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.

粒子物理实验中的测量必须考虑用于观测相互作用的探测器的不完善响应。最近，生成式机器学习模型在高维度中执行非直方图展开展现出了潜力。然而，目前所有生成方法限制于展开一组固定的可观察性，使它们无法在变量维度的碰撞数据环境中进行全事件展开。文章介绍了一种对生成展开中变量维度特征空间展开的新颖改进方法。该方法在大型强子对撞机上评估了在半轻子顶夸克对产生方面的性能。