Shower Separation in Five Dimensions for Highly Granular Calorimeters using Machine Learning
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.
Cite
@article{arxiv.2407.00178,
title = {Shower Separation in Five Dimensions for Highly Granular Calorimeters using Machine Learning},
author = {S. Lai and J. Utehs and A. Wilhahn and M. C. Fouz and O. Bach and E. Brianne and A. Ebrahimi and K. Gadow and P. Göttlicher and O. Hartbrich and D. Heuchel and A. Irles and K. Krüger and J. Kvasnicka and S. Lu and C. Neubüser and A. Provenza and M. Reinecke and F. Sefkow and S. Schuwalow and M. De Silva and Y. Sudo and H. L. Tran and L. Liu and R. Masuda and T. Murata and W. Ootani and T. Seino and T. Takatsu and N. Tsuji and R. Pöschl and F. Richard and D. Zerwas and F. Hummer and F. Simon and V. Boudry and J-C. Brient and J. Nanni and H. Videau and E. Buhmann and E. Garutti and S. Huck and G. Kasieczka and S. Martens and J. Rolph and J. Wellhausen and B. Bilki and D. Northacker and Y. Onel and L. Emberger and C. Graf},
journal= {arXiv preprint arXiv:2407.00178},
year = {2024}
}