Deep Generative Models for Proton Zero Degree Calorimeter Simulations in ALICE, CERN

TL;DR

This paper introduces an advanced deep learning approach using a specialized GAN model to efficiently simulate the proton Zero Degree Calorimeter responses in the ALICE experiment, reducing computational costs.

Contribution

We develop a novel SDI-GAN architecture with regularization and an auxiliary regressor to improve simulation accuracy and diversity for particle detector responses.

Findings

Significant speedup over traditional Monte-Carlo simulations

Enhanced modeling of calorimeter response intensities

Improved spatial fidelity of generated data

Abstract

Simulating detector responses is a crucial part of understanding the inner-workings of particle collisions in the Large Hadron Collider at CERN. The current reliance on statistical Monte-Carlo simulations strains CERN's computational grid, underscoring the urgency for more efficient alternatives. Addressing these challenges, recent proposals advocate for generative machine learning methods. In this study, we present an innovative deep learning simulation approach tailored for the proton Zero Degree Calorimeter in the ALICE experiment. Leveraging a Generative Adversarial Network model with Selective Diversity Increase loss, we directly simulate calorimeter responses. To enhance its capabilities in modeling a broad range of calorimeter response intensities, we expand the SDI-GAN architecture with additional regularization. Moreover, to improve the spatial fidelity of the generated data, we introduce an auxiliary regressor network. Our method offers a significant speedup when comparing to the traditional Monte-Carlo based approaches.