用神经网络搜寻引力波数据中的未知短信号，发现三例已知事件和多种干扰
A Neural Network-Based Search for Unmodeled Transients in LIGO-Virgo-KAGRA's Third Observing Run
- 构建低维嵌入空间捕捉信号物理特征
- 成功检出3个已知双星并合事件及多种探测器噪声
- 无需预设信号方向、极化或形态，适合未知信号搜索
本文报告了基于神经网络的搜索方法在LIGO-Virgo-KAGRA第三观测运行数据中寻找毫秒至几秒级短时引力波瞬变的结果。该方法针对30-1500 Hz频段内未建模的瞬变信号，不假设信号来向、极化或形态。采用引力波异常知识（GWAK）方法，成功检测到三个由现有流水线识别的紧凑双星并合事件（CBCs），以及一系列探测器异常。算法通过构建低维嵌入空间，有效捕捉信号的物理特征，实现对CBCs、探测器噪声及未建模瞬变的联合检测。研究证明，GWAK可突破现有流水线的探测极限，为未来引力波探测策略奠定基础。
原文摘要 · Abstract (English)
This paper presents the results of a Neural Network (NN)-based search for short-duration gravitational-wave transients in data from the third observing run of LIGO, Virgo, and KAGRA. The search targets unmodeled transients with durations of milliseconds to a few seconds in the 30-1500 Hz frequency band, without assumptions about the incoming signal direction, polarization, or morphology. Using the Gravitational Wave Anomalous Knowledge (GWAK) method, three compact binary coalescences (CBCs) identified by existing pipelines are successfully detected, along with a range of detector glitches. The algorithm constructs a low-dimensional embedded space to capture the physical features of signals, enabling the detection of CBCs, detector glitches, and unmodeled transients. This study demonstrates GWAK's ability to enhance gravitational-wave searches beyond the limits of existing pipelines, laying the groundwork for future detection strategies.
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