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Title: What is the machine learning?

Abstract

Applications of machine learning tools to problems of physical interest are often criticized for producing sensitivity at the expense of transparency. To address this concern, we explore a data planing procedure for identifying combinations of variables—aided by physical intuition—that can discriminate signal from background. Weights are introduced to smooth away the features in a given variable(s). New networks are then trained on this modified data. Observed decreases in sensitivity diagnose the variable’s discriminating power. Planing also allows the investigation of the linear versus nonlinear nature of the boundaries between signal and background. We demonstrate the efficacy of this approach using a toy example, followed by an application to an idealized heavy resonance scenario at the Large Hadron Collider. By unpacking the information being utilized by these algorithms, this method puts in context what it means for a machine to learn.

Authors:
; ;
Publication Date:
Research Org.:
Univ. of Oregon, Eugene, OR (United States)
Sponsoring Org.:
USDOE Office of Science (SC), High Energy Physics (HEP)
OSTI Identifier:
1425995
Alternate Identifier(s):
OSTI ID: 1498658; OSTI ID: 1600035
Grant/Contract Number:  
SC0011640; SC0018191
Resource Type:
Published Article
Journal Name:
Physical Review D
Additional Journal Information:
Journal Name: Physical Review D Journal Volume: 97 Journal Issue: 5; Journal ID: ISSN 2470-0010
Publisher:
American Physical Society
Country of Publication:
United States
Language:
English
Subject:
72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS; hypothetical gauge bosons; artificial neural networks; machine learning

Citation Formats

Chang, Spencer, Cohen, Timothy, and Ostdiek, Bryan. What is the machine learning?. United States: N. p., 2018. Web. doi:10.1103/PhysRevD.97.056009.
Chang, Spencer, Cohen, Timothy, & Ostdiek, Bryan. What is the machine learning?. United States. https://doi.org/10.1103/PhysRevD.97.056009
Chang, Spencer, Cohen, Timothy, and Ostdiek, Bryan. Tue . "What is the machine learning?". United States. https://doi.org/10.1103/PhysRevD.97.056009.
@article{osti_1425995,
title = {What is the machine learning?},
author = {Chang, Spencer and Cohen, Timothy and Ostdiek, Bryan},
abstractNote = {Applications of machine learning tools to problems of physical interest are often criticized for producing sensitivity at the expense of transparency. To address this concern, we explore a data planing procedure for identifying combinations of variables—aided by physical intuition—that can discriminate signal from background. Weights are introduced to smooth away the features in a given variable(s). New networks are then trained on this modified data. Observed decreases in sensitivity diagnose the variable’s discriminating power. Planing also allows the investigation of the linear versus nonlinear nature of the boundaries between signal and background. We demonstrate the efficacy of this approach using a toy example, followed by an application to an idealized heavy resonance scenario at the Large Hadron Collider. By unpacking the information being utilized by these algorithms, this method puts in context what it means for a machine to learn.},
doi = {10.1103/PhysRevD.97.056009},
journal = {Physical Review D},
number = 5,
volume = 97,
place = {United States},
year = {Tue Mar 13 00:00:00 EDT 2018},
month = {Tue Mar 13 00:00:00 EDT 2018}
}

Journal Article:
Free Publicly Available Full Text
Publisher's Version of Record
https://doi.org/10.1103/PhysRevD.97.056009

Citation Metrics:
Cited by: 48 works
Citation information provided by
Web of Science

Figures / Tables:

Figure 1 Figure 1: (Left panel) The machine is trained using rectilinear coordinates to distinguish blue and red as defined by the displayed training data. (Right panel) The classifier output ranges from blue to red.

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