Utilize este identificador para referenciar este registo:
https://hdl.handle.net/1822/76302
Título: | Gravitational-wave parameter inference using Deep Learning |
Autor(es): | Álvares, João D. Font, José A. Freitas, Felipe F. Freitas, Osvaldo G. Morais, António P. Nunes, Solange Onofre, A. Torres-Forné, Alejandro |
Palavras-chave: | GW astronomy convolutional neural networks spectrogram classification bayesian neural networks |
Data: | 2021 |
Editora: | IEEE |
Revista: | International Workshop on Content-Based Multimedia Indexing |
Citação: | J. D. Álvares et al., "Gravitational-wave parameter inference using Deep Learning," 2021 International Conference on Content-Based Multimedia Indexing (CBMI), 2021, pp. 1-6, doi: 10.1109/CBMI50038.2021.9461893. |
Resumo(s): | We explore machine learning methods to detect gravitational waves (GW) from binary black hole (BBH) mergers using deep learning (DL) algorithms. The DL networks are trained with gravitational waveforms obtained from BBH mergers with component masses randomly sampled in the range from 5 to 100 solar masses and luminosity distances from 100 Mpc to, at least, 2000 Mpc. The GW signal waveforms are injected in public data from the O2 run of the Advanced LIGO and Advanced Virgo detectors, in time windows that do not coincide with those of known detected signals, and the data from each detector in the Advanced LIGO and Advanced Virgo network is combined into a unique RGB image. We show that a classifier network can be trained in order to detect the presence of GW signal with high accuracy. Furthermore, we train a regression network to perform parameter inference on BBH spectrogram data. Without significant optimization of our algorithms we manage to corroborate most of the BBH detections in the GWTC-1 and GWTC-2 catalogs, and obtain parameter inference results that are mostly consistent with published results by the LIGO-Virgo Collaboration in GWTC-1. In particular, our predictions for the chirp mass are compatible (up to 3σ) with the official values for 90% of events. |
Tipo: | Artigo em ata de conferência |
URI: | https://hdl.handle.net/1822/76302 |
ISBN: | 9781665442206 |
DOI: | 10.1109/CBMI50038.2021.9461893 |
ISSN: | 1949-3983 |
Versão da editora: | https://ieeexplore.ieee.org/document/9461893 |
Arbitragem científica: | yes |
Acesso: | Acesso aberto |
Aparece nas coleções: | LIP - Artigos/papers |
Ficheiros deste registo:
Ficheiro | Descrição | Tamanho | Formato | |
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Gravitational-wave_parameter_inference_using_Deep_Learning.pdf | 377,73 kB | Adobe PDF | Ver/Abrir |