Associating γ-ray sources to their low-energy counterparts is one of the major challenges of modern γ-ray astronomy. In the context of the Fourth Fermi Large Area Telescope Source Catalog (4FGL), the associations rely mainly on parameters such as apparent magnitude, integrated flux, and angular separation between the γ-ray source and its low-energy candidate counterpart. In this work, we propose a new use of the likelihood ratio (LR) and a complementary supervised learning technique to associate γ-ray blazars in 4FGL, based only on spectral parameters such as the γ-ray photon index, mid-infrared colors, and radio-loudness. In the LR approach, we crossmatch the Wide-field Infrared Survey Explorer Blazar-Like Radio-Loud Sources catalog with 4FGL and compare the resulting candidate counterparts with the sources listed in the γ-ray blazar locus to compute an association probability (AP) for 1138 counterparts. In the supervised learning approach, we train a random forest algorithm with 869 high-confidence blazar associations and 711 fake associations and then compute an AP for 1311 candidate counterparts. A list with all 4FGL blazar candidates of uncertain type associated by our method is provided to guide future optical spectroscopic follow-up observations.
On the Physical Association of Fermi-LAT Blazars with Their Low-energy Counterparts
D'Abrusco R.;Massaro F.;
2020-01-01
Abstract
Associating γ-ray sources to their low-energy counterparts is one of the major challenges of modern γ-ray astronomy. In the context of the Fourth Fermi Large Area Telescope Source Catalog (4FGL), the associations rely mainly on parameters such as apparent magnitude, integrated flux, and angular separation between the γ-ray source and its low-energy candidate counterpart. In this work, we propose a new use of the likelihood ratio (LR) and a complementary supervised learning technique to associate γ-ray blazars in 4FGL, based only on spectral parameters such as the γ-ray photon index, mid-infrared colors, and radio-loudness. In the LR approach, we crossmatch the Wide-field Infrared Survey Explorer Blazar-Like Radio-Loud Sources catalog with 4FGL and compare the resulting candidate counterparts with the sources listed in the γ-ray blazar locus to compute an association probability (AP) for 1138 counterparts. In the supervised learning approach, we train a random forest algorithm with 869 high-confidence blazar associations and 711 fake associations and then compute an AP for 1311 candidate counterparts. A list with all 4FGL blazar candidates of uncertain type associated by our method is provided to guide future optical spectroscopic follow-up observations.
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