Erlangen 2026 – wissenschaftliches Programm
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T: Fachverband Teilchenphysik
T 93: Data, AI, Computing, Electronics VIII
T 93.4: Vortrag
Freitag, 20. März 2026, 09:45–10:00, KH 00.024
OmniJet-alpha for Calorimeters - Autoregressive generation of Calorimeter Showers — Joschka Birk1, Frank Gaede2, Anna Hallin1, Gregor Kasieczka1, Martina Mozzanica1, and •Henning Rose1 — 1Institute for Experimental Physics, Universität Hamburg Luruper Chaussee 149, 22761 Hamburg, Germany — 2Deutsches Elektronen-Synchrotron DESY, Notkestr. 85, 22607 Hamburg, Germany
We present an autoregressive approach for the generation of high-granularity calorimeter showers based on the OmniJetAlpha architecture. The proposed method directly embeds individual calorimeter shower hits, enabling end-to-end autoregressive generation without relying on discrete tokenization or vector-quantized codebooks. To model hit features efficiently, the architecture employs separate prediction heads for each feature dimension, allowing the overall model size to remain compact even at very high spatial and energy granularities, while avoiding codebook collapse and related representational bottlenecks. This design facilitates stable training and scalable generation in regimes where traditional token-based approaches become impractical. Our results demonstrate that generative pre-training can be performed directly at the data level for calorimeter shower modeling, removing the need for intermediate representations. This is a significant step toward leveraging transformer-based foundation models in high-energy physics, as autoregressive pre-training has proven exceptionally effective in generative modeling, as evidenced by recent advances in large language models.
Keywords: Autoregressive Generation; Foundation Models; Calorimeter Showers; Machine Learning; Simulation