The Landscape of Unfolding with Machine Learning

arXiv:2404.18807v1 Announce Type: cross
Abstract: Recent innovations from machine learning allow for data unfolding, without binning and including correlations across many dimensions. We describe a set of known, upgraded, and new methods for ML-based unfolding. The performance of these approaches are evaluated on the same two datasets. We find that all techniques are capable of accurately reproducing the particle-level spectra across complex observables. Given that these approaches are conceptually diverse, they offer an exciting toolkit for a new class of …

abstract arxiv correlations cs.lg data datasets dimensions hep-ex hep-ph innovations landscape machine machine learning performance set type