Most measurements in particle and nuclear physics use matrix-based unfolding algorithms to correct for detector effects. In nearly all cases, the observable is defined analogously at the particle and detector level. We point out that while the particle-level observable needs to be physically motivated to link with theory, the detector-level need not be and can be optimized. We show that using deep learning to define detector-level observables has the capability to improve the measurement when combined with standard unfolding methods.
@article{arxiv.2203.16722,
title = {Optimizing Observables with Machine Learning for Better Unfolding},
author = {Miguel Arratia and Daniel Britzger and Owen Long and Benjamin Nachman},
journal= {arXiv preprint arXiv:2203.16722},
year = {2022}
}
Comments
This is the version that was published on July 5, 2022