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We apply a number of statistical and machine learning techniques to classifyand rank gamma-ray sources from the Third Fermi Large Area Telescope (LAT)Source Catalog (3FGL), according to their likelihood of falling into the twomajor classes of gamma-ray emitters: pulsars (PSR) or Active Galactic Nuclei(AGN). Using 1904 3FGL sources that have been identified/associated with AGN(1738) and PSR (166), we train (using 70% of our sample) and test (using 30%)our algorithms and find that the best overall accuracy (>96%) is obtained withthe Random Forest (RF) technique, while using a logistic regression (LR)algorithm results in only marginally lower accuracy. We apply the sametechniques on a sub-sample of 142 known gamma-ray pulsars to classify them intotwo major subcategories: young (YNG) and millisecond pulsars (MSP). Once more,the RF algorithm has the best overall accuracy (~90%), while a boosted LRanalysis comes a close second. We apply our two best models (RF and LR) to theentire 3FGL catalog, providing predictions on the likely nature of {\itunassociated} sources, including the likely type of pulsar (YNG or MSP). Wealso use our predictions to shed light on the possible nature of some gamma-raysources with known associations (e.g. binaries, SNR/PWN). Finally, we provide alist of plausible X-ray counterparts for some pulsar candidates, obtained usingSwift, Chandra, and XMM. The results of our study will be of interest for bothin-depth follow-up searches (e.g. pulsar) at various wavelengths, as well asfor broader population studies.

Classification and Ranking of Fermi LAT Gamma-ray Sources from the 3FGL Catalog using Machine Learning Techniques