CaloClouds3: Ultra-Fast Geometry-Independent Highly-Granular Calorimeter Simulation

Abstract

We present CaloClouds3, a model for the fast simulation of photon showers in the barrel of a high granularity detector. This iteration demonstrates for the first time how a pointcloud model can employ angular conditioning to replicate photons at all incident angles. Showers produced by this model can be used across the whole detector barrel, due to specially produced position agnostic training data. With this flexibility, the model is usable in a full simulation and reconstruction chain, which offers a further handle for evaluating physics performance of the model. As inference time is a crucial consideration for a generative model, the pre-processing and hyperparameters are aggressively optimised, achieving a speed up factor of two orders of magnitude over Geant4 at inference.

28 pages, 19 figures

References in corpus (15)

• Score-based Generative Models for Calorimeter Shower Simulation
• CaloFlow: Fast and Accurate Generation of Calorimeter Showers with Normalizing Flows
• CaloClouds: Fast Geometry-Independent Highly-Granular Calorimeter Simulation
• CaloScore v2: Single-shot Calorimeter Shower Simulation with Diffusion Models
• Deep generative models for fast photon shower simulation in ATLAS
• Inductive Simulation of Calorimeter Showers with Normalizing Flows
• Deep Generative Models for Detector Signature Simulation: A Taxonomic Review
• CaloFlow for CaloChallenge Dataset 1
• Ultra-High-Resolution Detector Simulation with Intra-Event Aware GAN and Self-Supervised Relational Reasoning
• CaloDREAM -- Detector Response Emulation via Attentive flow Matching
• A Lorentz-Equivariant Transformer for All of the LHC
• Normalizing Flows for High-Dimensional Detector Simulations
• CaloChallenge 2022: A Community Challenge for Fast Calorimeter Simulation
• Toward a foundation model for heavy-ion collision experiments based on point-cloud diffusion
• CaloHadronic: a diffusion model for the generation of hadronic showers