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Deep learning model on gravitational waveforms in merging and ringdown phases of binary black hole coalescences
Physical Review D ( IF 5 ) Pub Date : 2021-06-22 , DOI: 10.1103/physrevd.103.123023
Joongoo Lee , Sang Hoon Oh , Kyungmin Kim , Gihyuk Cho , John J. Oh , Edwin J. Son , Hyung Mok Lee

The waveform templates of the matched filtering-based gravitational-wave search ought to cover wide range of parameters for the prosperous detection. Numerical relativity (NR) has been widely accepted as the most accurate method for modeling the waveforms. Still, it is well known that NR typically requires a tremendous amount of computational costs. In this paper, we demonstrate a proof-of-concept of a novel deterministic deep learning (DL) architecture that can generate gravitational waveforms from the merger and ringdown phases of the non-spinning binary black hole coalescence. Our model takes O(1) seconds for generating approximately 1500 waveforms with a 99.9% match on average to one of the state-of-the-art waveform approximants, the effective-one-body. We also perform matched filtering with the DL-waveforms and find that the waveforms can recover the event time of the injected gravitational-wave signals.

中文翻译:

双黑洞合并合并和衰荡阶段引力波形的深度学习模型

基于匹配滤波的引力波搜索的波形模板应该涵盖广泛的参数范围,以实现繁荣检测。数值相对论 (NR) 已被广泛接受为最准确的波形建模方法。尽管如此,众所周知,NR 通常需要大量的计算成本。在本文中,我们展示了一种新型确定性深度学习 (DL) 架构的概念验证,该架构可以从非自旋双黑洞合并的合并和衰荡阶段生成引力波形。我们的模型需要(1)秒生成大约 1500 个波形,平均匹配度为 99.9%,与最先进的波形近似之一有效一体。我们还对 DL 波形进行匹配滤波,发现波形可以恢复注入引力波信号的事件时间。
更新日期:2021-06-22
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