Reliable modeling of conditional densities is important for quantitative scientific fields such as particle physics. In domains outside physics, implicit quantile neural networks (IQN) have been shown to provide accurate models of conditional densities. We present a successful application of IQNs to jet simulation and correction using the tools and simulated data from the Compact Muon Solenoid (CMS) Open Data portal.
@article{arxiv.2111.11415,
title = {Implicit Quantile Neural Networks for Jet Simulation and Correction},
author = {Braden Kronheim and Michelle P. Kuchera and Harrison B. Prosper and Raghuram Ramanujan},
journal= {arXiv preprint arXiv:2111.11415},
year = {2022}
}
Comments
NeurIPS 2021 - Workshop on Machine Learning and the Physical Sciences, Dec 2021, Vancouver, Canada