标题： 解释用于探测引力波的机器学习模型 显示英文标题

标题： Interpreting a Machine Learning Model for Detecting Gravitational Waves

摘要： 我们描述了一项转化研究的案例研究，将为计算机视觉开发的可解释性技术应用于用于搜索和发现引力波的机器学习模型。 我们研究的模型经过训练，可以检测非高斯和非平稳高级激光干涉引力波天文台（LIGO）数据中的黑洞合并事件。 我们生成了机器学习模型处理包含真实引力波信号、噪声异常和纯高级LIGO噪声的高级LIGO数据时的响应可视化。 我们的研究结果揭示了这些机器学习模型中单个神经元的响应。 进一步分析表明，网络的不同部分似乎在局部特征与全局特征上有所专门化，并且这种差异似乎源于网络的分支结构以及LIGO探测器的噪声特性。 我们认为努力使这些“黑箱”模型透明化可以为未来的研究提供途径，并有助于设计用于引力波天文学的可解释机器学习模型。 显示英文摘要

摘要： We describe a case study of translational research, applying interpretability techniques developed for computer vision to machine learning models used to search for and find gravitational waves. The models we study are trained to detect black hole merger events in non-Gaussian and non-stationary advanced Laser Interferometer Gravitational-wave Observatory (LIGO) data. We produced visualizations of the response of machine learning models when they process advanced LIGO data that contains real gravitational wave signals, noise anomalies, and pure advanced LIGO noise. Our findings shed light on the responses of individual neurons in these machine learning models. Further analysis suggests that different parts of the network appear to specialize in local versus global features, and that this difference appears to be rooted in the branched architecture of the network as well as noise characteristics of the LIGO detectors. We believe efforts to whiten these "black box" models can suggest future avenues for research and help inform the design of interpretable machine learning models for gravitational wave astrophysics.