Abstract
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.
Abstract (translated)
粒子物理实验中进行的测量必须考虑到用于观测相互作用的探测器的不完美响应。一种方法，展开统计地调整探测器效应的实验数据。最近，用于高维度进行无带展开的生成机器学习模型显示出在变维数据中进行无带展开的潜力。然而，目前的所有生成方法都局限于展开固定的一组观测量，使它们无法在多维碰撞数据中进行完整的局域展开。在变分自编码器模型（VLD）的生成展开方法中，提出了一种新的修改方法，允许对高维度和变维特征空间进行展开。该方法在大型强子对撞机（LHC）中半衰期裂变产物的生产环境中进行了评估。
URL
https://arxiv.org/abs/2404.14332
