The measurements performed by particle physics experiments must account for the imperfect response of the detectors used to observe the interactions. One approach, unfolding, statistically adjusts the experimental data for detector effects. Recently, generative machine learning models have shown promise for performing unbinned unfolding in a high number of dimensions. However, all current generative approaches are limited to unfolding a fixed set of observables, making them unable to perform full-event unfolding in the variable dimensional environment of collider data. A novel modification to the variational latent diffusion model (VLD) approach to generative unfolding is presented, which allows for unfolding of high- and variable-dimensional feature spaces. The performance of this method is evaluated in the context of semi-leptonic top quark pair production at the Large Hadron Collider.

翻译：粒子物理实验的测量必须考虑用于观测相互作用的探测器的不完美响应。展开方法通过统计手段修正探测器效应对实验数据的影响。近年来，生成式机器学习模型在高维无区间展开中展现出潜力。然而，当前所有生成式方法均局限于展开固定观测变量集合，无法在碰撞数据的变维环境下执行全事件展开。本文提出了一种对生成式展开中变分潜在扩散模型（VLD）的创新改进，实现了高维及变维特征空间的展开。该方法在大型强子对撞机半轻子型顶夸克对产生场景中进行了性能评估。