AllShowers: One model for all calorimeter showers
Thorsten Buss, Henry Day-Hall, Frank Gaede, Gregor Kasieczka, Katja Kr\"uger

TL;DR
AllShowers is a unified, Transformer-based generative model that efficiently simulates diverse calorimeter showers across multiple particle types, improving realism and scalability in detector simulations.
Contribution
The paper introduces AllShowers, a single continuous normalizing flow model capable of generating calorimeter showers for various particles without retraining, advancing the state of simulation models.
Findings
Generates realistic showers for multiple particle types and energies
Outperforms previous models in fidelity for hadronic showers
Reduces computational demands with innovative attention masking
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…
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Taxonomy
TopicsParticle physics theoretical and experimental studies · High-Energy Particle Collisions Research · Particle Detector Development and Performance
