CERN Accelerating science

Article
Report number	arXiv:2511.01460 ; DESY-25-148
Title	CaloClouds3: Ultra-fast geometry-independent highly-granular calorimeter simulation
Related title	CaloClouds3: Ultra-Fast Geometry-Independent Highly-Granular Calorimeter Simulation
Author(s)	Buss, Thorsten (DESY ; Hamburg U.) ; Day-Hall, Henry (DESY) ; Gaede, Frank (DESY) ; Kasieczka, Gregor (Hamburg U.) ; Krüger, Katja (DESY) ; Korol, Anatolii (DESY) ; Madlener, Thomas (DESY) ; McKeown, Peter (CERN) ; Mozzanica, Martina (Hamburg U.) ; Valente, Lorenzo (Hamburg U.)
Publication	2026
Imprint	2025-11-03
Number of pages	28
In:	JINST 21 (2026) P03018
DOI	10.1088/1748-0221/21/03/P03018
Subject category	physics.ins-det ; hep-ex ; physics.comp-ph ; Detectors and Experimental Techniques ; Particle Physics - Experiment ; Computing and Computers
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.
Copyright/License	preprint: (License: arXiv nonexclusive-distrib 1.0) publication: © 2026 The Author(s) (License: CC-BY-4.0)

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