用分布映射提升生成式反卷积精度，实现高保真粒子物理数据重建。
Generative Unfolding with Distribution Mapping
- 基于薛定谔桥与直接扩散模型，构建条件概率准确的生成式展开方法
- 在单喷注子结构和22维相空间数据上达到顶尖反卷积精度
- 适合高能物理实验中需精确重构粒子谱的场景
机器学习使无需分箱的高微分截面测量成为可能。近期方法利用生成模型将初始模拟变换为反卷积数据。本文扩展了两种变形技术——薛定谔桥与直接扩散，确保模型学习到正确的条件概率分布。这使得分布映射方法的精度达到当前最优条件生成反卷积水平。数值结果基于标准单喷注子结构基准数据集及一个描述Z+2喷注22维相空间的新数据集。
原文摘要 · Abstract (English)
Machine learning enables unbinned, highly-differential cross section measurements. A recent idea uses generative models to morph a starting simulation into the unfolded data. We show how to extend two morphing techniques, Schrödinger Bridges and Direct Diffusion, in order to ensure that the models learn the correct conditional probabilities. This brings distribution mapping to a similar level of accuracy as the state-of-the-art conditional generative unfolding methods. Numerical results are presented with a standard benchmark dataset of single jet substructure as well as for a new dataset describing a 22-dimensional phase space of Z + 2-jets.
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