Deep learning for intermittent gravitational wave signals
學年 111
學期 2
出版（發表）日期 2023-02-16
作品名稱 Deep learning for intermittent gravitational wave signals
作品名稱（其他語言）
著者 Takahiro S. Yamamoto, Sachiko Kuroyanagi, Guo-Chin Liu
單位
出版者
著錄名稱、卷期、頁數 Physical Review D 107, 044032
摘要 The ensemble of unresolved compact binary coalescences is a promising source of the stochastic gravitational-wave (GW) background. For stellar-mass black hole binaries, the astrophysical stochastic GW background is expected to exhibit non-Gaussianity due to their intermittent features. We investigate the application of deep learning to detect such a non-Gaussian stochastic GW background and demonstrate it with the toy model employed by Drasco and Flanagan in 2003, in which each burst is described by a single peak concentrated at a time bin. For the detection problem, we compare three neural networks with different structures: a shallower convolutional neural network (CNN), a deeper CNN, and a residual network. We show that the residual network can achieve comparable sensitivity as the conventional non-Gaussian statistic for signals with the astrophysical duty cycle of log10⁡𝜉∈[−3,−1]. Furthermore, we apply deep learning for parameter estimation with two approaches in which the neural network (1) directly provides the duty cycle and the signal-to-noise ratio and (2) classifies the data into four classes depending on the duty cycle value. This is the first step of a deep learning application for detecting a non-Gaussian stochastic GW background and extracting information on the astrophysical duty cycle.
關鍵字
語言 en_US
ISSN 2470-0029
期刊性質 國外
收錄於 SCI
產學合作
通訊作者
審稿制度 否
國別 USA
公開徵稿
出版型式 ,電子版
相關連結

機構典藏連結 ( http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/125521 )