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arXiv 2106.12466gr-qcastro-ph.HEastro-ph.IM
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强透镜引力波事例的机器学习快速识别

Rapid Identification of Strongly Lensed Gravitational-Wave Events with Machine Learning

Srashti Goyal, Harikrishnan D., Shasvath J. Kapadia, Parameswaran Ajith

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11 pages, 6 figures
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摘要：

预计将由第二代和第三代探测器探测到的一小部分引力波（GW）信号将被星系和星系团强烈透镜化，产生多个可观测的副本。虽然开发了最佳贝叶斯模型选择方法来识别透镜信号，但处理用第二（第三）代检测器检测到的数万（数十亿）可能的事件对既计算密集又耗时。为了缓解这个问题，我们建议使用机器学习来快速排除绝大多数候选透镜对。作为一个证明的原则，我们模拟非旋转的二元黑洞事件添加到高斯噪声，和训练机器上的时间-频率图（Q变换）和本地化的天空图（使用Bayestar），这两个都可以在几秒钟内生成。我们表明，经过训练的机器能够准确地识别透镜对，其效率与现有的贝叶斯方法相当。

英文摘要：

A small fraction of the gravitational-wave (GW) signals that will be detected by second and third generation detectors are expected to be strongly lensed by galaxies and clusters, producing multiple observable copies. While optimal Bayesian model selection methods are developed to identify lensed signals, processing tens of thousands (billions) of possible pairs of events detected with second (third) generation detectors is both computationally intensive and time consuming. To mitigate this problem, we propose to use machine learning to rapidly rule out a vast majority of candidate lensed pairs. As a proof of principle, we simulate non-spinning binary black hole events added to Gaussian noise, and train the machine on their time-frequency maps (Q-transforms) and localisation skymaps (using Bayestar), both of which can be generated in seconds. We show that the trained machine is able to accurately identify lensed pairs with efficiencies comparable to existing Bayesian methods.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。

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