Implicit Quantile Neural Networks for Jet Simulation and Correction
This work addresses the need for reliable conditional density modeling in particle physics, specifically for jet simulation and correction, but it appears incremental as it applies an existing method (IQNs) to a new domain.
The paper tackled the problem of modeling conditional densities for jet simulation and correction in particle physics by applying implicit quantile neural networks (IQNs) to CMS Open Data, achieving a successful application as demonstrated in the 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.