International Workshop on Machine Learning and Quantum Computing Applications in Medicine and Physics

Machine Learning methods for simulating particle response in the Zero Degree Calorimeter at the ALICE experiment, CERN

16 Sep 2022, 12:10

30m

Talk Machine Learning in Particle Physics and Astrophysics

Speaker

Jan Dubiński (Warsaw University of Technology)

Description

Currently, over 50% of the computing power at CERN’s GRID (>500 000 CPUs in 170 centres) is used to run High Energy Physics simulations. The recent updates at the Large Hadron Collider (LHC) create the need for developing more efficient simulation methods. In particular, there exist a demand for a fast simulation of the neutron Zero Degree Calorimeter, where existing Monte Carlo-based methods impose a significant computational burden.

We propose an alternative approach to the problem that leverages machine learning. Our solution utilises neural network classifiers and generative models to directly simulate the response of the calorimeter. In particular, we examine the performance of variational autoencoders and generative adversarial networks, expanding the GAN architecture by an additional regularisation network and a simple, yet effective postprocessing step.

Our approach increases the simulation speed by 2 orders of magnitude while maintaining the high fidelity of the simulation.

Presentation Materials

WMLQ2022 Machine Learning methods for simulating particle response in the Zero Degree Calorimeter at the ALICE experiment, CERN

WMLQ2022 Machine Learning methods for simulating particle response in the Zero Degree Calorimeter at the ALICE experiment, CERN.pdf