# Generative Models for Fast Calorimeter Simulation.LHCb case

arXiv: https://arxiv.org/abs/1812.01319 · HTML: https://mlsanity.com/paper/1812.01319/

## Key facts

- Cited by 25 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Dec 2018.

## Built on by (most cited first, 25 of 25)

- [A Living Review of Machine Learning for Particle Physics](https://mlsanity.com/paper/2102.02770/) (2021)
- [Score-based Generative Models for Calorimeter Shower Simulation](https://mlsanity.com/paper/2206.11898/) (2022)
- [Machine Learning in the Search for New Fundamental Physics](https://mlsanity.com/paper/2112.03769/) (2021)
- [CaloFlow: Fast and Accurate Generation of Calorimeter Showers with Normalizing Flows](https://mlsanity.com/paper/2106.05285/) (2021)
- [Deep Generative Models for Detector Signature Simulation: A Taxonomic Review](https://mlsanity.com/paper/2312.09597/) (2023)
- [New directions for surrogate models and differentiable programming for High Energy Physics detector simulation](https://mlsanity.com/paper/2203.08806/) (2022)
- [Comparison of Point Cloud and Image-based Models for Calorimeter Fast Simulation](https://mlsanity.com/paper/2307.04780/) (2023)
- [Latent Space Refinement for Deep Generative Models](https://mlsanity.com/paper/2106.00792/) (2021)
- [Machine Learning methods for simulating particle response in the Zero Degree Calorimeter at the ALICE experiment, CERN](https://mlsanity.com/paper/2306.13606/) (2023)
- [Artificial Intelligence for the Electron Ion Collider (AI4EIC)](https://mlsanity.com/paper/2307.08593/) (2023)
- [Generalizable Foundation Models for Calorimetry via Mixtures-of-Experts and Parameter Efficient Fine Tuning](https://mlsanity.com/paper/2603.28804/) (2026)
- [Generative Models and Statistical Validation](https://mlsanity.com/paper/2605.30453/) (2026)
- [Physics Validation of Novel Convolutional 2D Architectures for Speeding Up High Energy Physics Simulations](https://mlsanity.com/paper/2105.08960/) (2021)
- [Towards Reliable Neural Generative Modeling of Detectors](https://mlsanity.com/paper/2204.09947/) (2022)
- [Generative models uncertainty estimation](https://mlsanity.com/paper/2210.09767/) (2022)
- [Geometry-aware Autoregressive Models for Calorimeter Shower Simulations](https://mlsanity.com/paper/2212.08233/) (2022)
- [The LHCb ultra-fast simulation option, Lamarr: design and validation](https://mlsanity.com/paper/2309.13213/) (2023)
- [Particle physics DL-simulation with control over generated data properties](https://mlsanity.com/paper/2405.14049/) (2024)
- [Deep Generative Models for Proton Zero Degree Calorimeter Simulations in ALICE, CERN](https://mlsanity.com/paper/2406.03263/) (2024)
- [Interpretable machine learning approach for electron antineutrino selection in a large liquid scintillator detector](https://mlsanity.com/paper/2406.12901/) (2024)
- [Sequential Kernelized Stein Discrepancy](https://mlsanity.com/paper/2409.17505/) (2024)
- [Accelerating GAN training using highly parallel hardware on public cloud](https://mlsanity.com/paper/2111.04628/) (2021)
- [Hyperparameter Optimization of Generative Adversarial Network Models for High-Energy Physics Simulations](https://mlsanity.com/paper/2208.07715/) (2022)
- [ML-based Fast Simulation of FARICH Responses](https://mlsanity.com/paper/2605.17635/) (2026)
- [GPT-Based Fast Simulation of CLAS12 Detector Hits via Conditional Autoregressive Generation](https://mlsanity.com/paper/2606.16035/) (2026)

## Abstract (arXiv)

Simulation is one of the key components in high energy physics. Historically it relies on the Monte Carlo methods which require a tremendous amount of computation resources. These methods may have difficulties with the expected High Luminosity Large Hadron Collider (HL LHC) need, so the experiment is in urgent need of new fast simulation techniques. We introduce a new Deep Learning framework based on Generative Adversarial Networks which can be faster than traditional simulation methods by 5 order of magnitude with reasonable simulation accuracy. This approach will allow physicists to produce a big enough amount of simulated data needed by the next HL LHC experiments using limited computing resources.