TY - JOUR

T1 - Probing the unidentified Fermi blazar-like population using optical polarization and machine learning

AU - Liodakis, I.

AU - Blinov, D.

N1 - Publisher Copyright: © 2019 The Author(s) Published by Oxford University Press on behalf of the Royal Astronomical Society.

PY - 2019/7/1

Y1 - 2019/7/1

N2 - The Fermi γ-ray space telescope has revolutionized our view of the γ-ray sky and the high-energy processes in the Universe. While the number of known γ-ray emitters has increased by orders of magnitude since the launch of Fermi, there is an ever increasing number of, now more than a thousand, detected point sources whose low-energy counterpart is to this day unknown. To address this problem, we combined optical polarization measurements from the RoboPol survey as well as other discriminants of blazars from publicly available all-sky surveys in machine learning (ML, random forest and logistic regression) frameworks that could be used to identify blazars in the Fermi unidentified fields with an accuracy of >95 per cent. Out of the potential observational biases considered, blazar variability seems to have the most significant effect reducing the predictive power of the frameworks to ˜ 80-85 per cent. We apply our ML framework to six unidentified Fermi fields observed using the RoboPol polarimeter. We identified the same candidate source proposed by Mandarakas et al. for 3FGL J0221.2 + 2518.

AB - The Fermi γ-ray space telescope has revolutionized our view of the γ-ray sky and the high-energy processes in the Universe. While the number of known γ-ray emitters has increased by orders of magnitude since the launch of Fermi, there is an ever increasing number of, now more than a thousand, detected point sources whose low-energy counterpart is to this day unknown. To address this problem, we combined optical polarization measurements from the RoboPol survey as well as other discriminants of blazars from publicly available all-sky surveys in machine learning (ML, random forest and logistic regression) frameworks that could be used to identify blazars in the Fermi unidentified fields with an accuracy of >95 per cent. Out of the potential observational biases considered, blazar variability seems to have the most significant effect reducing the predictive power of the frameworks to ˜ 80-85 per cent. We apply our ML framework to six unidentified Fermi fields observed using the RoboPol polarimeter. We identified the same candidate source proposed by Mandarakas et al. for 3FGL J0221.2 + 2518.

KW - methods: statistical

KW - galaxies: active

KW - galaxies: jets

KW - PLANE ROTATIONS

KW - GAMMA-RAY-LOUD

KW - ROBOPOL

KW - FLARING ACTIVITY

UR - http://www.mendeley.com/research/probing-unidentified-fermi-blazarlike-population-using-optical-polarization-machine-learning

UR - http://www.scopus.com/inward/record.url?scp=85072265545&partnerID=8YFLogxK

U2 - 10.1093/mnras/stz1008

DO - 10.1093/mnras/stz1008

M3 - Article

VL - 486

SP - 3415

EP - 3422

JO - Monthly Notices of the Royal Astronomical Society

JF - Monthly Notices of the Royal Astronomical Society

SN - 0035-8711

IS - 3

ER -