用视觉变压器提升引力波数据中瞬时噪声分类准确率
Vision Transformer for Transient Noise Classification
- 采用预训练视觉变压器模型处理引力波数据中的噪声图像
- 在24类噪声分类任务中达到92.26%的分类效率
- 适合从事引力波信号处理与机器学习交叉研究者参考
LIGO 数据中的瞬时噪声（毛刺）会干扰引力波探测。Gravity Spy 项目已对这些噪声事件进行了分类。随着 O3 运行引入两类新噪声，需训练新模型以实现有效分类。本文使用视觉变压器（ViT）模型，对包含前一轮22个类别及O3a新增两类噪声的混合数据集进行训练。基于预训练的 ViT-B/32 模型，在24类噪声分类任务中实现了92.26%的分类效率，证明了视觉变压器在提升引力波探测准确性方面的潜力，能有效区分瞬时噪声。
原文摘要 · Abstract (English)
Transient noise (glitches) in LIGO data hinders the detection of gravitational waves (GW). The Gravity Spy project has categorized these noise events into various classes. With the O3 run, there is the inclusion of two additional noise classes and thus a need to train new models for effective classification. We aim to classify glitches in LIGO data into 22 existing classes from the first run plus 2 additional noise classes from O3a using the Vision Transformer (ViT) model. We train a pre-trained Vision Transformer (ViT-B/32) model on a combined dataset consisting of the Gravity Spy dataset with the additional two classes from the LIGO O3a run. We achieve a classification efficiency of 92.26%, demonstrating the potential of Vision Transformer to improve the accuracy of gravitational wave detection by effectively distinguishing transient noise. Key words: gravitational waves --vision transformer --machine learning
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