Decoding Photons: Physics in the Latent Space of a BIB-AE Generative Network

TL;DR

This paper explores how a BIB-AE generative network encodes physics information in its latent space for photon shower simulation, leading to improved modeling of shower shapes in collider experiments.

Contribution

It reveals the physics encoding in the BIB-AE's latent space and proposes optimization methods to enhance simulation accuracy.

Findings

Enhanced modeling of shower shape along incident axis

Insights into physics information encoding in latent space

Methods to optimize generative performance

Abstract

Given the increasing data collection capabilities and limited computing resources of future collider experiments, interest in using generative neural networks for the fast simulation of collider events is growing. In our previous study, the Bounded Information Bottleneck Autoencoder (BIB-AE) architecture for generating photon showers in a high-granularity calorimeter showed a high accuracy modeling of various global differential shower distributions. In this work, we investigate how the BIB-AE encodes this physics information in its latent space. Our understanding of this encoding allows us to propose methods to optimize the generation performance further, for example, by altering latent space sampling or by suggesting specific changes to hyperparameters. In particular, we improve the modeling of the shower shape along the particle incident axis.