Preprint

PUBDB-2024-07895

http://join2-wiki.gsi.de/foswiki/pub/Main/Artwork/join2_logo100x88.png OmniJet-${\alpha_{ C}}$: Learning point cloud calorimeter simulations using generative transformers

Birk, J.Extern* ; Gaede, F. (Corresponding author)DESY* ; Hallin, A.Extern* ; Kasieczka, G.Extern* ; Mozzanica, M.Extern* ; Rose, H.

2025

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Please use a persistent id in citations: doi:10.3204/PUBDB-2024-07895

Report No.: DESY-24-214; arXiv:2501.05534

Abstract: We show the first use of generative transformers for generating calorimeter showers as point clouds in a high-granularity calorimeter. Using the tokenizer and generative part of the OmniJet-${\alpha}$ model, we represent the hits in the detector as sequences of integers. This model allows variable-length sequences, which means that it supports realistic shower development and does not need to be conditioned on the number of hits. Since the tokenization represents the showers as point clouds, the model learns the geometry of the showers without being restricted to any particular voxel grid.

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Contributing Institute(s):

1. Technol. zukünft. Teilchenph. Experim. (FTX)

Research Program(s):

1. 623 - Data Management and Analysis (POF4-623) (POF4-623)
2. DFG project G:(GEPRIS)390833306 - EXC 2121: Quantum Universe (390833306) (390833306)
3. DFG project G:(GEPRIS)460248186 - PUNCH4NFDI - Teilchen, Universum, Kerne und Hadronen für die NFDI (460248186) (460248186)

Experiment(s):

1. No specific instrument

Appears in the scientific report 2025

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