Premerger detection of massive black hole binaries using deep learning

Fuente: arXiv
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Main Authors: Ruan, Wen-Hong, Guo, Zong-Kuan
Format: Preprint
Published: 2024
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author Ruan, Wen-Hong
Guo, Zong-Kuan
author_facet Ruan, Wen-Hong
Guo, Zong-Kuan
contents Coalescing massive black hole binaries (MBHBs) are one of primary sources for space-based gravitational wave (GW) observations. The mergers of these binaries are expected to give rise to detectable electromagnetic (EM) emissions with a narrow time window. The premerger detection of GW signals is vital for follow-up EM observations. The conventional approach for searching GW signals involves high computational costs. In this study, we present a deep learning model to search for GW signals from MBHBs. Our model is able to process 4.7 days of simulated data within 0.01 seconds and detect GW signals several hours to days before the final merger. The model provides the possibility of the coincident GW and EM detection of MBHBs.
format Preprint
id arxiv_https___arxiv_org_abs_2402_16282
institution arXiv
publishDate 2024
record_format arxiv
spellingShingle Premerger detection of massive black hole binaries using deep learning
Ruan, Wen-Hong
Guo, Zong-Kuan
Instrumentation and Methods for Astrophysics
General Relativity and Quantum Cosmology
Coalescing massive black hole binaries (MBHBs) are one of primary sources for space-based gravitational wave (GW) observations. The mergers of these binaries are expected to give rise to detectable electromagnetic (EM) emissions with a narrow time window. The premerger detection of GW signals is vital for follow-up EM observations. The conventional approach for searching GW signals involves high computational costs. In this study, we present a deep learning model to search for GW signals from MBHBs. Our model is able to process 4.7 days of simulated data within 0.01 seconds and detect GW signals several hours to days before the final merger. The model provides the possibility of the coincident GW and EM detection of MBHBs.
title Premerger detection of massive black hole binaries using deep learning
topic Instrumentation and Methods for Astrophysics
General Relativity and Quantum Cosmology
url https://arxiv.org/abs/2402.16282