用符号回归从实验数据直接推导出可解释的强子化函数模型。
Inferring Interpretable Models of Fragmentation Functions using Symbolic Regression
- 通过符号回归从实验数据中自动学习强子化函数的解析形式。
- 学到的函数与吕恩字符串模型相似，能很好拟合带电强子多重数数据。
- 为高能物理中的非微扰过程提供可解释的机器学习建模新路径。
机器学习正快速进入自然科学领域，包括高能物理。本文首次直接从实验数据中推断出强子化函数（Fragmentation Functions, FFs）的函数形式。这类函数是描述涉及强子产生的高能物理过程中的物理可观测量的关键要素，可预测不同能量下的结果值。由于理论无法精确计算强子化函数，传统方法依赖于基于现象学模型预设函数形式的全局数据拟合来学习参数。本文采用一种机器学习技术——符号回归，从测量的带电强子多重数数据中学习解析模型。所获得的函数形式与吕恩字符串函数（Lund string model）相似，且对数据具有良好的拟合效果，因此可作为全球强子化函数拟合的潜在候选模型。该研究为量子色动力学相关现象学研究提供了新的方法范式，并可推广至更广泛的科学领域。
原文摘要 · Abstract (English)
Machine learning is rapidly making its path into natural sciences, including high-energy physics. We present the first study that infers, directly from experimental data, a functional form of fragmentation functions. The latter represent a key ingredient to describe physical observables measured in high-energy physics processes that involve hadron production, and predict their values at different energy. Fragmentation functions can not be calculated in theory and have to be determined instead from data. Traditional approaches rely on global fits of experimental data using a pre-assumed functional form inspired from phenomenological models to learn its parameters. This novel approach uses a ML technique, namely symbolic regression, to learn an analytical model from measured charged hadron multiplicities. The function learned by symbolic regression resembles the Lund string function and describes the data well, thus representing a potential candidate for use in global FFs fits. This study represents an approach to follow in such QCD-related phenomenology studies and more generally in sciences.
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