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

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.

arXiv comment10 pages, 6 figures, 3 tables

References in corpus (25)

• The ATLAS Simulation Infrastructure
• CaloGAN: Simulating 3D High Energy Particle Showers in Multi-Layer Electromagnetic Calorimeters with Generative Adversarial Networks
• Accelerating Science with Generative Adversarial Networks: An Application to 3D Particle Showers in Multi-Layer Calorimeters
• Graph Neural Networks in Particle Physics
• Getting High: High Fidelity Simulation of High Granularity Calorimeters with High Speed
• An Efficient Lorentz Equivariant Graph Neural Network for Jet Tagging
• Machine Learning and LHC Event Generation
• AtlFast3: the next generation of fast simulation in ATLAS
• Score-based Generative Models for Calorimeter Shower Simulation
• CaloFlow: Fast and Accurate Generation of Calorimeter Showers with Normalizing Flows
• MLPF: Efficient machine-learned particle-flow reconstruction using graph neural networks
• Fast Point Cloud Generation with Diffusion Models in High Energy Physics
• CaloClouds: Fast Geometry-Independent Highly-Granular Calorimeter Simulation
• EPiC-GAN: Equivariant Point Cloud Generation for Particle Jets
• How to Understand Limitations of Generative Networks
• CaloScore v2: Single-shot Calorimeter Shower Simulation with Diffusion Models
• Inductive Simulation of Calorimeter Showers with Normalizing Flows
• Reconstructing particles in jets using set transformer and hypergraph prediction networks
• PC-Droid: Faster diffusion and improved quality for particle cloud generation
• Deep Generative Models for Detector Signature Simulation: A Taxonomic Review
• Ultra-High-Resolution Detector Simulation with Intra-Event Aware GAN and Self-Supervised Relational Reasoning
• Machine Learning for Particle Flow Reconstruction at CMS
• Jet tagging algorithm of graph network with HaarPooling message passing
• Calorimeter shower superresolution
• CaloShowerGAN, a Generative Adversarial Networks model for fast calorimeter shower simulation

Cited by in corpus (6)

• Full Event Particle-Level Unfolding with Variable-Length Latent Variational Diffusion
• CaloChallenge 2022: A Community Challenge for Fast Calorimeter Simulation
• Calibrating Bayesian Generative Machine Learning for Bayesiamplification
• Parnassus: An Automated Approach to Accurate, Precise, and Fast Detector Simulation and Reconstruction
• TIGER: A Topology-Agnostic, Hierarchical Graph Network for Event Reconstruction
• ParaFlow: fast calorimeter simulations parameterized in upstream material configurations