The detection, extraction and parameter estimation of extreme-mass-ratio inspirals with deep learning

IF 6.4 1区 物理与天体物理 Q1 PHYSICS, MULTIDISCIPLINARY Science China Physics, Mechanics & Astronomy Pub Date : 2024-11-12 DOI:10.1007/s11433-024-2500-x
Qianyun Yun, Wen-Biao Han, Yi-Yang Guo, He Wang, Minghui Du
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Abstract

One of the primary goals of space-borne gravitational wave detectors is to detect and analyze extreme-mass-ratio inspirals (EMRIs). This task is particularly challenging because EMRI signals are complex, lengthy, and faint. In this work, we introduce a 2-layer convolutional neural network (CNN) approach to detect EMRI signals for space-borne detectors, achieving a true positive rate (TPR) of 96.9% at a 1% false positive rate (FPR) for signal-to-noise ratio (SNR) from 50 to 100. Especially, the key intrinsic parameters of EMRIs such as the mass, spin of the supermassive black hole (SMBH) and the initial eccentricity of the orbit can also be inferred directly by employing a neural network. The mass and spin of the SMBH can be determined at 99% and 92% respectively. This will greatly reduce the parameter spaces and computing cost for the following Bayesian parameter estimation. Our model also has a low dependency on the accuracy of the waveform model. This study underscores the potential of deep learning methods in EMRI data analysis, enabling the rapid detection of EMRI signals and efficient parameter estimation.

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利用深度学习对极端质量比吸气进行检测、提取和参数估计
空间引力波探测器的主要目标之一是探测和分析极质量比吸气(EMRI)。由于 EMRI 信号复杂、冗长且微弱,因此这项任务尤其具有挑战性。在这项工作中,我们引入了一种双层卷积神经网络(CNN)方法,用于检测空间探测器的 EMRI 信号,在信噪比(SNR)为 50 到 100 的情况下,真阳性率(TPR)达到 96.9%,假阳性率(FPR)为 1%。特别是,超大质量黑洞(SMBH)的质量和自旋以及轨道的初始偏心率等 EMRI 的关键内在参数也可以通过神经网络直接推断。超大质量黑洞的质量和自旋的确定率分别为 99% 和 92%。这将大大减少接下来贝叶斯参数估计的参数空间和计算成本。我们的模型对波形模型精度的依赖性也很低。这项研究强调了深度学习方法在 EMRI 数据分析中的潜力,使 EMRI 信号的快速检测和高效参数估计成为可能。
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来源期刊
Science China Physics, Mechanics & Astronomy
Science China Physics, Mechanics & Astronomy PHYSICS, MULTIDISCIPLINARY-
CiteScore
10.30
自引率
6.20%
发文量
4047
审稿时长
3 months
期刊介绍: Science China Physics, Mechanics & Astronomy, an academic journal cosponsored by the Chinese Academy of Sciences and the National Natural Science Foundation of China, and published by Science China Press, is committed to publishing high-quality, original results in both basic and applied research. Science China Physics, Mechanics & Astronomy, is published in both print and electronic forms. It is indexed by Science Citation Index. Categories of articles: Reviews summarize representative results and achievements in a particular topic or an area, comment on the current state of research, and advise on the research directions. The author’s own opinion and related discussion is requested. Research papers report on important original results in all areas of physics, mechanics and astronomy. Brief reports present short reports in a timely manner of the latest important results.
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