Calorimetry with Deep Learning: Particle Simulation and Reconstruction for Collider Physics

TL;DR

This paper explores deep learning models trained on calorimeter shower data to improve particle simulation and reconstruction in collider physics, offering faster and more accurate methods than traditional algorithms.

Contribution

It introduces end-to-end reconstruction and generative neural networks for calorimeter data, demonstrating significant performance improvements and applicability across different detector geometries.

Findings

Deep learning models outperform traditional algorithms in simulation accuracy.

Models are adaptable to various detector geometries.

Proposed methods are computationally efficient for collider experiments.

Abstract

Using detailed simulations of calorimeter showers as training data, we investigate the use of deep learning algorithms for the simulation and reconstruction of particles produced in high-energy physics collisions. We train neural networks on shower data at the calorimeter-cell level, and show significant improvements for simulation and reconstruction when using these networks compared to methods which rely on currently-used state-of-the-art algorithms. We define two models: an end-to-end reconstruction network which performs simultaneous particle identification and energy regression of particles when given calorimeter shower data, and a generative network which can provide reasonable modeling of calorimeter showers for different particle types at specified angles and energies. We investigate the optimization of our models with hyperparameter scans. Furthermore, we demonstrate the applicability of the reconstruction model to shower inputs from other detector geometries, specifically ATLAS-like and CMS-like geometries. These networks can serve as fast and computationally light methods for particle shower simulation and reconstruction for current and future experiments at particle colliders.