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Calorimetry with deep learning: particle simulation and reconstruction for collider physics

Belayneh, Dawit and Carminati, Federico and Farbin, Amir and Hooberman, Benjamin and Khattak, Gulrukh and Liu, Miaoyuan and Liu, Junze and Olivito, Dominick and Pacela, Vitória Barin and Pierini, Maurizio and Schwing, Alexander and Spiropulu, Maria and Vallecorsa, Sofia and Vlimant, Jean-Roch and Wei, Wei and Zhang, Matt (2020) Calorimetry with deep learning: particle simulation and reconstruction for collider physics. European Physical Journal. C, Particles and Fields, 80 (7). Art. No. 688. ISSN 1434-6044. https://resolver.caltech.edu/CaltechAUTHORS:20200803-133551487

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Abstract

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.


Item Type:Article
Related URLs:
URLURL TypeDescription
https://doi.org/10.1140/epjc/s10052-020-8251-9DOIArticle
https://arxiv.org/abs/1912.06794arXivDiscussion Paper
https://zenodo.org/communities/mpp-hepRelated ItemCode
ORCID:
AuthorORCID
Pierini, Maurizio0000-0003-1939-4268
Spiropulu, Maria0000-0001-8172-7081
Vlimant, Jean-Roch0000-0002-9705-101X
Additional Information:© The Author(s) 2020. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. Funded by SCOAP3. Received 08 January 2020; Accepted 16 July 2020; Published 31 July 2020. 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 no 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. Data Availability Statement: This manuscript has no associated data or the data will not be deposited. [Authors’ comment: The data utilized for this study is publicly available on Zenodo at https://zenodo.org/communities/mpp-hep.]
Funders:
Funding AgencyGrant Number
Department of Energy (DOE)DE-SC0011925
European Research Council (ERC)772369
Zhejiang University083650
University of IllinoisUNSPECIFIED
NSFOCI-0725070
NSFACI-1238993
State of IllinoisUNSPECIFIED
NVIDIA CorporationUNSPECIFIED
SuperMicro CorporationUNSPECIFIED
Kavli FoundationUNSPECIFIED
SCOAP3UNSPECIFIED
Issue or Number:7
Record Number:CaltechAUTHORS:20200803-133551487
Persistent URL:https://resolver.caltech.edu/CaltechAUTHORS:20200803-133551487
Official Citation:Belayneh, D., Carminati, F., Farbin, A. et al. Calorimetry with deep learning: particle simulation and reconstruction for collider physics. Eur. Phys. J. C 80, 688 (2020). https://doi.org/10.1140/epjc/s10052-020-8251-9
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:104704
Collection:CaltechAUTHORS
Deposited By: Tony Diaz
Deposited On:03 Aug 2020 20:53
Last Modified:03 Aug 2020 20:53

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