用注意力机制提升引力波参数估计的可靠性
Enhancing the reliability of machine learning for gravitational wave parameter estimation with attention-based models
- 基于视觉Transformer构建双模型，从频谱图估计有效自旋和啁啾质量
- 发现模型对异常信号的关注度越高，参数估计偏差越大
- 通过注意力热图可判断结果是否可靠，适合物理可信性研究者
我们提出一种提升机器学习在引力波参数估计中可靠性的方法。基于视觉Transformer构建两个独立模型，从双黑洞并合引力波信号的频谱图中估计有效自旋与啁啾质量。为增强可靠性，利用注意力图可视化模型预测时关注的区域，证明两模型均依赖物理上有意义的信息进行估计。进一步地，通过注意力图量化异常事件对参数估计的影响：当模型更多关注异常信号时，参数估计偏差显著增强。这表明注意力图可用于判断机器学习结果的可信程度。
原文摘要 · Abstract (English)
We introduce a technique to enhance the reliability of gravitational wave parameter estimation results produced by machine learning. We develop two independent machine learning models based on the Vision Transformer to estimate effective spin and chirp mass from spectrograms of gravitational wave signals from binary black hole mergers. To enhance the reliability of these models, we utilize attention maps to visualize the areas our models focus on when making predictions. This approach enables demonstrating that both models perform parameter estimation based on physically meaningful information. Furthermore, by leveraging these attention maps, we demonstrate a method to quantify the impact of glitches on parameter estimation. We show that as the models focus more on glitches, the parameter estimation results become more strongly biased. This suggests that attention maps could potentially be used to distinguish between cases where the results produced by the machine learning model are reliable and cases where they are not.
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