mlsanityData to 14 Oct 2026

Controlling Physical Attributes in GAN-Accelerated Simulation of Electromagnetic Calorimeters

arXiv 1711.08813 ↗First posted 23 Nov 2017hep-ex cs.LG physics.data-ancited by 23

Not on the map. Why?

Abstract (arXiv)

High-precision modeling of subatomic particle interactions is critical for many fields within the physical sciences, such as nuclear physics and high energy particle physics. Most simulation pipelines in the sciences are computationally intensive -- in a variety of scientific fields, Generative Adversarial Networks have been suggested as a solution to speed up the forward component of simulation, with promising results. An important component of any simulation system for the sciences is the ability to condition on any number of physically meaningful latent characteristics that can effect the forward generation procedure. We introduce an auxiliary task to the training of a Generative Adversarial Network on particle showers in a multi-layer electromagnetic calorimeter, which allows our model to learn an attribute-aware conditioning mechanism.

Key facts. Cited by 23 papers in the map (data through 14 Oct 2026). First posted on arXiv 23 Nov 2017.

Builds on

No reference list yet.

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2021: 4; 2022: 3; 2023: 9; 2024: 1; 2025: 4; 2026: 2.

23 papers.

Sort:

By area:

  1. 2021A Living Review of Machine Learning for Particle Physicscited by 78
  2. 2022Score-based Generative Models for Calorimeter Shower Simulationcited by 52
  3. 2021CaloFlow: Fast and Accurate Generation of Calorimeter Showers with Normalizing Flowscited by 46
  4. 2021CaloFlow II: Even Faster and Still Accurate Generation of Calorimeter Showers with Normalizing Flowscited by 34
  5. 2023CaloClouds: Fast Geometry-Independent Highly-Granular Calorimeter Simulationcited by 33
  6. 2023PC-JeDi: Diffusion for Particle Cloud Generation in High Energy Physicscited by 27
  7. 2023Ultra-High-Resolution Detector Simulation with Intra-Event Aware GAN and Self-Supervised Relational Reasoningcited by 25
  8. 2023Inductive Simulation of Calorimeter Showers with Normalizing Flowscited by 22
  9. 2023CaloClouds II: Ultra-Fast Geometry-Independent Highly-Granular Calorimeter Simulationcited by 20
  10. 2023Deep Generative Models for Detector Signature Simulation: A Taxonomic Reviewcited by 18
  11. 2022New directions for surrogate models and differentiable programming for High Energy Physics detector simulationcited by 16
  12. 2023Comparison of Point Cloud and Image-based Models for Calorimeter Fast Simulationcited by 16
11 more
  1. 2022CaloFlow for CaloChallenge Dataset 1cited by 14
  2. 2025OmniJet-αC: Learning point cloud calorimeter simulations using generative transformerscited by 14
  3. 2024Convolutional L2LFlows: Generating Accurate Showers in Highly Granular Calorimeters Using Convolutional Normalizing Flowscited by 13
  4. 2021Latent Space Refinement for Deep Generative Modelscited by 10
  5. 2023PC-Droid: Faster diffusion and improved quality for particle cloud generationcited by 8
  6. 2025CaloHadronic: a diffusion model for the generation of hadronic showerscited by 8
  7. 2023Calorimeter shower superresolutioncited by 7
  8. 2026AllShowers: One model for all calorimeter showerscited by 3
  9. 2025A First Full Physics Benchmark for Highly Granular Calorimeter Surrogatescited by 2
  10. 2025BitHEP -- The Limits of Low-Precision ML in HEPcited by 1
  11. 2026CaloTrilogy: Toward a Breakthrough in One-Step, End-to-End, Physics-Guided Shower Generation for Modern Calorimeterscited by 1