天基引力波探测器数据中暂现信号的自监督重建

中文摘要 AI 辅助

天基引力波（GW）数据可能包含波形形态事先未知的暂现信号。提取这些信号对于表征新源和缓解仪器异常具有重要意义。然而，现有的基于深度神经网络（DNN）的提取方法依赖于干净的训练目标和特定波形类别的样本，这可能在暂现形态未事先指定时限制其适用性。本研究开发了一种受Noise2Noise（N2N）启发的自监督框架，该框架从含噪观测中学习，无需干净的训练目标，且在推理时不需要暂现特定的波形模板。一个在含噪的大质量黑洞双星（MBHB）观测上训练的单一模型提供了高重叠度的MBHB恢复，并在源混淆的测试数据中恢复了仪器毛刺和诸如宇宙弦爆发等其他引力波暂现信号的主要形态。除波形恢复外，当独立信息将候选暂现信号识别为仪器信号时，其提取的波形可在无需异常特定模板的情况下从数据中减去。在模拟的连续数据流中，该过程显著抑制了毛刺主导频带内注入异常的功率。这些结果支持使用自监督提取对形态未知的候选暂现信号进行初步波形估计，从而实现后续表征，并在适当情况下对仪器异常进行条件性扣除。

英文摘要

Space-based gravitational-wave (GW) data may contain transient signals whose waveform morphologies are not known in advance. Extracting these signals is important for characterizing new sources and mitigating instrumental anomalies. However, existing deep neural network (DNN)-based extraction approaches rely on clean training targets and waveform-class-specific examples, which may limit their applicability when the transient morphology is not specified in advance. This work develops a Noise2Noise (N2N)-inspired self-supervised framework that learns from noisy observations without clean training targets and requires no transient-specific waveform templates at inference. A single model trained on noisy massive black-hole binary (MBHB) observations provides high-overlap MBHB recovery and recovers the dominant morphologies of instrumental glitches and other GW transient signals such as cosmic string bursts in source-confused test data. Beyond waveform recovery, when independent information identifies a candidate transient as instrumental, its extracted waveform can be subtracted from the data without an anomaly-specific template. In a simulated continuous data stream, this procedure substantially suppresses the injected-anomaly power within the glitch-dominated frequency band. These results support the use of self-supervised extraction for initial waveform estimation of candidate transients with unknown morphologies, enabling subsequent characterization and, where appropriate, conditional subtraction of instrumental anomalies.

发表机构

• Center for Gravitational Wave Experiment, National Microgravity Laboratory, Institute of Mechanics, Chinese Academy of Sciences（中国科学院力学研究所微重力实验室引力波实验中心）
• Taiji Laboratory for Gravitational Wave Universe (Beijing/Hangzhou), University of Chinese Academy of Sciences (UCAS)（中国科学院大学太极引力波宇宙实验室（北京/杭州））

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