用于聚类KAGRA O3GK瞬态噪声数据的无监督深度学习方法
Unsupervised Deep Learning Method for Clustering KAGRA O3GK Transient Noise Data
AI总结：
本研究针对引力波探测器瞬态噪声分类的监督方法成本高、扩展性差的问题，采用无监督深度学习方法对KAGRA O3GK运行期的瞬态噪声频谱图进行降维聚类，验证了该方法的有效性及应用潜力。
AI中文摘要：
地基引力波探测器的出现大幅提升了微弱引力波信号的探测能力，但这类探测器受各类瞬态噪声（称为“ glitch”）影响，这类噪声会模拟真实引力波信号，限制探测器灵敏度。根据 glitch 的时频特征对其分类，不仅有助于更深入理解其起源，还支持对数据进行降噪以维持数据质量。尽管监督机器学习方法常用于 glitch 分类，但这类方法需要通过手动标注获取带标签的训练数据集，该过程成本高昂，且不适用于不断升级的探测器。为解决这一挑战，本研究探究用于 glitch 频谱图图像降维和聚类的无监督深度学习方法。本研究采用三种不同方法，并利用 KAGRA 探测器 O3GK 运行期的数据对其进行对比。研究结果表明，与传统机器学习方法相比，基于深度学习的特征提取可显著提升聚类性能。本研究呈现了利用无监督深度学习对 KAGRA glitch 数据集的初步分析，凸显该方法在未来观测运行中实现高效、可扩展的 glitch 分类的潜力。
英文摘要：
The advent of ground-based gravitational wave detectors has significantly improved the detection of faint gravitational wave signals. However, these detectors are affected by various types of transient noise, known as glitches, which can mimic true gravitational wave signals and limit detector sensitivity. Classifying glitches according to their time-frequency characteristics not only facilitates a deeper understanding of their origins, but also supports their mitigation on data to maintain data quality. While supervised machine learning methods are commonly employed for glitch classification, they require the labelling of training datasets obtained through manual annotation, a process which is costly and not scalable for evolving detectors. In order to address this challenge, the present study investigates unsupervised deep learning methods for dimensionality reduction and clustering of glitch spectrogram images. In this study, three distinct approaches are applied and compared using data from the KAGRA detector's O3GK run. The findings of this study demonstrate that deep learning-based feature extraction significantly enhances the clustering performance compared to traditional machine learning methods. This study presents an initial analysis of the KAGRA glitch dataset using unsupervised deep learning, highlighting the potential of this approach for efficient and scalable glitch classification in future observation runs.
