DeepSNR：离线引力波探测的深度学习基础
DeepSNR: A deep learning foundation for offline gravitational wave detection
摘要：
迄今为止，所有关于引力波发现的科学声明都依赖于对候选观测结果的离线统计分析，以量化相对于背景过程的重要性。LIGO实验中这种离线检测管道的当前基础是匹配滤波器算法，该算法产生基于信噪比的统计数据，用于对候选观测结果进行排名。现有的基于深度学习的探测引力波的尝试在信号灵敏度和计算效率方面都显示出了希望，输出概率得分。然而，概率分数并不容易集成到发现工作流中，这将深度学习的使用限制在非发现导向的应用程序中。本文介绍了深度学习信噪比（DeepSNR）检测流水线，该流水线使用一种新方法从深度学习分类器生成信噪比排名统计，为在面向发现的流水线中使用深度学习算法提供了第一个基础。DeepSNR的性能通过识别二进制黑洞合并候选者与来自第一次观测运行的开放LIGO数据中的噪声源来证明。LIGO探测器响应的高保真度模拟被用来呈现深度学习模型在物理观测量方面的第一个灵敏度估计。还研究了DeepSNR在各种实验考虑下的鲁棒性。这些结果为DeepSNR在更广泛的背景下用于引力波和罕见信号的科学发现铺平了道路，可能能够检测到更微弱的信号和以前从未观察到的现象。
英文摘要：
All scientific claims of gravitational wave discovery to date rely on the offline statistical analysis of candidate observations in order to quantify significance relative to background processes. The current foundation in such offline detection pipelines in experiments at LIGO is the matched-filter algorithm, which produces a signal-to-noise-ratio-based statistic for ranking candidate observations. Existing deep-learning-based attempts to detect gravitational waves, which have shown promise in both signal sensitivity and computational efficiency, output probability scores. However, probability scores are not easily integrated into discovery workflows, limiting the use of deep learning thus far to non-discovery-oriented applications. In this paper, the Deep Learning Signal-to-Noise Ratio (DeepSNR) detection pipeline, which uses a novel method for generating a signal-to-noise ratio ranking statistic from deep learning classifiers, is introduced, providing the first foundation for the use of deep learning algorithms in discovery-oriented pipelines. The performance of DeepSNR is demonstrated by identifying binary black hole merger candidates versus noise sources in open LIGO data from the first observation run. High-fidelity simulations of the LIGO detector responses are used to present the first sensitivity estimates of deep learning models in terms of physical observables. The robustness of DeepSNR under various experimental considerations is also investigated. The results pave the way for DeepSNR to be used in the scientific discovery of gravitational waves and rare signals in broader contexts, potentially enabling the detection of fainter signals and never-before-observed phenomena.
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