Looking for infrared counterparts of Fermi/LAT blazar candidates
Julien Lefaucheur, Catherine Boisson, Paolo Goldoni, Santiago, Pita

TL;DR
This paper develops a machine learning method to identify infrared counterparts of Fermi/LAT blazar candidates, aiming to improve multi-wavelength identification and support future gamma-ray and Cherenkov Telescope Array studies.
Contribution
The study introduces a novel machine learning approach to associate infrared counterparts with Fermi blazar candidates, enhancing identification accuracy.
Findings
Identified potential infrared counterparts for 3FGL blazar candidates.
Provided a list with estimated false positive rates for each association.
Enhanced the catalog of known extragalactic blazars.
Abstract
The Fermi/LAT telescope is an efficient blazar-detector in the MeV/GeV range. More than 1100 (900) blazars detected above 100 MeV (10 GeV) are clearly associated to BL Lacertae or Flat Spectrum Radio Quasar objects in the Fermi/LAT 3FGL catalogue. This number could significantly increase if multi-wavelength counterparts could be identified for the 573 3FGL blazars with unknown type, or even for the 1010 3FGL unassociated sources which are thought to be dominated by blazars, at least at high galactic latitude. Unfortunately, the size of the Fermi/LAT error box makes multi-wavelength follow-ups difficult. We propose a method to associate "blazar-like" infrared counterparts, having coordinates with a precision of a few arcseconds, to Fermi/LAT blazars and unassociated sources. To reach this goal, we built machine-learning classifiers based on the statistical differences of magnitude…
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