 31116 31239 32035 32086 32288 33059 33087 33198 33243 33506 33528 34171 34437 34623 34772 35063 35227 35498 35594 36100 36133 36394 36445 36615 36951 37112 37492 37744 37922 37940 37991 38016 38112 38127 38176 38192 38873 39139 39208 39978 40422 40585 41091 41359 41510 41518 41574 41655 41683 41699 41772 41824 42222 42385 42436 42566 42920 43038 43197 43643 43710 43746 43877 44049 44123 44255 44913 45011 45077 45226 45242 45316 45847 46122 47154 47196 47461 48201 48259 48854 48858https://cdsarc.cds.unistra.fr/viz-bin/moc/J/ApJ/887/18?format=asciiGamma-rayX-rayRadiodefaultJ/ApJ/887/18/table1Classification with machine learningrecnoRecord number assigned by the VizieR team. Should Not be used for identification.meta.recordint_RAPositions from SwF3 names (right ascension part)degpos.eq.ra;meta.main_DEPositions from SwF3 names (declination part)degpos.eq.dec;meta.mainSwF3Swift name (JHHMMSS.s+DDMMSS)meta.id;meta.maincharf_SwF3[b-d] Flag on SwF3 (1)meta.codechar3FGLFermi name (JHHMM.m+DDMM)meta.id.crosscharClassObject class (2)meta.code.classcharPbz[0/1] Random forest blazar probabilitystat.fit.goodnessfloatFx[0.7/285] 0.1-2.4keV X-ray flux in the units of 10^-13^erg/cm^2^/s1e-16W.m**-2phot.flux;em.X-rayfloatFgam[5.3/536] 0.1-100GeV gamma-ray flux in the units of 10^-13^erg/cm^2^/s1e-16W.m**-2phot.flux.density;em.gamma.hardfloatOTObject type in literaturesrc.classcharr_OTReference on OT (Author and bibcode)meta.ref;pos.framecharOT2Second object type from 4FGL (Abdollahi+, 2020, J/ApJS/247/33)src.classcharSimbadSimbad column added by the CDSmeta.ref.urlcharNEDNED column added by the CDSmeta.ref.urlchar