Research and characterisation of blazar candidates in the Fermi/LAT 3FGL catalogue using multivariate classifications

In the recently published 3FGL catalogue, the Fermi/LAT collaboration reports the detection of gamma-ray emission from 3034 sources obtained after 4 years of observations. The nature of 1010 of those sources is unknown, whereas 2023 have counter-parts well identified in other wavelengths. Most of the associated sources are quoted as blazars (1717/2023), but the BL Lac or FSRQ nature of 573 of these blazars is still undetermined. The aim of this study is two-fold. First, to significantly increase the number of blazar candidates from a search among the high number of Fermi/LAT 3FGL unassociated sources. Second, to determine the BL Lac or FSRQ nature of the blazar candidates, including those determined as such in this work and the blazar candidates of uncertain type (BCU) already present in the 3FGL catalogue. For this purpose, multivariate classifiers -boosted decision trees and multilayer perceptron neural networks- are trained on the basis of carefully chosen discriminant parameters. The decisions of these classifiers have been combined in order to obtain high source identification efficiencies along with well controlled rates of fake associations. From this work we obtain a sample of 538 blazar candidates among the 1010 unassociated sources of the 3FGL catalogue. We obtain also a sample of 490 BL Lac and 270 FSRQs both from our 538 blazar candidates and from the sample of 573 BCUs of the 3FGL catalogue.