新たな診断ツールがLIGOの重力波探索を支援 (New diagnostic tool will help LIGO hunt gravitational waves)

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2025-01-30 カリフォルニア大学リバーサイド校 (UCR)

カリフォルニア大学リバーサイド校(UCリバーサイド)の研究者たちは、LIGO(レーザー干渉計重力波観測所)のデータ解析を支援する新しい診断ツールを開発しました。このツールは、教師なし機械学習アプローチを用いて、LIGOの補助チャンネルデータ内の新たなパターンを自動的に検出します。これにより、環境ノイズの影響を特定し、検出器の感度向上に役立てることが可能となります。この技術は、粒子加速器実験や大規模な産業システムにも応用できる潜在性を持っています。

<関連情報>

地上型重力波検出器の環境状態特性評価のための多変量時系列クラスタリング Multivariate Time Series Clustering for Environmental State Characterization of Ground-Based Gravitational-Wave Detectors

Rutuja Gurav, Isaac Kelly, Pooyan Goodarzi, Anamaria Effler, Barry Barish, Evangelos Papalexakis, Jonathan Richardson
arXiv  Submitted on 13 Dec 2024
DOI:https://doi.org/10.48550/arXiv.2412.09832

新たな診断ツールがLIGOの重力波探索を支援 (New diagnostic tool will help LIGO hunt gravitational waves)

Abstract

Gravitational-wave observatories like LIGO are large-scale, terrestrial instruments housed in infrastructure that spans a multi-kilometer geographic area and which must be actively controlled to maintain operational stability for long observation periods. Despite exquisite seismic isolation, they remain susceptible to seismic noise and other terrestrial disturbances that can couple undesirable vibrations into the instrumental infrastructure, potentially leading to control instabilities or noise artifacts in the detector output. It is, therefore, critical to characterize the seismic state of these observatories to identify a set of temporal patterns that can inform the detector operators in day-to-day monitoring and diagnostics. On a day-to-day basis, the operators monitor several seismically relevant data streams to diagnose operational instabilities and sources of noise using some simple empirically-determined thresholds. It can be untenable for a human operator to monitor multiple data streams in this manual fashion and thus a distillation of these data-streams into a more human-friendly format is sought. In this paper, we present an end-to-end machine learning pipeline for features-based multivariate time series clustering to achieve this goal and to provide actionable insights to the detector operators by correlating found clusters with events of interest in the detector.

1701物理及び化学
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