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。反卷积方法通过统计调整实验数据以消除探测器效应的影响。近年来，生成式机器学习模型在高维无区间反卷积中展现出潜力。然而，当前所有生成式方法仅限于对固定观测集进行反卷积，无法在碰撞数据的可变维度环境中实现全事例反卷积。本文提出一种对变分隐变量扩散模型（VLD）生成式反卷积方法的新颖改进，使其能够处理高维和可变维度的特征空间反卷积。该方法在大型强子对撞机上半轻子型顶夸克对产生过程的背景下进行了性能评估。