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

## TL;DR

ExpertSim introduces a Mixture-of-Generative-Experts deep learning model that significantly speeds up particle detector simulations at CERN while maintaining high accuracy, addressing the computational challenges of traditional Monte Carlo methods.

## Contribution

This paper presents ExpertSim, a novel Mixture-of-Generative-Experts architecture tailored for particle detector simulation, improving efficiency and accuracy over existing methods.

## Key findings

- ExpertSim achieves faster simulation times than Monte Carlo methods.
- The model maintains high fidelity in simulating calorimeter responses.
- ExpertSim effectively captures data variability across different simulation subsets.

## Abstract

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.