TY - JOUR
T1 - Calorimetry with deep learning
T2 - particle simulation and reconstruction for collider physics
AU - Belayneh, Dawit
AU - Carminati, Federico
AU - Farbin, Amir
AU - Hooberman, Benjamin
AU - Khattak, Gulrukh
AU - Liu, Miaoyuan
AU - Liu, Junze
AU - Olivito, Dominick
AU - Pacela, Vitória Barin
AU - Pierini, Maurizio
AU - Schwing, Alexander
AU - Spiropulu, Maria
AU - Vallecorsa, Sofia
AU - Vlimant, Jean Roch
AU - Wei, Wei
AU - Zhang, Matt
N1 - Funding Information:
The authors thank Daniel Weitekamp for providing us with the event generator used in regression training. We also thank Andre Sailer from the CERN CLIC group, for guiding us on how to generate the single-particle samples. This project is partially supported by the United States Department of Energy, Office of High Energy Physics Research under Caltech Contract No. DE-SC0011925. JR is partially supported by the Office of High Energy Physics HEP-Computation. M. P. is supported by the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation program (grant agreement n o \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\hbox {n}^o$$\end{document} 772369). This research is also partially supported by the Zhejiang University/University of Illinois Institute Collaborative Research Program (award 083650). This research is part of the Blue Waters sustained-petascale computing project, which is supported by the National Science Foundation (awards OCI-0725070 and ACI-1238993) and the State of Illinois. Blue Waters is a joint effort of the University of Illinois at Urbana-Champaign and its National Center for Supercomputing Applications. Part of this work was conducted at “ iBanks ”, the AI GPU cluster at Caltech. We acknowledge NVIDIA, SuperMicro and the Kavli Foundation for their support of “ iBanks ”. The authors are grateful to Caltech and the Kavli Foundation for their support of undergraduate student research in cross-cutting areas of machine learning and domain sciences.
PY - 2020/7/1
Y1 - 2020/7/1
N2 - Using detailed simulations of calorimeter showers as training data, we investigate the use of deep learning algorithms for the simulation and reconstruction of single isolated particles produced in high-energy physics collisions. We train neural networks on single-particle 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.
AB - Using detailed simulations of calorimeter showers as training data, we investigate the use of deep learning algorithms for the simulation and reconstruction of single isolated particles produced in high-energy physics collisions. We train neural networks on single-particle 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.
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U2 - 10.1140/epjc/s10052-020-8251-9
DO - 10.1140/epjc/s10052-020-8251-9
M3 - Article
AN - SCOPUS:85088840095
VL - 80
JO - European Physical Journal C
JF - European Physical Journal C
SN - 1434-6044
IS - 7
M1 - 688
ER -