SciPost Submission Page
AllShowers: One model for all calorimeter showers
by Thorsten Buss, Henry Day-Hall, Frank Gaede, Gregor Kasieczka, Katja Krüger
Submission summary
| Authors (as registered SciPost users): | Thorsten Buss |
| Submission information | |
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| Preprint Link: | scipost_202601_00069v1 (pdf) |
| Code repository: | https://github.com/FLC-QU-hep/AllShowers |
| Data repository: | https://zenodo.org/records/18020348 |
| Date submitted: | Jan. 29, 2026, 3:25 p.m. |
| Submitted by: | Thorsten Buss |
| Submitted to: | SciPost Physics |
| Ontological classification | |
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| Academic field: | Physics |
| Specialties: |
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| Approaches: | Experimental, Computational |
The author(s) disclose that the following generative AI tools have been used in the preparation of this submission:
We used GitHub Copilot (with Claude Sonnet 4.5) mainly for code completion, Grammarly for finding punctuation and grammar mistakes in the manuscript, and ChatGPT 5.1 for converting detailed bullet-point lists into floating text, which we further edited.
Abstract
Accurate and efficient detector simulation is essential for modern collider experiments. To reduce the high computational cost, various fast machine learning surrogate models have been proposed. Traditional surrogate models for calorimeter shower modeling train separate networks for each particle species, limiting scalability and reuse. We introduce AllShowers, a unified generative model that simulates calorimeter showers across multiple particle types using a single generative model. AllShowers is a continuous normalizing flow model with a Transformer architecture, enabling it to generate complex spatial and energy correlations in variable-length point cloud representations of showers. Trained on a diverse dataset of simulated showers in the highly granular ILD detector, the model demonstrates the ability to generate realistic showers for electrons, photons, and charged and neutral hadrons across a wide range of incident energies and angles without retraining. In addition to unifying shower generation for multiple particle types, AllShowers surpasses the fidelity of previous single-particle-type models for hadronic showers. Key innovations include the use of a layer embedding, allowing the model to learn all relevant calorimeter layer properties; a custom attention masking scheme to reduce computational demands and introduce a helpful inductive bias; and a shower- and layer-wise optimal transport mapping to improve training convergence and sample quality. AllShowers marks a significant step towards a universal model for calorimeter shower simulations in collider experiments.
Author indications on fulfilling journal expectations
- Provide a novel and synergetic link between different research areas.
- Open a new pathway in an existing or a new research direction, with clear potential for multi-pronged follow-up work
- Detail a groundbreaking theoretical/experimental/computational discovery
- Present a breakthrough on a previously-identified and long-standing research stumbling block