The Landscape of Unfolding with Machine Learning
High Energy Physics - Phenomenology
2025-02-26 v2 Machine Learning
High Energy Physics - Experiment
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 measurements that can probe the Standard Model with an unprecedented level of detail and may enable sensitivity to new phenomena.
Cite
@article{arxiv.2404.18807,
title = {The Landscape of Unfolding with Machine Learning},
author = {Nathan Huetsch and Javier Mariño Villadamigo and Alexander Shmakov and Sascha Diefenbacher and Vinicius Mikuni and Theo Heimel and Michael Fenton and Kevin Greif and Benjamin Nachman and Daniel Whiteson and Anja Butter and Tilman Plehn},
journal= {arXiv preprint arXiv:2404.18807},
year = {2025}
}