Speakers
Description
In high energy physics experiments, the calorimeter is a key detector measuring the energy of particles. These particles interact with the material of the calorimeter, creating cascades of secondary particles, the so-called showers. Describing development of cascades of particles relies on precise simulation methods, which is inherently slow and constitutes a challenge for HEP experiments. Furthermore, with the upcoming high luminosity upgrade of the LHC with more complex events and a much increased trigger rate, the amount of required simulated events will increase. Machine Learning (ML) techniques such as generative models are currently widely explored for faster simulation alternatives. The pipeline of a ML fast simulation solution consists of multiple components starting from data generation and preprocessing to model training, optimization, validation and deployment within C++ framework. In this contribution, we will present our latest developements: to build a portable and a scalable pipeline with Kubeflow, to automate hyperparameter search with Optuna and NAS and to optimize the inference memory footprint in C++ by leveraging quantization and graph optimization strategies for different hardware acceleretors.
