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}
}
https://doi.org/10.1103/PhysRevD.97.056009
Web of Science
Figures / Tables:
Works referenced in this record:
uBoost: a boosting method for producing uniform selection efficiencies from multivariate classifiers
journal, December 2013
- Stevens, J.; Williams, M.
- Journal of Instrumentation, Vol. 8, Issue 12
New approaches for boosting to uniformity
journal, March 2015
- Rogozhnikov, A.; Bukva, A.; Gligorov, V.
- Journal of Instrumentation, Vol. 10, Issue 03
The phenomenology of extra neutral gauge bosons
journal, August 1999
- Leike, A.
- Physics Reports, Vol. 317, Issue 3-4
Jet flavor classification in high-energy physics with deep neural networks
journal, December 2016
- Guest, Daniel; Collado, Julian; Baldi, Pierre
- Physical Review D, Vol. 94, Issue 11
Playing tag with ANN: boosted top identification with pattern recognition
journal, July 2015
- Almeida, Leandro G.; Backović, Mihailo; Cliche, Mathieu
- Journal of High Energy Physics, Vol. 2015, Issue 7
Thinking outside the ROCs: Designing Decorrelated Taggers (DDT) for jet substructure
journal, May 2016
- Dolen, James; Harris, Philip; Marzani, Simone
- Journal of High Energy Physics, Vol. 2016, Issue 5
Decorrelated jet substructure tagging using adversarial neural networks
journal, October 2017
- Shimmin, Chase; Sadowski, Peter; Baldi, Pierre
- Physical Review D, Vol. 96, Issue 7
Searching for exotic particles in high-energy physics with deep learning
journal, July 2014
- Baldi, P.; Sadowski, P.; Whiteson, D.
- Nature Communications, Vol. 5, Issue 1
Jet substructure classification in high-energy physics with deep neural networks
journal, May 2016
- Baldi, Pierre; Bauer, Kevin; Eng, Clara
- Physical Review D, Vol. 93, Issue 9
How much information is in a jet?
journal, June 2017
- Datta, Kaustuv; Larkoski, Andrew
- Journal of High Energy Physics, Vol. 2017, Issue 6
Estimating the Bayes error rate through classifier combining
conference, January 1996
- Tumer, K.; Ghosh, J.
- Proceedings of 13th International Conference on Pattern Recognition
Identification of boosted, hadronically decaying W bosons and comparisons with ATLAS data taken at $$\sqrt{s} = 8$$ s = 8 TeV
journal, March 2016
- Aad, G.; Abbott, B.; Abdallah, J.
- The European Physical Journal C, Vol. 76, Issue 3
Corrigendum to “Measurements of Higgs boson production and couplings in diboson final states with the ATLAS detector at the LHC” [Phys. Lett. B 726 (1–3) (2013) 88]
journal, June 2014
- Collaboration, Atlas
- Physics Letters B, Vol. 734, p. 406
Evidence for the 125 GeV Higgs boson decaying to a pair of τ leptons
journal, May 2014
- Chatrchyan, S.; Khachatryan, V.; Sirunyan, A. M.
- Journal of High Energy Physics, Vol. 2014, Issue 5
Enhanced Higgs Boson to Search with Deep Learning
journal, March 2015
- Baldi, P.; Sadowski, P.; Whiteson, D.
- Physical Review Letters, Vol. 114, Issue 11
Jet-images — deep learning edition
journal, July 2016
- de Oliveira, Luke; Kagan, Michael; Mackey, Lester
- Journal of High Energy Physics, Vol. 2016, Issue 7
Measurements of Higgs boson production and couplings in diboson final states with the ATLAS detector at the LHC
journal, October 2013
- Aad, G.; Abajyan, T.; Abbott, B.
- Physics Letters B, Vol. 726, Issue 1-3
Deep-learning top taggers or the end of QCD?
journal, May 2017
- Kasieczka, Gregor; Plehn, Tilman; Russell, Michael
- Journal of High Energy Physics, Vol. 2017, Issue 5
The automated computation of tree-level and next-to-leading order differential cross sections, and their matching to parton shower simulations
journal, July 2014
- Alwall, J.; Frederix, R.; Frixione, S.
