CaloHadronic: a diffusion model for the generation of hadronic showers

TL;DR

This paper introduces a transformer-based diffusion model capable of efficiently generating complex hadronic showers across electromagnetic and hadronic calorimeters, advancing machine learning simulations in particle physics.

Contribution

It extends previous diffusion models with a transformer architecture to simulate holistic hadronic showers in highly granular calorimeters, a novel achievement in the field.

Findings

Effective generation of complex hadronic shower structures

First holistic simulation across electromagnetic and hadronic calorimeters

Improved accuracy and efficiency over previous models

Abstract

Simulating showers of particles in highly-granular calorimeters is a key frontier in the application of machine learning to particle physics. Achieving high accuracy and speed with generative machine learning models can enable them to augment traditional simulations and alleviate a major computing constraint. Recent developments have shown how diffusion based generative shower simulation approaches that do not rely on a fixed structure, but instead generate geometry-independent point clouds, are very efficient. We present a transformer-based extension to previous architectures which were developed for simulating electromagnetic showers in the highly granular electromagnetic calorimeter of the International Large Detector, ILD. The attention mechanism now allows us to generate complex hadronic showers with more pronounced substructure across both the electromagnetic and hadronic calorimeters. This is the first time that machine learning methods are used to holistically generate showers across the electromagnetic and hadronic calorimeter in highly granular imaging calorimeter systems.