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Title: Weakly supervised classification in high energy physics

Abstract

As machine learning algorithms become increasingly sophisticated to exploit subtle features of the data, they often become more dependent on simulations. Here, this paper presents a new approach called weakly supervised classification in which class proportions are the only input into the machine learning algorithm. Using one of the most challenging binary classification tasks in high energy physics $-$ quark versus gluon tagging $-$ we show that weakly supervised classification can match the performance of fully supervised algorithms. Furthermore, by design, the new algorithm is insensitive to any mis-modeling of discriminating features in the data by the simulation. Weakly supervised classification is a general procedure that can be applied to a wide variety of learning problems to boost performance and robustness when detailed simulations are not reliable or not available.

Authors:
 [1];  [2]; ORCiD logo [3];  [3]
  1. Stanford Univ., CA (United States). Dept. of Physics
  2. Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States). Physics Division
  3. SLAC National Accelerator Lab., Menlo Park, CA (United States)
Publication Date:
Research Org.:
SLAC National Accelerator Lab., Menlo Park, CA (United States); Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
Sponsoring Org.:
USDOE
OSTI Identifier:
1369428
Alternate Identifier(s):
OSTI ID: 1379863
Grant/Contract Number:  
AC02-76SF00515; AC02-05CH11231
Resource Type:
Journal Article: Accepted Manuscript
Journal Name:
Journal of High Energy Physics (Online)
Additional Journal Information:
Journal Name: Journal of High Energy Physics (Online); Journal Volume: 2017; Journal Issue: 5; Journal ID: ISSN 1029-8479
Publisher:
Springer Berlin
Country of Publication:
United States
Language:
English
Subject:
72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS; 97 MATHEMATICS AND COMPUTING; Jets

Citation Formats

Dery, Lucio Mwinmaarong, Nachman, Benjamin, Rubbo, Francesco, and Schwartzman, Ariel. Weakly supervised classification in high energy physics. United States: N. p., 2017. Web. doi:10.1007/JHEP05(2017)145.
Dery, Lucio Mwinmaarong, Nachman, Benjamin, Rubbo, Francesco, & Schwartzman, Ariel. Weakly supervised classification in high energy physics. United States. doi:10.1007/JHEP05(2017)145.
Dery, Lucio Mwinmaarong, Nachman, Benjamin, Rubbo, Francesco, and Schwartzman, Ariel. Mon . "Weakly supervised classification in high energy physics". United States. doi:10.1007/JHEP05(2017)145. https://www.osti.gov/servlets/purl/1369428.
@article{osti_1369428,
title = {Weakly supervised classification in high energy physics},
author = {Dery, Lucio Mwinmaarong and Nachman, Benjamin and Rubbo, Francesco and Schwartzman, Ariel},
abstractNote = {As machine learning algorithms become increasingly sophisticated to exploit subtle features of the data, they often become more dependent on simulations. Here, this paper presents a new approach called weakly supervised classification in which class proportions are the only input into the machine learning algorithm. Using one of the most challenging binary classification tasks in high energy physics $-$ quark versus gluon tagging $-$ we show that weakly supervised classification can match the performance of fully supervised algorithms. Furthermore, by design, the new algorithm is insensitive to any mis-modeling of discriminating features in the data by the simulation. Weakly supervised classification is a general procedure that can be applied to a wide variety of learning problems to boost performance and robustness when detailed simulations are not reliable or not available.},
doi = {10.1007/JHEP05(2017)145},
journal = {Journal of High Energy Physics (Online)},
number = 5,
volume = 2017,
place = {United States},
year = {Mon May 01 00:00:00 EDT 2017},
month = {Mon May 01 00:00:00 EDT 2017}
}

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Cited by: 3 works
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