Full Event Particle-Level Unfolding with Variable-Length Latent Variational Diffusion

arXiv:2404.14332v1 Announce Type: cross
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 …

abstract arxiv cs.ai cs.lg data detectors diffusion effects event experimental generative hep-ex hep-ph interactions machine machine learning machine learning models observe particle particle physics physics type