Machine Learning in Julia for Calorimeter Showers

The calorimeter, a key detector in Large Hadron Collider (LHC) experiments, measures the energy of particles interacting with detector materials. Particles emerging from collisions create cascades of secondary particles, or showers. Describing these processes requires precise simulation methods like the Geant4 toolkit. Machine learning (ML) techniques, such as generative modeling, offer a promising alternative. Recently, the Fast Calorimeter Simulation Challenge (CaloChallenge) spurred the development and evaluation of different models. Also, in High-Energy Physics (HEP), there has been an increasing interest in using Julia as a language for software development, for combining the ease of programming of interactive languages, e.g. Python, with the speed of compiled languages, e.g. C++. This project targets to assess Julia's machine learning ecosystem's maturity, in terms of availability and robustness of libraries and tools for deep learning. To this end, a selection of models from the CaloChallenge will be chosen for implementation in Julia, as well as the required functionality to enable training and evaluation of the networks. Ultimately, a comprehensive analysis will be conducted between Python/C++ and Julia implementations, considering training time, inference time and evaluation metrics from CaloChallenge.