CaloGraph: Graph-based diffusion model for fast shower generation in calorimeters with irregular geometry

TL;DR

This paper introduces CaloGraph, a graph-based diffusion model that enables fast and efficient calorimeter response simulations, especially for irregular geometries, representing a novel application of diffusion models in high-energy physics.

Contribution

The paper presents the first application of a graph diffusion model for calorimeter simulation, addressing computational challenges in detector response generation.

Findings

Effective for low-granularity, irregular geometries

Significantly faster than traditional simulation methods

First use of graph diffusion in particle physics simulations

Abstract

Denoising diffusion models have gained prominence in various generative tasks, prompting their exploration for the generation of calorimeter responses. Given the computational challenges posed by detector simulations in high-energy physics experiments, the necessity to explore new machine-learning-based approaches is evident. This study introduces a novel graph-based diffusion model designed specifically for rapid calorimeter simulations. The methodology is particularly well-suited for low-granularity detectors featuring irregular geometries. We apply this model to the ATLAS dataset published in the context of the Fast Calorimeter Simulation Challenge 2022, marking the first application of a graph diffusion model in the field of particle physics.