Applying deep neural networks to the detection and space parameter estimation of compact binary coalescence with a network of gravitational wave detectors

Kavli Affiliate: Li Xin Li

| First 5 Authors: Xilong Fan, Jin Li, Xin Li, Yuanhong Zhong, Junwei Cao

| Summary:

In this paper, we study an application of deep learning to the advanced LIGO
and advanced Virgo coincident detection of gravitational waves (GWs) from
compact binary star mergers. This deep learning method is an extension of the
Deep Filtering method used by George and Huerta (2017) for multi-inputs of
network detectors. Simulated coincident time series data sets in advanced LIGO
and advanced Virgo detectors are analyzed for estimating source luminosity
distance and sky location. As a classifier, our deep neural network (DNN) can
effectively recognize the presence of GW signals when the optimal
signal-to-noise ratio (SNR) of network detectors $geq$ 9. As a predictor, it
can also effectively estimate the corresponding source space parameters,
including the luminosity distance $D$, right ascension $alpha$, and
declination $delta$ of the compact binary star mergers. When the SNR of the
network detectors is greater than 8, their relative errors are all less than
23%. Our results demonstrate that Deep Filtering can process coincident GW time
series inputs and perform effective classification and multiple space parameter
estimation. Furthermore, we compare the results obtained from one, two, and
three network detectors; these results reveal that a larger number of network
detectors results in a better source location.

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