Karlsruhe 2024 – scientific programme
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T: Fachverband Teilchenphysik
T 43: Data, AI, Computing 3 (pointclouds & graphs)
T 43.1: Talk
Tuesday, March 5, 2024, 16:00–16:15, Geb. 30.33: MTI
CaloClouds: Fast Geometry-Independent Highly-Granular Calorimeter Simulation — Erik Buhmann1, Sascha Diefenbacher2, Engin Eren3, Frank Gaede3,4, Gregor Kasieczka1,4, •Anatolii Korol3, William Korcari1, Katja Krüger3, and Peter McKeown3 — 1Institut für Experimentalphysik, Universität Hamburg, Luruper Chaussee 149, 22761 Hamburg, Germany — 2Physics Division, Lawrence Berkeley National Laboratory, 1 Cyclotron Rd, Berkeley, CA 94720, USA — 3Deutsches Elektronen-Synchrotron DESY, Notkestr. 85, 22607 Hamburg, Germany — 4Center for Data and Computing in Natural Sciences CDCS, Deutsches Elektronen-Synchrotron DESY, Notkestr. 85, 22607 Hamburg, Germany
Simulating showers of particles in highly granular detectors is a key frontier in applying machine learning to particle physics. Achieving high accuracy and speed with generative machine learning models would enable them to augment traditional simulations and alleviate a significant computing constraint. This contribution marks a significant breakthrough in this task by directly generating a point cloud of O(1000) space points with energy depositions in the detector in 3D-space. Importantly, it achieves this without relying on the structure of the detector layers. This capability enables the generation of showers with arbitrary incident particle positions and accommodates varying sensor shapes and layouts.
Keywords: Simulation methods and programs; Data processing methods; Analysis and statistical methods; Calorimeter methods