Advanced search
Start date
(Reference retrieved automatically from Web of Science through information on FAPESP grant and its corresponding number as mentioned in the publication by the authors.)

On the Physical Association of Fermi-LAT Blazars with Their Low-energy Counterparts

Full text
de Menezes, Raniere [1, 2] ; D'Abrusco, Raffaele [3] ; Massaro, Francesco [4, 5, 1, 6] ; Gasparrini, Dario [7, 8] ; Nemmen, Rodrigo [2]
Total Authors: 5
[1] Univ Torino, Dipartimento Fis, Via Pietro Giuria 1, I-10125 Turin - Italy
[2] Univ Sao Paulo, Dept Astron, Rua Matao 1226, BR-05508090 Sao Paulo, SP - Brazil
[3] Ctr Astrophys Harvard & Smithsonian, 60 Garden St, Cambridge, MA 20138 - USA
[4] Ist Nazl Fis Nucl, Sez Torino, I-10125 Turin - Italy
[5] INAF Osservatorio Astrofis Torino, Via Osservatorio 20, I-10025 Pino Torinese - Italy
[6] CIFS, Via Pietro Giuria 1, I-10125 Turin - Italy
[7] Ist Nazl Fis Nucl, Sez Roma Tor Vergata, I-00133 Rome - Italy
[8] Space Sci Data Ctr Agenzia Spaziale Italiana, Via Politecn Snc, I-00133 Rome - Italy
Total Affiliations: 8
Document type: Journal article
Web of Science Citations: 0

Associating gamma-ray sources to their low-energy counterparts is one of the major challenges of modern gamma-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 gamma-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 gamma-ray blazars in 4FGL, based only on spectral parameters such as the gamma-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 gamma-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. (AU)

FAPESP's process: 16/25484-9 - Optical observations of blazar candidates and unknown gamma-ray sources
Grantee:Raniere Maciel de Menezes
Support type: Scholarships in Brazil - Doctorate
FAPESP's process: 18/24801-6 - Multislit spectroscopy of unidentified gamma-ray sources
Grantee:Raniere Maciel de Menezes
Support type: Scholarships abroad - Research Internship - Doctorate
FAPESP's process: 17/01461-2 - The extreme universe: black holes and the Fermi telescope
Grantee:Rodrigo Nemmen da Silva
Support type: Research Grants - Young Investigators Grants