Implicit Quantile Networks For Emulation in Jet Physics

TL;DR

This paper demonstrates that implicit quantile networks can effectively emulate jet physics simulations, closely matching traditional methods while providing faster sampling for physics applications.

Contribution

The work introduces the use of implicit quantile networks as emulators for jet physics, showcasing their ability to model complex conditional densities efficiently.

Findings

IQNs closely match Delphes in jet simulation accuracy

IQNs enable faster jet event generation

Effective modeling of conditional densities in physics simulations

Abstract

The ability to model and sample from conditional densities is important in many physics applications. Implicit quantile networks (IQN) have been successfully applied to this task in domains outside physics. In this work, we illustrate the potential of IQNs as components of emulators using the simulation of jets as an example. Specifically, we use an IQN to map jets described by their 4-momenta at the generation level to jets at the event reconstruction level. The conditional densities emulated by our model closely match those generated by $\texttt{Delphes}$, while also enabling faster jet simulation.