Deep Learning for estimating parameters of Gravitational Waves - Sorbonne Université
Journal Articles Monthly Notices of the Royal Astronomical Society Year : 2021

Deep Learning for estimating parameters of Gravitational Waves

Abstract

In recent years, improvements in Deep Learning (DL) techniques towards Gravitational Wave (GW) astronomy have led to a significant rise in the development of various classification algorithms that have been successfully employed to extract GWs of binary blackhole merger events from noisy time-series data. However, the success of these models is constrained by the length of time-sample and the class of GW source: binary blackhole and neutron star binaries to some extent. In this work, we intended to advance the boundaries of DL techniques using Convolutional Neural Networks, to go beyond binary classification and predict the physical parameters of the events. We aim to propose an alternative method that can be employed for realtime detection and parameter prediction. The DL model we present has been trained on 12s of data to predict the GW source parameters if detected. During training, the maximum accuracy attained was 90.93%, with a validation accuracy of 89.97%.
Fichier principal
Vignette du fichier
2008.06550.pdf (9.35 Mo) Télécharger le fichier
Origin Publication funded by an institution

Dates and versions

hal-03556934 , version 1 (04-02-2022)

Identifiers

Cite

Shashwat Singh, Amitesh Singh, Ankul Prajapati, Kamlesh N Pathak. Deep Learning for estimating parameters of Gravitational Waves. Monthly Notices of the Royal Astronomical Society, 2021, 508 (1), pp.1358-1370. ⟨10.1093/mnras/stab2417⟩. ⟨hal-03556934⟩
22 View
27 Download

Altmetric

Share

More