End-to-End Event Classification of High-Energy Physics Data

Andrews, M.; Paulini, M.; Gleyzer, S.; Poczos, B.

doi:10.1088/1742-6596/1085/4/042022

Title: End-to-End Event Classification of High-Energy Physics Data

Abstract

Feature extraction algorithms, such as convolutional neural networks, have introduced the possibility of using deep learning to train directly on raw data without the need for rule-based feature engineering. In the context of particle physics, such end-to-end approaches can be used for event classification to learn directly from detector-level data in a way that is completely independent of the high-level physics reconstruction. We demonstrate a technique for building such end-to-end event classifiers to distinguish simulated electromagnetic decays in a high-fidelity model of the CMS Electromagnetic Calorimeter.

Authors:

Andrews, M. ^[1]; Paulini, M. ^[1]; Gleyzer, S. ^[2]; Poczos, B. ^[1]

Carnegie Mellon Univ., Pittsburgh, PA (United States)
Univ. of Florida, Gainesville, FL (United States)

Publication Date:: Thu Oct 18 00:00:00 EDT 2018

Research Org.:: Carnegie Mellon Univ., Pittsburgh, PA (United States)

Sponsoring Org.:: USDOE Office of Science (SC), High Energy Physics (HEP)

OSTI Identifier:: 1755640

Grant/Contract Number:: SC0010118

Resource Type:: Accepted Manuscript

Journal Name:: Journal of Physics. Conference Series

Additional Journal Information:: Journal Volume: 1085; Journal ID: ISSN 1742-6588

Publisher:: IOP Publishing

Country of Publication:: United States

Language:: English

Subject:: 72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS

Citation Formats


                    Andrews, M., Paulini, M., Gleyzer, S., and Poczos, B. End-to-End Event Classification of High-Energy Physics Data.  United States: N. p., 2018. 
Web.  doi:10.1088/1742-6596/1085/4/042022.

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                    Andrews, M., Paulini, M., Gleyzer, S., & Poczos, B. End-to-End Event Classification of High-Energy Physics Data.  United States.  https://doi.org/10.1088/1742-6596/1085/4/042022

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                    Andrews, M., Paulini, M., Gleyzer, S., and Poczos, B. Thu .  
"End-to-End Event Classification of High-Energy Physics Data".  United States.  https://doi.org/10.1088/1742-6596/1085/4/042022.  https://www.osti.gov/servlets/purl/1755640.

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@article{osti_1755640,

  title        = {End-to-End Event Classification of High-Energy Physics Data},

  author       = {Andrews, M. and Paulini, M. and Gleyzer, S. and Poczos, B.},

  abstractNote = {Feature extraction algorithms, such as convolutional neural networks, have introduced the possibility of using deep learning to train directly on raw data without the need for rule-based feature engineering. In the context of particle physics, such end-to-end approaches can be used for event classification to learn directly from detector-level data in a way that is completely independent of the high-level physics reconstruction. We demonstrate a technique for building such end-to-end event classifiers to distinguish simulated electromagnetic decays in a high-fidelity model of the CMS Electromagnetic Calorimeter.},

  doi          = {10.1088/1742-6596/1085/4/042022},

  journal      = {Journal of Physics. Conference Series},

  number       = ,

  volume       = 1085,

  place        = {United States},

  year         = {Thu Oct 18 00:00:00 EDT 2018},

  month        = {Thu Oct 18 00:00:00 EDT 2018}

}

Copy to clipboard

Journal Article:

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Observation of the diphoton decay of the Higgs boson and measurement of its properties
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Works referencing / citing this record:

CapsNets Continuing the Convolutional Quest
text, January 2019

Diefenbacher, Sascha; Frost, Hermann; Kasieczka, Gregor
arXiv
DOI: 10.48550/arxiv.1906.11265

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Title: End-to-End Event Classification of High-Energy Physics Data

Abstract

Citation Formats

Learning representations by back-propagating errors journal, October 1986

Searching for Exotic Particles in High-Energy Physics with Deep Learning text, January 2014

Gradient-based learning applied to document recognition journal, January 1998

Searching for exotic particles in high-energy physics with deep learning journal, July 2014

Long Short-Term Memory journal, November 1997

Deep-learning top taggers or the end of QCD text, January 2017

Observation of the diphoton decay of the Higgs boson and measurement of its properties journal, October 2014

Deep-learning Top Taggers or The End of QCD? text, January 2017

Jet-images — deep learning edition journal, July 2016

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A convolutional neural network neutrino event classifier journal, September 2016

CapsNets Continuing the Convolutional Quest text, January 2019

Learning representations by back-propagating errors
journal, October 1986

Searching for Exotic Particles in High-Energy Physics with Deep Learning
text, January 2014

Gradient-based learning applied to document recognition
journal, January 1998

Searching for exotic particles in high-energy physics with deep learning
journal, July 2014

Long Short-Term Memory
journal, November 1997

Deep-learning top taggers or the end of QCD
text, January 2017

Observation of the diphoton decay of the Higgs boson and measurement of its properties
journal, October 2014

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text, January 2017

Jet-images — deep learning edition
journal, July 2016

Deep-learning top taggers or the end of QCD?
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text, January 2014

A convolutional neural network neutrino event classifier
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CapsNets Continuing the Convolutional Quest
text, January 2019