Implicit Quantile Neural Networks for Jet Simulation and Correction

TL;DR

This paper demonstrates the application of implicit quantile neural networks (IQNs) to improve jet simulation and correction in particle physics, leveraging CMS Open Data for accurate modeling of conditional densities.

Contribution

It introduces a novel application of IQNs to jet simulation and correction, showcasing their effectiveness in a high-energy physics context.

Findings

IQNs accurately model conditional densities for jet data

Application improves jet simulation and correction quality

Demonstrates feasibility of IQNs in scientific data modeling

Abstract

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.