Deep learning searches for gravitational wave stochastic backgrounds

被引：1

作者：

Utina, Andrei ^{[1
,2
]}

Marangio, Francesco ^{[3
]}

Morawski, Filip ^{[4
]}

Iess, Alberto ^{[5
]}

Regimbau, Tania ^{[6
]}

Fiameni, Giuseppe ^{[7
,8
]}

Cuoco, Elena ^{[9
]}

机构：

[1] Maastricht Univ, GWFP, Maastricht, Netherlands

[2] Nikhef, Maastricht, Netherlands

[3] LMU Munchen, Geschwister Scholl Pl 1, D-80539 Munich, Germany

[4] Polish Acad Sci, Nicolaus Copernicus Astron Ctr, Bartycka 18, PL-00716 Warsaw, Poland

[5] Univ Roma Tor Vergata, Dipartimento Fis, Rome, Italy

[6] CNRS, LAPP, 9 Chemin Bellevue, F-74941 Annecy Le Vieux, France

[7] NVIDIA AI Technol Ctr, Rome, Italy

[8] NVIDIA Corp, Santa Clara, CA USA

[9] Ist Nazl Fis Nucl, European Gravitat Observ EGO, Scuola Normale Super, Pisa, Italy

来源：

2021 INTERNATIONAL CONFERENCE ON CONTENT-BASED MULTIMEDIA INDEXING (CBMI) | 2021年

关键词：

Gravitational Wave Backgrounds; Deep Learning; CNN; LSTM; ET; LIGO;

D O I：

10.1109/CBMI50038.2021.9461904

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

The background of gravitational waves (GW) has long been studied and remains one of the most exciting aspects in the observation and analysis of gravitational radiation. The paper focuses on the search for the background of gravitational waves using deep neural networks. An astrophysical background due to the presence of many binary black hole coalescences was simulated for Advanced LIGO O3 sensitivity and the Einstein Telescope (ET) design sensitivity. The detection pipeline targets signal data out of the noisy detector background. Its architecture comprises of simulated whitened data as input to three classes of deep neural networks algorithms: a 1D and a 2D convolutional neural network (CNN) and a Long Short Term Memory (LSTM) network. It was found that all three algorithms could distinguish signals from noise with high precision for the ET sensitivity, but the current sensitivity of LIGO is too low to permit the algorithms to learn signal features from the input vectors.

引用

页码：171 / 176

页数：6

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