End-to-End Physics Event Classification with CMS Open Data: Applying Image-Based Deep Learning to Detector Data for the Direct Classification of Collision Events at the LHC
Abstract
This paper describes the construction of novel end-to-end image-based classifiers that directly leverage low-level simulated detector data to discriminate signal and background processes in pp collision events at the Large Hadron Collider at CERN. To better understand what end-to-end classifiers are capable of learning from the data and to address a number of associated challenges, we distinguish the decay of the standard model Higgs boson into two photons from its leading background sources using high-fidelity simulated CMS Open Data. We demonstrate the ability of end-to-end classifiers to learn from the angular distribution of the photons recorded as electromagnetic showers, their intrinsic shapes, and the energy of their constituent hits, even when the underlying particles are not fully resolved, delivering a clear advantage in such cases over purely kinematics-based classifiers.
- Authors:
-
- Carnegie Mellon Univ., Pittsburgh, PA (United States)
- Univ. of Florida, Gainesville, FL (United States)
- Publication Date:
- Research Org.:
- Carnegie Mellon Univ., Pittsburgh, PA (United States)
- Sponsoring Org.:
- USDOE Office of Science (SC), High Energy Physics (HEP)
- OSTI Identifier:
- 1755518
- Grant/Contract Number:
- SC0010118
- Resource Type:
- Accepted Manuscript
- Journal Name:
- Computing and Software for Big Science
- Additional Journal Information:
- Journal Volume: 4; Journal Issue: 1; Journal ID: ISSN 2510-2036
- Publisher:
- Springer
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS; end-to-end; detector images; machine learning; deep learning; CNN; Resnet; photon ID; event classification; mass sculpting; LHC; CMS; open data; higgs boson
Citation Formats
Andrews, M., Paulini, M., Gleyzer, S., and Poczos, B. End-to-End Physics Event Classification with CMS Open Data: Applying Image-Based Deep Learning to Detector Data for the Direct Classification of Collision Events at the LHC. United States: N. p., 2020.
Web. doi:10.1007/s41781-020-00038-8.
Andrews, M., Paulini, M., Gleyzer, S., & Poczos, B. End-to-End Physics Event Classification with CMS Open Data: Applying Image-Based Deep Learning to Detector Data for the Direct Classification of Collision Events at the LHC. United States. https://doi.org/10.1007/s41781-020-00038-8
Andrews, M., Paulini, M., Gleyzer, S., and Poczos, B. Wed .
"End-to-End Physics Event Classification with CMS Open Data: Applying Image-Based Deep Learning to Detector Data for the Direct Classification of Collision Events at the LHC". United States. https://doi.org/10.1007/s41781-020-00038-8. https://www.osti.gov/servlets/purl/1755518.
@article{osti_1755518,
title = {End-to-End Physics Event Classification with CMS Open Data: Applying Image-Based Deep Learning to Detector Data for the Direct Classification of Collision Events at the LHC},
author = {Andrews, M. and Paulini, M. and Gleyzer, S. and Poczos, B.},
abstractNote = {This paper describes the construction of novel end-to-end image-based classifiers that directly leverage low-level simulated detector data to discriminate signal and background processes in pp collision events at the Large Hadron Collider at CERN. To better understand what end-to-end classifiers are capable of learning from the data and to address a number of associated challenges, we distinguish the decay of the standard model Higgs boson into two photons from its leading background sources using high-fidelity simulated CMS Open Data. We demonstrate the ability of end-to-end classifiers to learn from the angular distribution of the photons recorded as electromagnetic showers, their intrinsic shapes, and the energy of their constituent hits, even when the underlying particles are not fully resolved, delivering a clear advantage in such cases over purely kinematics-based classifiers.},
doi = {10.1007/s41781-020-00038-8},
journal = {Computing and Software for Big Science},
number = 1,
volume = 4,
place = {United States},
year = {Wed Mar 11 00:00:00 EDT 2020},
month = {Wed Mar 11 00:00:00 EDT 2020}
}
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