The Landscape of Unfolding with Machine Learning
<|reference_start|>The Landscape of Unfolding with Machine Learning: 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.<|reference_end|>
@article{huetsch2024the,
    title={The Landscape of Unfolding with Machine Learning},
    author={Nathan Huetsch, Javier Mari~no Villadamigo, Alexander Shmakov, Sascha
  Diefenbacher, Vinicius Mikuni, Theo Heimel, Michael Fenton, Kevin Greif,
  Benjamin Nachman, Daniel Whiteson, Anja Butter, Tilman Plehn},
    journal={arXiv preprint arXiv:2404.18807},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.18807},
    primaryClass={hep-ph cs.LG hep-ex}
}