Electron Energy Regression in the CMS High-Granularity Calorimeter Prototype

TL;DR

This paper introduces a new dataset and machine learning approach for reconstructing electron energies in a novel high-granularity calorimeter prototype at CERN, aiming to improve event reconstruction efficiency.

Contribution

It provides a publicly available simulated dataset and demonstrates machine learning techniques for accurate electron energy reconstruction in a complex detector environment.

Findings

Successful reconstruction of electron energy using ML methods

Dataset enables further research in calorimeter data analysis

Potential for improved event reconstruction accuracy

Abstract

We present a new publicly available dataset that contains simulated data of a novel calorimeter to be installed at the CERN Large Hadron Collider. This detector will have more than six-million channels with each channel capable of position, ionisation and precision time measurement. Reconstructing these events in an efficient way poses an immense challenge which is being addressed with the latest machine learning techniques. As part of this development a large prototype with 12,000 channels was built and a beam of high-energy electrons incident on it. Using machine learning methods we have reconstructed the energy of incident electrons from the energies of three-dimensional hits, which is known to some precision. By releasing this data publicly we hope to encourage experts in the application of machine learning to develop efficient and accurate image reconstruction of these electrons.