AllShowers: One model for all calorimeter showers
physics.ins-det, cs.LG, hep-ex, hep-ph
Submitted: 2026-01-16
Updated: 2026-09-15
Code: https://github.com/FLC-QU-hep/AllShowers
Project page: https://ml4physicalsciences.github.io/2022/files/NeurIPS_ML4PS_2022_77.pdf
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Terminology
Sources
- A Roadmap for HEP Software and Computing R&D for the 2020s
- Accelerating Science with Generative Adversarial Networks: An Application to 3D Particle Showers in Multi-Layer Calorimeters
- Precise simulation of electromagnetic calorimeter showers using a Wasserstein Generative Adversarial Network
- Fast and accurate simulation of particle detectors using generative adversarial networks
- Calorimetry with Deep Learning: Particle Simulation and Reconstruction for Collider Physics
- GANplifying Event Samples
- Deep generative models for fast photon shower simulation in ATLAS
- CaloShowerGAN, a Generative Adversarial Networks model for fast calorimeter shower simulation
- CALPAGAN: Calorimetry for Particles using GANs
- CaloQVAE : Simulating high-energy particle-calorimeter interactions using hybrid quantum-classical generative models
- Calo-VQ: Vector-Quantized Two-Stage Generative Model in Calorimeter Simulation
- CaloFlow: Fast and Accurate Generation of Calorimeter Showers with Normalizing Flows
- CaloFlow II: Even Faster and Still Accurate Generation of Calorimeter Showers with Normalizing Flows
- CaloFlow for CaloChallenge Dataset 1
- Generative Machine Learning for Detector Response Modeling with a Conditional Normalizing Flow
- Inductive Simulation of Calorimeter Showers with Normalizing Flows
- Calorimeter shower superresolution
- Normalizing Flows for High-Dimensional Detector Simulations
- CaloPointFlow II Generating Calorimeter Showers as Point Clouds
- Unifying Simulation and Inference with Normalizing Flows