长短期记忆网络用于引力波早期预警探测
Long Short-Term Memory for Early Warning Detection of Gravitational Waves
AI总结：
本文提出基于长短期记忆网络的深度学习概念验证方法，用于双黑洞并合事件的并合前早期预警，在灵敏度和预警能力上与匹配滤波相当，可提前四秒发出警报。
摘要：
对致密双星并合事件早期旋进阶段的引力波进行并合前探测，将能够在电磁波段观测到并合的早期阶段。这将显著影响多信使天文学，为天文学家提供获取丰富新信息的潜在途径。在此，我们提出一种基于深度学习的概念验证方法，用于产生双黑洞系统的并合前早期预警警报。我们展示了使用在时域白化探测器应变上训练的长短期记忆网络来检测和分类致密双星事件的可能性。在本工作中，我们考虑单个先进激光干涉引力波天文台探测器在设计灵敏度下的运行，并与传统匹配滤波方法的近似进行灵敏度和早期预警能力的近似比较。我们发现我们的模型在这两方面都具有竞争力，并且当应用于模拟测试数据集时，能够在并合前最多四秒产生早期警报。
英文摘要：
The pre-merger detection of gravitational waves from the early inspiral phase of compact binary coalescence events would allow the observation of the earlier stages of the merger in the electromagnetic band. This would significantly impact multi-messenger astronomy, giving astronomers potential access to rich new information. Here, we introduce a proof-of-concept deep-learning-based approach to produce pre-merger early-warning alerts for binary black hole systems. We show the possibility of using a Long Short-Term Memory network trained on the whitened detector strain in the time domain to detect and classify compact binary events. In this work, we consider a single advanced Laser Interferometer Gravitational-Wave Observatory detector at design sensitivity and make approximate sensitivity and early warning capability comparisons with approximations to traditional matched filtering approaches. We find that our model is competitive in both aspects, and when applied to a simulated test dataset was able to produce an early alert up to four seconds before the merger.
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