Fermi's Sibyl: Mining the gamma-ray sky for dark matter subhaloes

Abstract

Dark matter annihilation signals coming from Galactic subhaloes may account for a small fraction of unassociated point sources detected in the Second Fermi-LAT catalogue (2FGL). To investigate this possibility, we present Sibyl, a Random Forest classifier that offers predictions on class memberships for unassociated Fermi-LAT sources at high Galactic latitudes using gamma-ray features extracted from the 2FGL. Sibyl generates a large ensemble of classification trees that are trained to vote on whether a particular object is an active galactic nucleus (AGN) or a pulsar. After training on a list of 908 identified/associated 2FGL sources, Sibyl reaches individual accuracy rates of up to 97.7% for AGNs and 96.5% for pulsars. Predictions for the 269 unassociated 2FGL sources at |b| > 10 degrees suggest that 216 are potential AGNs and 16 are potential pulsars (with majority votes greater than 70%). The remaining 37 objects are inconclusive, but none is an extreme outlier. These results could guide future quests for dark matter Galactic subhaloes.