3FGL Demographics Outside the Galactic Plane using Supervised Machine Learning: Pulsar and Dark Matter Subhalo Interpretations
N. Mirabal, E. Charles, E. C. Ferrara, P. L. Gonthier, A. K. Harding,, M. A. S\'anchez-Conde, D. J. Thompson

TL;DR
This study uses machine learning to identify potential new gamma-ray sources outside the Galactic plane, exploring whether they are pulsars or dark matter subhalos, and provides constraints on dark matter properties.
Contribution
It applies supervised machine learning to classify unassociated gamma-ray sources and investigates their possible nature as pulsars or dark matter subhalos, offering new insights and constraints.
Findings
Identified 34 high-confidence Galactic candidates outside the plane.
Number of MSPs aligns with population synthesis predictions.
Placed upper limits on dark matter annihilation cross sections.
Abstract
Nearly 1/3 of the sources listed in the Third Fermi Large Area Telescope (LAT) catalog (3FGL) remain unassociated. It is possible that predicted and even unanticipated gamma-ray source classes are present in these data waiting to be discovered. Taking advantage of the excellent spectral capabilities achieved by the Fermi LAT, we use machine learning classifiers (Random Forest and XGBoost) to pinpoint potentially novel source classes in the unassociated 3FGL sample outside the Galactic plane. Here we report a total of 34 high-confidence Galactic candidates at |b| > 5 degrees. The currently favored standard astrophysical interpretations for these objects are pulsars or low-luminosity globular clusters hosting millisecond pulsars (MSPs). Yet, these objects could also be interpreted as dark matter annihilation taking place in ultra-faint dwarf galaxies or dark matter subhalos.…
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