Machine Learning methods for simulating particle response in the Zero Degree Calorimeter at the ALICE experiment, CERN
This addresses the need for more efficient simulation methods in High Energy Physics at CERN, where current methods use over half of the computing power, but it is incremental as it applies existing ML techniques to a specific domain problem.
The paper tackled the computational burden of simulating particle response in the Zero Degree Calorimeter at CERN by proposing a machine learning approach using neural networks, which increased simulation speed by 2 orders of magnitude while maintaining high fidelity.
Currently, over half of the computing power at CERN GRID 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 exists 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.