mlsanityData to 7 Oct 2026

CaloDVAE : Discrete Variational Autoencoders for Fast Calorimeter Shower Simulation

arXiv 2210.07430 ↗First posted 14 Oct 2022physics.ins-det cs.LG hep-excited by 6

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Abstract (arXiv)

Calorimeter simulation is the most computationally expensive part of Monte Carlo generation of samples necessary for analysis of experimental data at the Large Hadron Collider (LHC). The High-Luminosity upgrade of the LHC would require an even larger amount of such samples. We present a technique based on Discrete Variational Autoencoders (DVAEs) to simulate particle showers in Electromagnetic Calorimeters. We discuss how this work paves the way towards exploration of quantum annealing processors as sampling devices for generation of simulated High Energy Physics datasets.

Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). Builds on 10 of its 34 references that are in the map. First posted on arXiv 14 Oct 2022.

Builds on

10 of 34 references.

References · 10

  1. 2013Auto-Encoding Variational Bayescited by 3,980
  2. 2016Categorical Reparameterization with Gumbel-Softmaxcited by 2,708
  3. 2016The Concrete Distribution: A Continuous Relaxation of Discrete Random Variablescited by 1,135
  4. 2016Discrete Variational Autoencoderscited by 124
  5. 2017CaloGAN: Simulating 3D High Energy Particle Showers in Multi-Layer Electromagnetic Calorimeters with Generative Adversarial Networkscited by 78
  6. 2017Learning Particle Physics by Example: Location-Aware Generative Adversarial Networks for Physics Synthesiscited by 46
  7. 2018Quantum Variational Autoencodercited by 41
  8. 2018DVAE++: Discrete Variational Autoencoders with Overlapping Transformationscited by 31
  9. 2019A Path Towards Quantum Advantage in Training Deep Generative Models with Quantum Annealerscited by 15
  10. 2018GumBolt: Extending Gumbel trick to Boltzmann priorscited by 9

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 4; 2024: 0; 2025: 0; 2026: 2.

6 papers.

  1. 2023Deep Generative Models for Detector Signature Simulation: A Taxonomic Reviewcited by 18
  2. 2023EPiC-ly Fast Particle Cloud Generation with Flow-Matching and Diffusioncited by 17
  3. 2023CaloQVAE : Simulating high-energy particle-calorimeter interactions using hybrid quantum-classical generative modelscited by 11
  4. 2023Calorimeter shower superresolutioncited by 7
  5. 2026Generalizable Foundation Models for Calorimetry via Mixtures-of-Experts and Parameter Efficient Fine Tuningcited by 3
  6. 2026GPT-Based Fast Simulation of CLAS12 Detector Hits via Conditional Autoregressive Generationcited by 0