English(EN) Automatic classification pipeline for glitches in the Virgo detector

Virgo探测器使用AI管道对引力波故障进行分类

作者 PulseAugur 编辑部 · [1 个来源] · 2026-08-24 04:00

研究人员开发了VIGILant，一个自动分类和可视化Virgo引力波探测器中故障的管道。该系统同时采用了基于树的机器学习模型和ResNet34卷积神经网络，其中ResNet34取得了0.9772的高F1分数和0.9833的准确率。VIGILant自O4c观测运行以来已部署在Virgo现场供日常使用，提供了一个交互式仪表板来监控故障群体和探测器行为，有助于识别需要进一步关注的低置信度预测。 AI

影响通过自动化引力波天文台的故障检测和分类，增强了科学数据分析。

排序理由该条目描述了一篇研究论文，其中详细介绍了一种用于科学仪器的新的机器学习管道。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

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报道来源 [1]

arXiv cs.LG TIER_1 English(EN) · Tiago Fernandes, Francesco Di Renzo, Antonio Onofre, Alejandro Torres-Forn\'e, Jos\'e A. Font · 2026-08-24 04:00

Virgo探测器故障的自动分类流水线

arXiv:2604.13687v2 Announce Type: replace-cross Abstract: 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 visualiz…

本文如何被排名

报道来源 [1]

Virgo探测器故障的自动分类流水线

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