Volume 622, February 2019
|Number of page(s)||12|
|Section||Numerical methods and codes|
|Published online||05 February 2019|
Extending the event-weighted pulsation search to very faint gamma-ray sources
Laboratoire Leprince-Ringuet, Ecole polytechnique, CNRS/IN2P3, 91128 Palaiseau, France
Accepted: 17 December 2018
Context. Because of the relatively broad angular resolution of current gamma-ray instruments in the MeV–GeV energy range, the photons of a given source are mixed with those coming from nearby sources or diffuse background. This source confusion seriously hampers the search for pulsation from faint sources.
Aims. Statistical tests for pulsation can be made significantly more sensitive when the probability that a photon comes from the pulsar is used as a weight. However, computing this probability requires knowledge of the spectral model of all sources in the region of interest, including the pulsar itself. This is not possible for very faint pulsars that are not detected as gamma-ray sources or whose spectrum is not measured precisely enough. Extending the event-weighted pulsation search to such very faint gamma-ray sources would allow improving our knowledge of the gamma-ray pulsar population.
Methods. We present two methods that overcome this limitation by scanning the spectral parameter space, while minimizing the number of trials. The first one approximates the source to background ratio yielding a simple estimate of the weight while the second one makes use of the full spatial and spectral information of the region of interest around the pulsar.
Results. We tested these new methods on a sample of 144 gamma-ray pulsars already detected by the Fermi Large Area Telescope data. Both methods detect pulsation from all pulsars of the sample, including the ones for which no significant phase-averaged gamma-ray emission is detected.
Key words: gamma rays: general / pulsars: general / methods: data analysis / methods: statistical
© ESO 2019
Open Access article, published by EDP Sciences, under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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