Search for glitches of gamma-ray pulsars with deep learning

Autor:	Sokolova, E. V., Panin, A. G.
Rok vydání:	2020
Předmět:	Astrophysics - Instrumentation and Methods for Astrophysics Astrophysics - High Energy Astrophysical Phenomena
Zdroj:	A&A 660, A43 (2022)
Druh dokumentu:	Working Paper
DOI:	10.1051/0004-6361/202038822
Popis:	The pulsar glitches are generally assumed to be an apparent manifestation of the superfluid interior of the neutron stars. Most of them were discovered and extensively studied by continuous monitoring in the radio wavelengths. The Fermi-LAT space telescope has made a revolution uncovering a large population of gamma-ray pulsars. In this paper we suggest to employ these observations for the searches of new glitches. We develop the method capable of detecting step-like frequency change associated with glitches in a sparse gamma-ray data. It is based on the calculations of the weighted H-test statistics and glitch identification by a convolutional neural network. The method demonstrates high accuracy on the Monte Carlo set and will be applied for searches of the pulsar glitches in the real gamma-ray data in the future works. Comment: 5 pages, 5 figures
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2006.15956 Zobrazit plný text záznamu View this record from Arxiv