AI 中文总结
本研究针对IBUU输运模型开发符号回归模拟器，其重现质子集体流的精度与DNN相当，且预测速度更快，还可反向预测核子散射截面修正因子，有望用于输运模型的灵敏度与不确定性分析。
AI 中文摘要
符号回归提供了一种可解释的机器学习方法，用于构建物理输入与观测量之间的显式解析关系。本研究针对同位旋依赖的Boltzmann-Uehling-Uhlenbeck（IBUU）输运模型开发了符号回归模拟器，并将其性能与深度神经网络（DNN）模拟器进行对比。利用前期模拟器研究中使用的相同输运模型数据，结果显示符号回归可重现横向流v₁的质子快度中间斜率F₁与椭圆流v₂，精度与DNN相当，且训练后预测速度快得多，还能提供显式解析表达式。进一步展示了符号回归在反向方向的应用：构建从流观测量预测介质内核子-核子散射截面修正因子X的解析关系。尽管符号回归模型的训练时间远长于DNN，且运行间差异更大，但其解析形式与快速评估的特性，使其有望成为未来输运模型灵敏度与不确定性分析的工具。
英文摘要
Symbolic regression provides an interpretable machine-learning approach for constructing explicit analytic relations between physical inputs and observables. In this work, we develop symbolic-regression emulators for the isospin-dependent Boltzmann-Uehling-Uhlenbeck (IBUU) transport model and compare their performance with deep neural network (DNN) emulators. Using the same transport-model data employed in our previous emulator studies, we show that symbolic regression can reproduce the proton mid-rapidity slope $F_1$ of transverse flow $v_1$ and elliptic flow $v_2$ with accuracy comparable to that of DNNs, while providing explicit analytic expressions and substantially faster prediction once trained. We further demonstrate the use of symbolic regression in the reverse direction by constructing analytic relations that predict the in-medium nucleon-nucleon cross-section modification factor $X$ from the flow observables. Although the symbolic-regression models require substantially longer training times and exhibit greater run-to-run variation than DNNs, their analytic form and rapid evaluation make them promising tools for future transport-model sensitivity and uncertainty analyses.
Comments12 pages including 4 figures
