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

Created by
  • Haebom

Author

Patryk B\k{e}dkowski, Jan Dubi nski, Filip Szatkowski, Kamil Deja, Przemys{\l}aw Rokita, Tomasz Trzci nski

Outline

This paper addresses detector response simulations, which are essential for understanding the inner workings of particle collisions at CERN's Large Hadron Collider (LHC). Because conventional statistical Monte Carlo methods are computationally expensive and place significant strain on CERN's computing grid, this study proposes a generative machine learning approach for efficient simulations. To address the significant variation in data distributions across simulations, which is difficult to capture with conventional methods, we propose ExpertSim, a deep learning simulation approach tailored to the ALICE experiment's zero-degree calorimeter. ExpertSim uses a Mixture of Generative Experts architecture to specialize each expert in simulating a different subset of the data, thereby improving accuracy and speed. It offers speed improvements over conventional Monte Carlo methods, and the code is available on GitHub.

Takeaways, Limitations

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Takeaways:
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We present a novel deep learning-based approach (ExpertSim) for particle detector simulation that is more efficient than conventional Monte Carlo methods.
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Effectively handle data distribution variability by leveraging the Mixture-of-Generative-Experts architecture.
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A promising solution for simulating CERN's high-throughput detectors, with improved accuracy and speed.
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Increasing the reproducibility and scalability of research results through open source code disclosure.
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Limitations:
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Currently, this model is specialized for the zero-degree calorimeter of the ALICE experiment, and further research is needed to determine its generalizability to other detectors and experiments.
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Further analysis of parameter optimization and model complexity of the Mixture-of-Generative-Experts architecture is needed.
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The performance of the model should be more rigorously verified through comparative analysis with actual experimental data.
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