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.

arXiv comment10 pages, 5 figures

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Cited by in corpus (20)

• Deep Generative Models for Detector Signature Simulation: A Taxonomic Review
• CaloDREAM -- Detector Response Emulation via Attentive flow Matching
• Normalizing Flows for High-Dimensional Detector Simulations
• Convolutional L2LFlows: Generating Accurate Showers in Highly Granular Calorimeters Using Convolutional Normalizing Flows
• Full Event Particle-Level Unfolding with Variable-Length Latent Variational Diffusion
• Generalizing to new geometries with Geometry-Aware Autoregressive Models (GAAMs) for fast calorimeter simulation
• CaloChallenge 2022: A Community Challenge for Fast Calorimeter Simulation
• Anomaly detection with flow-based fast calorimeter simulators
• 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
• Ultra Fast Calorimeter Simulation with Generative Machine Learning on FPGAs
• CaloClouds3: Ultra-Fast Geometry-Independent Highly-Granular Calorimeter Simulation
• ParaFlow: fast calorimeter simulations parameterized in upstream material configurations