万全之策：基于多管道融合的引力波瞬变探测

中文摘要 AI 辅助

LIGO-Virgo-KAGRA合作组织及更广泛的研究群体在进行引力波瞬变（包括建模和未建模信号）搜索时，通常涉及多个检测算法，这些算法往往冗余，有时互补。本文旨在解决如何组合这些检测算法，以最大化此类利用多个分析管道的天体物理搜索在噪声与信号区分能力上的问题。我们采用机器学习方法，构建了一个基于神经网络的分类器，该分类器利用LIGO-Virgo-KAGRA实时（及离线）引力波数据处理中所有参与管道的信息（或信息缺失），用于搜索致密双星并合事件。我们使用模拟（天体物理）信号和来自仪器的真实噪声对该方法进行基准测试，并绘制接收者操作特征曲线以量化其性能。我们展示了这如何有效地产生一个新的组合管道，其性能优于任何单一管道或其他逻辑组合。这对于LIGO-Virgo-KAGRA探测器的实时操作尤为关键，因为这种检测方法的组合可以提高识别天体物理源的鲁棒性，并减少可能作为天文电报发出的虚假警报数量。我们的方法还旨在简化LIGO-Virgo-KAGRA探测器天体物理结果的呈现，通过整合所有参与天体物理搜索的管道，并优化更广泛的多信使天体物理社区在跟进引力波瞬变事件天文警报时的资源利用。

英文摘要

The search for gravitational-wave transients (modeled and unmodeled) as performed by the LIGO-Virgo-KAGRA collaborations and the broader community typically involves multiple detection algorithms, often redundant and sometimes complementary. We here address the problem of combining such detection algorithms in order to maximize the noise vs signal discrimination power of such an astrophysical search that utilizes multiple analysis pipelines. Using a machine learning approach, we construct a neural network-based classifier that utilizes information (or lack thereof) from all participating pipelines in the search for compact binary coalescences in LIGO-Virgo-KAGRA's realtime (and offline) processing of gravitational-wave data. We benchmark the method with simulated (astrophysical) signals and real noise from the instruments and obtain receiver operating characteristic curves to quantify its performance. We show how this leads to an effectively new, combined pipeline that outperforms any single pipeline alone or other logical combinations of them. This is particularly critical for real-time operations of the LIGO-Virgo-KAGRA detectors as such combination of detection methods can lead to increased robustness in identifying astrophysical sources and to reduction of the number of false alerts that may otherwise be sent out as astronomical telegrams. Our method also aims in simplifying the presentation of astrophysical results out of the LIGO-Virgo-KAGRA detectors by combining all participating pipelines in the astrophysical searches as well as optimizing resources by the broader multi-messenger astrophysics community in following up astronomical alerts for gravitational-wave transient events.

发表机构

• Aristotle University of Thessaloniki（塞萨洛尼基亚里士多德大学）
• Institute for Accelerated AI Algorithms for Data-Driven Discovery (A3D3), MIT（麻省理工学院数据驱动发现加速AI算法研究所）
• University of Thessaly（色萨利大学）

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