Classification of New X-Ray Counterparts for Fermi Unassociated Gamma-Ray Sources Using the Swift X-Ray Telescope
Approximately 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 regio...
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description | Approximately 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 (Pbzr) for each source to be a blazar. This yielded 173 blazar candidates from this source sample, with Pbzr ≥ 90% for each of these sources, and 134 of these possible blazar source associations had Pbzr ≥ 99%. The results yielded 13 sources with Pbzr ≤ 10%, which we deemed as reasonable candidates for pulsars, seven of which result with Pbzr ≤ 1%. There were 31 sources that exhibited intermediate probabilities and were termed ambiguous due to their unclear characterization as a pulsar or a blazar. |
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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 (Pbzr) for each source to be a blazar. This yielded 173 blazar candidates from this source sample, with Pbzr ≥ 90% for each of these sources, and 134 of these possible blazar source associations had Pbzr ≥ 99%. The results yielded 13 sources with Pbzr ≤ 10%, which we deemed as reasonable candidates for pulsars, seven of which result with Pbzr ≤ 1%. There were 31 sources that exhibited intermediate probabilities and were termed ambiguous due to their unclear characterization as a pulsar or a blazar.</description><identifier>ISSN: 0004-637X</identifier><identifier>EISSN: 1538-4357</identifier><identifier>DOI: 10.3847/1538-4357/ab4ceb</identifier><language>eng</language><publisher>Philadelphia: The American Astronomical Society</publisher><subject>Algorithms ; Astrophysics ; Blazars ; Classifiers ; Decision trees ; Fluxes ; Gamma ray sources ; Gamma rays ; Machine learning ; Properties (attributes) ; Pulsars ; Signal to noise ratio ; Wavelengths ; X ray sources ; X ray telescopes ; X-ray fluxes</subject><ispartof>The Astrophysical journal, 2019-12, Vol.887 (1), p.18</ispartof><rights>2019. The American Astronomical Society. 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J</addtitle><description>Approximately 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 (Pbzr) for each source to be a blazar. This yielded 173 blazar candidates from this source sample, with Pbzr ≥ 90% for each of these sources, and 134 of these possible blazar source associations had Pbzr ≥ 99%. The results yielded 13 sources with Pbzr ≤ 10%, which we deemed as reasonable candidates for pulsars, seven of which result with Pbzr ≤ 1%. There were 31 sources that exhibited intermediate probabilities and were termed ambiguous due to their unclear characterization as a pulsar or a blazar.</description><subject>Algorithms</subject><subject>Astrophysics</subject><subject>Blazars</subject><subject>Classifiers</subject><subject>Decision trees</subject><subject>Fluxes</subject><subject>Gamma ray sources</subject><subject>Gamma rays</subject><subject>Machine learning</subject><subject>Properties (attributes)</subject><subject>Pulsars</subject><subject>Signal to noise ratio</subject><subject>Wavelengths</subject><subject>X ray sources</subject><subject>X ray telescopes</subject><subject>X-ray fluxes</subject><issn>0004-637X</issn><issn>1538-4357</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2019</creationdate><recordtype>article</recordtype><recordid>eNp1kEFLwzAYhoMoOKd3jwE9WpcsTZscZbgpDAW3wW4hTRPNWJuadIz9e1M79KKn8IXnfb-PB4BrjO4JS_MRpoQlKaH5SBap0sUJGPx8nYIBQihNMpKvz8FFCJtuHHM-AJ-TrQzBGqtka10NnYEveg_XyZs8wInb1a32jfRtgMZ5ONW-snBVx4hTVra6hDNZVfKbXridVzrAVbD1O2w_NFzsrWmPXUu91UG5Rl-CMyO3QV8d3yFYTR-Xk6dk_jp7njzME0Vy3ibUKM5zxBHRsih5bigjFCluMq4NQzkqCkwkw4YUhBBZcEXLjOYlH8sCqxKTIbjpexvvPnc6tGITD6zjSjGOTmjGU0YihXpKeReC10Y03lbSHwRGohMrOouisyh6sTFy20esa347ZbMRjEVcYCaa0kTs7g_s39Yvnh-Hzw</recordid><startdate>20191210</startdate><enddate>20191210</enddate><creator>Kaur, Amanpreet</creator><creator>Falcone, Abraham D.