用深度学习自动识别引力波探测器中的异常信号，提升数据质量。
Automatic classification pipeline for glitches in the Virgo detector
- 结合结构化参数与声谱图，用树模型和卷积网络分类噪声
- ResNet34模型在测试集上达F1 0.9772，每条信号推理仅数十毫秒
- 已部署至维拉观测运行，支持实时监控与人工复核
引力波探测器中的异常信号频繁污染数据，干扰天体信号的观测与分析。本文提出VIGILant自动分类与可视化管道，针对维拉探测器O3b段的异常信号数据集，评估了两种机器学习方法：基于结构化Omicron参数的树模型（决策树、随机森林、XGBoost）与基于声谱图训练的卷积神经网络（ResNet）。尽管树模型具有更高可解释性且训练快速，但ResNet34表现更优，在测试集上达到F1分数0.9772与准确率0.9833，单次推理时间仅数十毫秒。该管道自观测运行O4c起已在维拉现场部署，为合作团队提供交互式仪表盘，用于监控异常信号群体与探测器状态，识别低置信度预测，提示需人工关注的异常事件。
原文摘要 · Abstract (English)
Glitches frequently contaminate data in gravitational-wave detectors, complicating the observation and analysis of astrophysical signals. This work introduces VIGILant, an automatic pipeline for classification and visualization of glitches in the Virgo detector. Using a curated dataset of Virgo O3b glitches, two machine learning approaches are evaluated: tree-based models (Decision Tree, Random Forest and XGBoost) using structured Omicron parameters, and Convolutional Neural Networks (ResNet) trained on spectrogram images. While tree-based models offer higher interpretability and fast training, the ResNet34 model achieved superior performance, reaching a F1 score of 0.9772 and accuracy of 0.9833 in the testing set, with inference times of tens of milliseconds per glitch. The pipeline has been deployed for daily operation at the Virgo site since observing run O4c, providing the Virgo collaboration with an interactive dashboard to monitor glitch populations and detector behavior. This allows to identify low-confidence predictions, highlighting glitches requiring further attention.
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