Shower Separation in Five Dimensions for Highly Granular Calorimeters using Machine Learning

TL;DR

This paper explores neural network models for shower separation in highly granular calorimeters, demonstrating that incorporating timing information improves the accuracy of distinguishing charged and neutral hadron energy deposits, with implications for Particle Flow algorithms.

Contribution

It introduces and compares neural network models trained with and without timing information for shower separation in calorimeters, highlighting the benefits of temporal data in particle clustering.

Findings

Timing information enhances shower separation performance.

Models trained on simulation generalize well to experimental data.

Neural networks tend to misallocate energy to the less energetic shower.

Abstract

To achieve state-of-the-art jet energy resolution for Particle Flow, sophisticated energy clustering algorithms must be developed that can fully exploit available information to separate energy deposits from charged and neutral particles. Three published neural network-based shower separation models were applied to simulation and experimental data to measure the performance of the highly granular CALICE Analogue Hadronic Calorimeter (AHCAL) technological prototype in distinguishing the energy deposited by a single charged and single neutral hadron for Particle Flow. The performance of models trained using only standard spatial and energy and charged track position information from an event was compared to models trained using timing information available from AHCAL, which is expected to improve sensitivity to shower development and, therefore, aid in clustering. Both simulation and experimental data were used to train and test the models and their performances were compared. The best-performing neural network achieved significantly superior event reconstruction when timing information was utilised in training for the case where the charged hadron had more energy than the neutral one, motivating temporally sensitive calorimeters. All models under test were observed to tend to allocate energy deposited by the more energetic of the two showers to the less energetic one. Similar shower reconstruction performance was observed for a model trained on simulation and applied to data and a model trained and applied to data.