3FGL Demographics Outside the Galactic Plane using Supervised Machine Learning: Pulsar and Dark Matter Subhalo Interpretations

@article{Mirabal20163FGLDO,
  title={3FGL Demographics Outside the Galactic Plane using Supervised Machine Learning: Pulsar and Dark Matter Subhalo Interpretations},
  author={N. Mirabal and E. Charles and E. Ferrara and P. Gonthier and A. Harding and M. S'anchez-Conde and D. Thompson},
  journal={arXiv: High Energy Astrophysical Phenomena},
  year={2016}
}
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… Expand
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