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...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:The Astrophysical journal 2019-12, Vol.887 (1), p.18
Hauptverfasser: Kaur, Amanpreet, Falcone, Abraham D., Stroh, Michael D., Kennea, Jamie A., Ferrara, Elizabeth C.
Format: Artikel
Sprache:eng
Schlagworte:
Online-Zugang:Volltext bestellen
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung: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.
ISSN:0004-637X
1538-4357
DOI:10.3847/1538-4357/ab4ceb