We present a technique for translating a black-box machine-learned classifier operating on a high-dimensional input space into a small set of human-interpretable observables that can be combined to make the same classification decisions. We iteratively select these observables from a large space of high-level discriminants by finding those with the highest decision similarity relative to the black box, quantified via a metric we introduce that evaluates the relative ordering of pairs of inputs. Successive iterations focus only on the subset of input pairs that are misordered by the current set of observables. This method enables simplification of the machine-learning strategy, interpretation of the results in terms of well-understood physical concepts, validation of the physical model, and the potential for new insights into the nature of the problem itself. As a demonstration, we apply our approach to the benchmark task of jet classification in collider physics, where a convolutional neural network acting on calorimeter jet images outperforms a set of six well-known jet substructure observables. Our method maps the convolutional neural network into a set of observables called energy flow polynomials, and it closes the performance gap by identifying a class of observables with an interesting physical interpretation that has been previously overlooked in the jet substructure literature.

中文翻译：

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将机器学习的物理学映射到人类可读的空间

我们提出了一种将在高维输入空间上运行的黑匣子机器学习的分类器转换为人类可解释的可观察性的小集合的技术，可以将它们组合起来以做出相同的分类决策。我们通过查找相对于黑匣子具有最高决策相似度的决策者，从大范围的高阶判别器中迭代选择这些可观察变量，并通过我们引入的评估输入对相对顺序的度量对其进行量化。连续迭代仅关注被当前可观察对象集错序的输入对的子集。这种方法可以简化机器学习策略，以易于理解的物理概念解释结果，验证物理模型，以及对问题本身性质的新见解的潜力。作为演示，我们将我们的方法应用于对撞机物理学中的射流分类的基准任务，在该模型中，作用于量热计射流图像的卷积神经网络的性能优于一组六个著名的可观察到的射流亚结构。我们的方法将卷积神经网络映射到一组称为能流多项式的可观测量，并通过用有趣的物理解释识别一类可观测量，从而弥补了性能差距，而该有趣的物理解释先前在射流子结构文献中被忽略。在热量计射流图像上使用的卷积神经网络的性能优于一组六个可观察到的著名射流子结构。我们的方法将卷积神经网络映射到一组称为能流多项式的可观测量，并通过用有趣的物理解释识别一类可观测量，从而弥补了性能差距，而该有趣的物理解释先前在射流子结构文献中被忽略。在热量计射流图像上使用的卷积神经网络的性能优于一组六个可观察到的著名射流子结构。我们的方法将卷积神经网络映射到一组称为能流多项式的可观测量，并通过用有趣的物理解释识别一类可观测量，从而弥补了性能差距，而该有趣的物理解释先前在射流子结构文献中被忽略。