CaloScore v2: Single-shot Calorimeter Shower Simulation with Diffusion Models

Abstract

Diffusion generative models are promising alternatives for fast surrogate models, producing high-fidelity physics simulations. However, the generation time often requires an expensive denoising process with hundreds of function evaluations, restricting the current applicability of these models in a realistic setting. In this work, we report updates on the CaloScore architecture, detailing the changes in the diffusion process, which produces higher quality samples, and the use of progressive distillation, resulting in a diffusion model capable of generating new samples with a single function evaluation. We demonstrate these improvements using the Calorimeter Simulation Challenge 2022 dataset.

10 pages, 5 figures

References in corpus (4)

• Generative Adversarial Networks
• Score-Based Generative Modeling through Stochastic Differential Equations
• Fast simulation of muons produced at the SHiP experiment using Generative Adversarial Networks
• CaloMan: Fast generation of calorimeter showers with density estimation on learned manifolds

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• Normalizing Flows for High-Dimensional Detector Simulations
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• Full Event Particle-Level Unfolding with Variable-Length Latent Variational Diffusion
• Anomaly detection with flow-based fast calorimeter simulators
• CaloChallenge 2022: A Community Challenge for Fast Calorimeter Simulation
• CaloGraph: Graph-based diffusion model for fast shower generation in calorimeters with irregular geometry
• Calibrating Bayesian Generative Machine Learning for Bayesiamplification
• Effectiveness of denoising diffusion probabilistic models for fast and high-fidelity whole-event simulation in high-energy heavy-ion experiments
• Unsupervised and lightly supervised learning in particle physics
• Advancing Set-Conditional Set Generation: Diffusion Models for Fast Simulation of Reconstructed Particles
• Ultra fast, event-by-event heavy-ion simulations for next generation experiments
• Toward a foundation model for heavy-ion collision experiments based on point-cloud diffusion
• Choose Your Diffusion: Efficient and flexible ways to accelerate the diffusion model in fast high energy physics simulation
• CaloHadronic: a diffusion model for the generation of hadronic showers
• ParaFlow: fast calorimeter simulations parameterized in upstream material configurations
• CaloClouds3: Ultra-Fast Geometry-Independent Highly-Granular Calorimeter Simulation
• Ultra Fast Calorimeter Simulation with Generative Machine Learning on FPGAs