用迁移学习提升引力波探测对噪声变化的适应能力
Transfer Learning Adapts to Changing PSD in Gravitational Wave Data
- 通过简化模型+迁移学习策略，应对复杂噪声挑战
- 在非白噪声下准确率达99%以上，可快速适应噪声波动
- 适合实时引力波监测与动态噪声环境下的应用
引力波探测为研究极端能量条件下的物理规律提供了前所未有的机遇，但来自Advanced LIGO和Virgo等观测台的引力波数据存在显著噪声，传统降噪方法难以有效处理非高斯效应，尤其是短时内噪声功率谱密度（PSD）的波动。此前的AI方法虽有突破，却面临可扩展性差、可靠性低及梯度消失等问题。本文提出一种简化架构结合新型训练策略，利用迁移学习实现快速适应新噪声谱。实验表明，该模型在非白噪声场景下检测准确率超过99%，仅需数个训练轮次即可适配新的噪声条件，适用于动态变化的噪声环境中的实时应用。
原文摘要 · Abstract (English)
The detection of gravitational waves has opened unparalleled opportunities for observing the universe, particularly through the study of black hole inspirals. These events serve as unique laboratories to explore the laws of physics under conditions of extreme energies. However, significant noise in gravitational wave (GW) data from observatories such as Advanced LIGO and Virgo poses major challenges in signal identification. Traditional noise suppression methods often fall short in fully addressing the non-Gaussian effects in the data, including the fluctuations in noise power spectral density (PSD) over short time intervals. These challenges have led to the exploration of an AI approach that, while overcoming previous obstacles, introduced its own challenges, such as scalability, reliability issues, and the vanishing gradient problem. Our approach addresses these issues through a simplified architecture. To compensate for the potential limitations of a simpler model, we have developed a novel training methodology that enables it to accurately detect gravitational waves amidst highly complex noise. Employing this strategy, our model achieves over 99% accuracy in non-white noise scenarios and shows remarkable adaptability to changing noise PSD conditions. By leveraging the principles of transfer learning, our model quickly adapts to new noise profiles with just a few epochs of fine-tuning, facilitating real-time applications in dynamically changing noise environments.
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