用变分自编码器从引力波数据中无监督检测异常信号
Unsupervised Learning Approach to Anomaly Detection in Gravitational Wave Data
- 用仅含噪声的数据训练变分自编码器，异常信号无法被准确重构
- 在LIGO数据上检测到引力波事件，AUC达0.89
- 适合寻找未知物理现象，无需标注数据
引力波（GW）是爱因斯坦广义相对论预言的天体物理现象，为研究宇宙和基础物理提供了强大工具。本文提出一种基于变分自编码器（VAEs）的无监督异常检测方法，用于分析引力波时间序列数据。通过仅使用噪声数据进行训练，该模型能准确重构噪声，但对异常（如引力波信号）重构失败，导致重建误差出现可测量的突增。该方法应用于利文斯顿（LIGO H1）和汉福德（LIGO L1）探测器的数据，测试集包含噪声与引力波事件，结果表明检测性能可靠，曲线下面积（AUC）达到0.89。本研究展示了VAE作为无监督方法在引力波数据异常检测中的鲁棒性，提供了一个可扩展框架，可用于识别已知及潜在的新物理现象。
原文摘要 · Abstract (English)
Gravitational waves (GW), predicted by Einstein's General Theory of Relativity, provide a powerful probe of astrophysical phenomena and fundamental physics. In this work, we propose an unsupervised anomaly detection method using variational autoencoders (VAEs) to analyze GW time-series data. By training on noise-only data, the VAE accurately reconstructs noise inputs while failing to reconstruct anomalies, such as GW signals, which results in measurable spikes in the reconstruction error. The method was applied to data from the LIGO H1 and L1 detectors. Evaluation on testing datasets containing both noise and GW events demonstrated reliable detection, achieving an area under the ROC curve (AUC) of 0.89. This study introduces VAEs as a robust, unsupervised approach for identifying anomalies in GW data, which offers a scalable framework for detecting known and potentially new phenomena in physics.
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