- Journal of High Energy Physics, Vol. 2014, Issue 7
Approximation by superpositions of a sigmoidal function
journal, December 1989
- Cybenko, G.
- Mathematics of Control, Signals, and Systems, Vol. 2, Issue 4
A generic anti-QCD jet tagger
journal, November 2017
- Aguilar-Saavedra, J. A.; Collins, Jack; Mishra, Rashmish K.
- Journal of High Energy Physics, Vol. 2017, Issue 11
FeynRules 2.0 — A complete toolbox for tree-level phenomenology
journal, August 2014
- Alloul, Adam; Christensen, Neil D.; Degrande, Céline
- Computer Physics Communications, Vol. 185, Issue 8
Evidence for the 125 GeV Higgs boson decaying to a pair of τ leptons
text, January 2014
- Chatrchyan, S.; Khachatryan, V.; Sirunyan, A. M.
- RWTH Aachen University
Works referencing / citing this record:
Quark jet versus gluon jet: fully-connected neural networks with high-level features
journal, June 2019
- Luo, Hui; Luo, Ming-Xing; Wang, Kai
- Science China Physics, Mechanics & Astronomy, Vol. 62, Issue 9
Constituents Phase Reconstruction through Applied Machine Learning in Nanoindentation Mapping Data of Mortar Surface
journal, June 2019
- Koumoulos, Elias P.; Paraskevoudis, Konstantinos; Charitidis, Costas A.
- Journal of Composites Science, Vol. 3, Issue 3
Quark jet versus gluon jet: fully-connected neural networks with high-level features
journal, June 2019
- Luo, Hui; Luo, Ming-Xing; Wang, Kai
- Science China Physics, Mechanics & Astronomy, Vol. 62, Issue 9
Constituents Phase Reconstruction through Applied Machine Learning in Nanoindentation Mapping Data of Mortar Surface
journal, June 2019
- Koumoulos, Elias P.; Paraskevoudis, Konstantinos; Charitidis, Costas A.
- Journal of Composites Science, Vol. 3, Issue 3
Identifying the Relevant Dependencies of the Neural Network Response on Characteristics of the Input Space
journal, September 2018
- Wunsch, Stefan; Friese, Raphael; Wolf, Roger
- Computing and Software for Big Science, Vol. 2, Issue 1
Supervised Deep Learning in High Energy Phenomenology: a Mini Review
journal, August 2019
- Abdughani, Murat; Ren, Jie; Wu, Lei
- Communications in Theoretical Physics, Vol. 71, Issue 8
Solving differential equations with neural networks: Applications to the calculation of cosmological phase transitions
journal, July 2019
- Piscopo, Maria Laura; Spannowsky, Michael; Waite, Philip
- Physical Review D, Vol. 100, Issue 1
Machine learning uncertainties with adversarial neural networks
journal, January 2019
- Englert, Christoph; Galler, Peter; Harris, Philip
- The European Physical Journal C, Vol. 79, Issue 1
Guiding new physics searches with unsupervised learning
journal, March 2019
- De Simone, Andrea; Jacques, Thomas
- The European Physical Journal C, Vol. 79, Issue 4
Quark-gluon tagging: Machine learning vs detector
journal, January 2019
- Kasieczka, Gregor; Kiefer, Nicholas; Plehn, Tilman
- SciPost Physics, Vol. 6, Issue 6
Deep-learning jets with uncertainties and more
journal, January 2020
- Bollweg, Sven; Haussmann, Manuel; Kasieczka, Gregor
- SciPost Physics, Vol. 8, Issue 1
CapsNets continuing the convolutional quest
journal, January 2020
- Diefenbacher, Sascha; Frost, Hermann; Kasieczka, Gregor
- SciPost Physics, Vol. 8, Issue 2
Figures / Tables found in this record: