Speaker
Henning Rose
Description
We successfully demonstrate the use of a generative transformer for learning point-cloud simulations of electromagnetic showers in the International Large Detector (ILD) calorimeter. By reusing the architecture and workflow of the “OmniJet-alpha” model, this transformer predicts sequences of tokens that represent energy deposits within the calorimeter. This autoregressive approach enables the model to learn the sequence length of the point cloud, supporting a variable-length and realistic shower development. Furthermore, the tokenized representation allows the model to learn the shower geometry without being restricted to a fixed voxel grid.
| Track | Detector simulation & event generation |
|---|
Authors
Anna Maria Cecilia Hallin
(University of Hamburg)
Gregor Kasieczka
(Hamburg University (DE))
Henning Rose
Joschka Valentin Maria Birk
Martina Mozzanica
(Hamburg University (DE))
