mlsanityData to 14 Oct 2026

ExpertSim: Fast Particle Detector Simulation Using Mixture-of-Generative-Experts

arXiv 2508.20991 ↗First posted 28 Aug 2025cs.CV cs.AIcited by 0

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Abstract (arXiv)

Simulating detector responses is a crucial part of understanding the inner workings of particle collisions in the Large Hadron Collider at CERN. Such simulations are currently performed with statistical Monte Carlo methods, which are computationally expensive and put a significant strain on CERN's computational grid. Therefore, recent proposals advocate for generative machine learning methods to enable more efficient simulations. However, the distribution of the data varies significantly across the simulations, which is hard to capture with out-of-the-box methods. In this study, we present ExpertSim - a deep learning simulation approach tailored for the Zero Degree Calorimeter in the ALICE experiment. Our method utilizes a Mixture-of-Generative-Experts architecture, where each expert specializes in simulating a different subset of the data. This allows for a more precise and efficient generation process, as each expert focuses on a specific aspect of the calorimeter response. ExpertSim not only improves accuracy, but also provides a significant speedup compared to the traditional Monte-Carlo methods, offering a promising solution for high-efficiency detector simulations in particle physics experiments at CERN. We make the code available at https://github.com/patrick-bedkowski/expertsim-mix-of-generative-experts.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 38 of its 55 references that are in the map. First posted on arXiv 28 Aug 2025.

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References · 38

  1. 2015Distilling the Knowledge in a Neural Networkcited by 10,324
  2. 2019EfficientNet: Rethinking Model Scaling for Convolutional Neural Networkscited by 6,442
  3. 2017Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layercited by 3,509
  4. 2021Switch Transformers: Scaling to Trillion Parameter Models with Simple and Efficient Sparsitycited by 3,073
  5. 2013Estimating or Propagating Gradients Through Stochastic Neurons for Conditional Computationcited by 2,079
  6. 2020GShard: Scaling Giant Models with Conditional Computation and Automatic Shardingcited by 1,631
  7. 2021GLaM: Efficient Scaling of Language Models with Mixture-of-Expertscited by 897
  8. 2024DeepSeekMoE: Towards Ultimate Expert Specialization in Mixture-of-Experts Language Modelscited by 867
  9. 2021Scaling Vision with Sparse Mixture of Expertscited by 747
  10. 2018Universal Transformerscited by 647
  11. 2022Mixture-of-Experts with Expert Choice Routingcited by 623
  12. 2016Adaptive Computation Time for Recurrent Neural Networkscited by 506
26 more
  1. 2017SkipNet: Learning Dynamic Routing in Convolutional Networkscited by 332
  2. 2021Pruning and Quantization for Deep Neural Network Acceleration: A Surveycited by 263
  3. 2022Unified Scaling Laws for Routed Language Modelscited by 253
  4. 2021Hash Layers For Large Sparse Modelscited by 237
  5. 2019Depth-Adaptive Transformercited by 236
  6. 2019Variational Mixture-of-Experts Autoencoders for Multi-Modal Deep Generative Modelscited by 178
  7. 2021MoEfication: Transformer Feed-forward Layers are Mixtures of Expertscited by 152
  8. 2021DEMix Layers: Disentangling Domains for Modular Language Modelingcited by 132
  9. 2016Spatially Adaptive Computation Time for Residual Networkscited by 118
  10. 2021Split Computing and Early Exiting for Deep Learning Applications: Survey and Research Challengescited by 115
  11. 2019Dynamic Convolutions: Exploiting Spatial Sparsity for Faster Inferencecited by 91
  12. 2020Why should we add early exits to neural networks?cited by 70
  13. 2022MoEBERT: from BERT to Mixture-of-Experts via Importance-Guided Adaptationcited by 37
  14. 2021EvoMoE: An Evolutional Mixture-of-Experts Training Framework via Dense-To-Sparse Gatecited by 30
  15. 2022CaloMan: Fast generation of calorimeter showers with density estimation on learned manifoldscited by 28
  16. 2020End-to-end Sinkhorn Autoencoder with Noise Generatorcited by 17
  17. 2018MEGAN: Mixture of Experts of Generative Adversarial Networks for Multimodal Image Generationcited by 15
  18. 2023Mobile V-MoEs: Scaling Down Vision Transformers via Sparse Mixture-of-Expertscited by 14
  19. 2022Selectively increasing the diversity of GAN-generated samplescited by 9
  20. 2019Learning Instance-wise Sparsity for Accelerating Deep Modelscited by 7
  21. 2024Generative Diffusion Models for Fast Simulations of Particle Collisions at CERNcited by 6
  22. 2023Machine Learning methods for simulating particle response in the Zero Degree Calorimeter at the ALICE experiment, CERNcited by 5
  23. 2024Applying generative neural networks for fast simulations of the ALICE (CERN) experimentcited by 3
  24. 2025Even Faster Simulations with Flow Matching: A Study of Zero Degree Calorimeter Responsescited by 2
  25. 2024Particle physics DL-simulation with control over generated data propertiescited by 1
  26. 2024Deep Generative Models for Proton Zero Degree Calorimeter Simulations in ALICE, CERNcited by 1

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