AI 中文总结
研究KAGRA干涉仪中散射光噪声，提出f-cluster分析方法，通过提取时频图拱形特征周期性来估计散射物体振荡频率，应用于KAGRA数据识别出散射频率，发现其与地面运动相关性，有助于表征实际数据中散射光噪声及探寻噪声源。
AI 中文摘要
在引力波望远镜中，非平稳噪声会妨碍干涉仪稳定运行并限制引力波分析。本文聚焦于散射光噪声，它在短时间内跨宽频带波动，会模拟引力波信号、增加误报率或使参数估计结果产生偏差。为此提出一种名为f-cluster的新分析方法，该方法从时频图中提取拱形特征的周期性，检测功率谱图中过功率区域的出现时间和周期性来估计散射物体的振荡频率。将此方法应用于KAGRA 2025年4月1日至30日的数据，成功识别出散射频率$f_{sc}=0.432$Hz，还发现散射光噪声发生率与0.3 - 1.0Hz频段地面运动幅度有很强相关性，相关系数为0.82，表明可根据地震运动预测散射光噪声发生。该算法无需对散射物体做假设就能识别散射光噪声频率及其发生时间，有助于表征实际数据中的散射光噪声，为进一步研究散射光噪声源提供信息。
英文摘要
In a gravitational wave (GW) telescope, non-stationary noise can prevent stable interferometer operation and limit the GW analyses. Among the non-stationary noise, we focus on the scattered light noise. It fluctuates across a wide frequency band within a short time. Therefore, the scattered light noise can mimic the GW signal, increase the false alarm rate, or bias the parameter estimation results. In this work, we propose a new analysis method, named f-cluster, to characterize these features without prior assumptions about the scattering source. The scattered light noise shows characteristic arch-shaped features in time-frequency maps. Therefore, the algorithm extracts the periodicity of the arch shapes from the spectrogram. The algorithm detects the occurrence times and periodicity of excess-power regions in the spectrogram to estimate the oscillation frequency of the scattering object. To demonstrate the algorithm, we applied this method to KAGRA data from April 1 to 30, 2025, and successfully identified a scattering frequency of $f_{sc} = 0.432$ Hz. Furthermore, we found a strong correlation between the occurrence rate of the scattered light noise and the amplitude of ground motion in the $0.3-1.0$ Hz band with a Pearson correlation coefficient of 0.82. This correlation showed that the occurrence of scattered light noise can be forecast from the seismic motion. We confirmed that our algorithm identifies the frequency of the scattered light noise and its occurrence time, without making assumptions about the scattering object, thereby contributing to the characterization of the scattered light noise in real data. Our algorithm provides information that leads to further investigation into the noise hunting to specify the source of scattered light noise.
Comments23 pages and 12 figures
