Classification of New X-Ray Counterparts for Fermi Unassociated Gamma-Ray Sources Using the Swift X-Ray Telescope

Amanpreet Kaur, Abraham D. Falcone, Michael D. Stroh, Jamie A. Kennea, Elizabeth C. Ferrara

Research output: Contribution to journalArticle

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 (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.

Original languageEnglish (US)
Article number18
JournalAstrophysical Journal
Volume887
Issue number1
DOIs
StatePublished - Dec 10 2019

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telescopes
x rays
pulsars
gamma rays
blazars
signal-to-noise ratio
classifiers
catalogs
Swift observatory
observatory
wavelength
machine learning
signal to noise ratios
decision
wavelengths

All Science Journal Classification (ASJC) codes

  • Astronomy and Astrophysics
  • Space and Planetary Science

Cite this

@article{4a4ac1a89acd423ca02bfca7407073e6,
title = "Classification of New X-Ray Counterparts for Fermi Unassociated Gamma-Ray Sources Using the Swift X-Ray Telescope",
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 (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.",
author = "Amanpreet Kaur and Falcone, {Abraham D.} and Stroh, {Michael D.} and Kennea, {Jamie A.} and Ferrara, {Elizabeth C.}",
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Classification of New X-Ray Counterparts for Fermi Unassociated Gamma-Ray Sources Using the Swift X-Ray Telescope. / Kaur, Amanpreet; Falcone, Abraham D.; Stroh, Michael D.; Kennea, Jamie A.; Ferrara, Elizabeth C.

In: Astrophysical Journal, Vol. 887, No. 1, 18, 10.12.2019.

Research output: Contribution to journalArticle

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AB - 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 (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.

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