用平滑方法揭示机器学习在粒子物理中依赖的能标，让模型决策更可解释。
A Step Toward Interpretability: Smearing the Likelihood
- 通过输入事件间能量距离平滑，将离散数据转化为连续空间。
- 发现判别性能随分辨率降低而提升，表明模型对全尺度辐射敏感。
- 适合关注模型可解释性与高能物理特征提取的研究者。
机器学习在粒子物理中的可解释性问题尚无统一定义和解决方案。本文提出首个初步步骤：定义并实现一种方法，用于识别机器学习模型所利用的相关物理能量尺度。该方法通过对指定能量距离内的所有输入事件进行平滑或平均，使有限离散数据集上的任意量在数据空间上呈现连续性。在此框架下，我们明确证明（近似）标度律是极端值理论应用于机器在有限数据集上必须外推的最小不可约距离分布的结果。以夸克与胶子喷注识别为例，构建平滑似然函数，结果显示判别能力随分辨率下降而持续增强，表明真实似然对所有能标的发射均敏感。
原文摘要 · Abstract (English)
The problem of interpretability of machine learning architecture in particle physics has no agreed-upon definition, much less any proposed solution. We present a first modest step toward these goals by proposing a definition and corresponding practical method for isolation and identification of relevant physical energy scales exploited by the machine. This is accomplished by smearing or averaging over all input events that lie within a prescribed metric energy distance of one another and correspondingly renders any quantity measured on a finite, discrete dataset continuous over the dataspace. Within this approach, we are able to explicitly demonstrate that (approximate) scaling laws are a consequence of extreme value theory applied to analysis of the distribution of the irreducible minimal distance over which a machine must extrapolate given a finite dataset. As an example, we study quark versus gluon jet identification, construct the smeared likelihood, and show that discrimination power steadily increases as resolution decreases, indicating that the true likelihood for the problem is sensitive to emissions at all scales.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。