</creator><creator>Stroh, Michael D.</creator><creator>Kennea, Jamie A.</creator><creator>Ferrara, Elizabeth C.</creator><general>The American Astronomical Society</general><general>IOP Publishing</general><scope>AAYXX</scope><scope>CITATION</scope><scope>7TG</scope><scope>8FD</scope><scope>H8D</scope><scope>KL.</scope><scope>L7M</scope><orcidid>https://orcid.org/0000-0002-6745-4790</orcidid><orcidid>https://orcid.org/0000-0002-0878-1193</orcidid></search><sort><creationdate>20191210</creationdate><title>Classification of New X-Ray Counterparts for Fermi Unassociated Gamma-Ray Sources Using the Swift X-Ray Telescope</title><author>Kaur, Amanpreet ; Falcone, Abraham D. ; Stroh, Michael D. ; Kennea, Jamie A. ; Ferrara, Elizabeth C.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c379t-5fc9970903eabd97f58350c9f69ef8070bb13a81f3b333ab9c5d657d92ab1cd13</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2019</creationdate><topic>Algorithms</topic><topic>Astrophysics</topic><topic>Blazars</topic><topic>Classifiers</topic><topic>Decision trees</topic><topic>Fluxes</topic><topic>Gamma ray sources</topic><topic>Gamma rays</topic><topic>Machine learning</topic><topic>Properties (attributes)</topic><topic>Pulsars</topic><topic>Signal to noise ratio</topic><topic>Wavelengths</topic><topic>X ray sources</topic><topic>X ray telescopes</topic><topic>X-ray fluxes</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Kaur, Amanpreet</creatorcontrib><creatorcontrib>Falcone, Abraham D.</creatorcontrib><creatorcontrib>Stroh, Michael D.</creatorcontrib><creatorcontrib>Kennea, Jamie A.</creatorcontrib><creatorcontrib>Ferrara, Elizabeth C.</creatorcontrib><collection>CrossRef</collection><collection>Meteorological & Geoastrophysical Abstracts</collection><collection>Technology Research Database</collection><collection>Aerospace Database</collection><collection>Meteorological & Geoastrophysical Abstracts - Academic</collection><collection>Advanced Technologies Database with Aerospace</collection><jtitle>The Astrophysical journal</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Kaur, Amanpreet</au><au>Falcone, Abraham D.</au><au>Stroh, Michael D.</au><au>Kennea, Jamie A.</au><au>Ferrara, Elizabeth C.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Classification of New X-Ray Counterparts for Fermi Unassociated Gamma-Ray Sources Using the Swift X-Ray Telescope</atitle><jtitle>The Astrophysical journal</jtitle><stitle>APJ</stitle><addtitle>Astrophys. J</addtitle><date>2019-12-10</date><risdate>2019</risdate><volume>887</volume><issue>1</issue><spage>18</spage><pages>18-</pages><issn>0004-637X</issn><eissn>1538-4357</eissn><abstract>Approximately 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 (Pbzr) for each source to be a blazar. This yielded 173 blazar candidates from this source sample, with Pbzr ≥ 90% for each of these sources, and 134 of these possible blazar source associations had Pbzr ≥ 99%. The results yielded 13 sources with Pbzr ≤ 10%, which we deemed as reasonable candidates for pulsars, seven of which result with Pbzr ≤ 1%. There were 31 sources that exhibited intermediate probabilities and were termed ambiguous due to their unclear characterization as a pulsar or a blazar.</abstract><cop>Philadelphia</cop><pub>The American Astronomical Society</pub><doi>10.3847/1538-4357/ab4ceb</doi><tpages>11</tpages><orcidid>https://orcid.org/0000-0002-6745-4790</orcidid><orcidid>https://orcid.org/0000-0002-0878-1193</orcidid><oa>free_for_read</oa></addata></record> |
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subjects | Algorithms Astrophysics Blazars Classifiers Decision trees Fluxes Gamma ray sources Gamma rays Machine learning Properties (attributes) Pulsars Signal to noise ratio Wavelengths X ray sources X ray telescopes X-ray fluxes |
title | Classification of New X-Ray Counterparts for Fermi Unassociated Gamma-Ray Sources Using the Swift X-Ray Telescope |
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