Résultats du processus de synthèse de l'article arXiv : 2008.06550v1

Résumé : In recent years, improvements in Deep Learning techniques towards Gravitational Wave astronomy have led to a significant rise in various classification algorithm development. A few of these algorithms have been successfully employed to search gravitational waves from binary blackhole merger events. However, these algorithms still lack success with significant time duration and further prediction of the merger events parameters. In this work, we intended to advance the boundaries of deep learning techniques, using the convolutional neural networks, to go beyond binary classification and predicting complicated features that possess physical significance. This method is not a replacement for the already established and thoroughly examined methods like matched filtering for the detection of gravitational waves but is an alternative method wherein human interference is minimal. The deep learning model we present has been trained on 12s of data segment, aimed to predict the 27 features of any LIGO time series data. During training, the maximum accuracy attained was 90.93%, with a validation accuracy of 89.97%.