Hadrons, Better, Faster, Stronger

TL;DR

This paper advances the development of machine-learning-based hadronic shower simulators, demonstrating improved generative models that accurately produce realistic detector responses, crucial for high-energy physics experiments.

Contribution

The paper improves upon previous generative models and demonstrates their successful application to simulate hadronic showers in a realistic detector environment.

Findings

Successful learning of hadronic showers by improved generative models

Application of reconstruction software affects energy response and resolution

Marks progress towards realistic, fast shower simulation

Abstract

Motivated by the computational limitations of simulating interactions of particles in highly-granular detectors, there exists a concerted effort to build fast and exact machine-learning-based shower simulators. This work reports progress on two important fronts. First, the previously investigated WGAN and BIB-AE generative models are improved and successful learning of hadronic showers initiated by charged pions in a segment of the hadronic calorimeter of the International Large Detector (ILD) is demonstrated for the first time. Second, we consider how state-of-the-art reconstruction software applied to generated shower energies affects the obtainable energy response and resolution. While many challenges remain, these results constitute an important milestone in using generative models in a realistic setting.