论文标题
可解释的高能量物理AI
Explainable AI for High Energy Physics
论文作者
论文摘要
神经网络在高能量物理学研究中无处不在。但是，这些高度非线性的参数化函数被视为\ textit {black box} - 其内部运作以传达信息并构建所需的输入输出关系通常是棘手的。可解释的AI（XAI）方法可用于确定神经模型与数据的关系，以通过在输入与模型输出之间建立定量可行的关系来使其与数据\ textIt {bughtial {ablesable}。在这封信中，我们探索了在高能量物理学问题的背景下使用XAI方法的潜力。
Neural Networks are ubiquitous in high energy physics research. However, these highly nonlinear parameterized functions are treated as \textit{black boxes}- whose inner workings to convey information and build the desired input-output relationship are often intractable. Explainable AI (xAI) methods can be useful in determining a neural model's relationship with data toward making it \textit{interpretable} by establishing a quantitative and tractable relationship between the input and the model's output. In this letter of interest, we explore the potential of using xAI methods in the context of problems in high energy physics.
