论文标题
搜索具有深度学习的伽马射线脉冲星的故障
Search for glitches of gamma-ray pulsars with deep learning
论文作者
论文摘要
普遍认为脉冲星故障是中子星的超流体内部的明显表现。通过无线电波长进行连续监测发现并广泛研究了其中的大多数。费米 - 拉特太空望远镜使一场革命揭示了大量的伽马射线脉冲星。在本文中，我们建议将这些观察结果用于搜索新故障。我们开发了能够检测稀疏伽马射线数据中与故障相关的阶梯状频率变化的方法。它基于卷积神经网络对加权H检验统计数据和故障识别的计算。该方法证明了蒙特卡洛集合的高精度，将来将应用于将来的实际伽马射线数据中的脉冲星故障。
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.
