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Title: Exploring End-to-end Deep Learning Applications for Event Classification at CMS

Abstract

An essential part of new physics searches at the Large Hadron Collider (LHC) at CERN involves event classification, or distinguishing potential signal events from those coming from background processes. Current machine learning techniques accomplish this using traditional hand-engineered features like particle 4-momenta, motivated by our understanding of particle decay phenomenology. While such techniques have proven useful for simple decays, they are highly dependent on our ability to model all aspects of the phenomenology and detector response. Meanwhile, powerful deep learning algorithms are capable of not only training on high-level features, but of performing feature extraction. In computer vision, convolutional neural networks have become the state-of-the-art for many applications. Motivated by their success, we apply deep learning algorithms to low-level detector data from the 2012 CMS Simulated Open Data to directly learn useful features, in what we call, end-to-end event classification. We demonstrate the power of this approach in the context of a physics search and offer solutions to some of the inherent challenges, such as image construction, image sparsity, combining multiple sub-detectors, and de-correlating the classifier from the search observable, among others.

Authors:
 [1];  [1];  [2];  [1]
  1. Carnegie Mellon Univ., Pittsburgh, PA (United States)
  2. 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:
1755630
Grant/Contract Number:  
SC0010118
Resource Type:
Accepted Manuscript
Journal Name:
EPJ Web of Conferences
Additional Journal Information:
Journal Volume: 214; Journal ID: ISSN 2100-014X
Publisher:
EDP Sciences
Country of Publication:
United States
Language:
English
Subject:
72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS

Citation Formats

Andrews, Michael, Paulini, Manfred, Gleyzer, Sergei, and Poczos, Barnabas. Exploring End-to-end Deep Learning Applications for Event Classification at CMS. United States: N. p., 2019. Web. https://doi.org/10.1051/epjconf/201921406031.
Andrews, Michael, Paulini, Manfred, Gleyzer, Sergei, & Poczos, Barnabas. Exploring End-to-end Deep Learning Applications for Event Classification at CMS. United States. https://doi.org/10.1051/epjconf/201921406031
Andrews, Michael, Paulini, Manfred, Gleyzer, Sergei, and Poczos, Barnabas. Tue . "Exploring End-to-end Deep Learning Applications for Event Classification at CMS". United States. https://doi.org/10.1051/epjconf/201921406031. https://www.osti.gov/servlets/purl/1755630.
@article{osti_1755630,
title = {Exploring End-to-end Deep Learning Applications for Event Classification at CMS},
author = {Andrews, Michael and Paulini, Manfred and Gleyzer, Sergei and Poczos, Barnabas},
abstractNote = {An essential part of new physics searches at the Large Hadron Collider (LHC) at CERN involves event classification, or distinguishing potential signal events from those coming from background processes. Current machine learning techniques accomplish this using traditional hand-engineered features like particle 4-momenta, motivated by our understanding of particle decay phenomenology. While such techniques have proven useful for simple decays, they are highly dependent on our ability to model all aspects of the phenomenology and detector response. Meanwhile, powerful deep learning algorithms are capable of not only training on high-level features, but of performing feature extraction. In computer vision, convolutional neural networks have become the state-of-the-art for many applications. Motivated by their success, we apply deep learning algorithms to low-level detector data from the 2012 CMS Simulated Open Data to directly learn useful features, in what we call, end-to-end event classification. We demonstrate the power of this approach in the context of a physics search and offer solutions to some of the inherent challenges, such as image construction, image sparsity, combining multiple sub-detectors, and de-correlating the classifier from the search observable, among others.},
doi = {10.1051/epjconf/201921406031},
journal = {EPJ Web of Conferences},
number = ,
volume = 214,
place = {United States},
year = {2019},
month = {9}
}

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