Improving inference with matrix elements and machine learning

Brehmer, Johann; Cranmer, Kyle; Kling, F.

doi:10.1142/s0217751x20410080

Title: Improving inference with matrix elements and machine learning

Abstract

Particle physics processes bring together a high-energy amplitude described by quantum field theory and nonperturbative effects and detector interactions described by complex computer simulations. We review some recently developed multivariate inference techniques that leverage this structure and combine matrix-element information with machine learning. Automated by the MadMiner package, the new techniques have been applied to multiple problems in particle physics, allowing for stronger limits than traditional analysis methods and showing their potential to improve the sensitivity of the LHC legacy measurements.

Authors:

Brehmer, Johann ^[1]; Cranmer, Kyle ^[1]; Kling, F. ^[2]

New York Univ. (NYU), NY (United States)
SLAC National Accelerator Lab., Menlo Park, CA (United States)

Publication Date:: 2020-06-04

Research Org.:: SLAC National Accelerator Lab., Menlo Park, CA (United States)

Sponsoring Org.:: USDOE Office of Science (SC)

OSTI Identifier:: 1647463

Grant/Contract Number:: AC02-76SF00515; ACI-1450310; OAC-1836650; PHY-1505463; OAC-1841471; PHY-1620638

Resource Type:: Accepted Manuscript

Journal Name:: International Journal of Modern Physics A

Additional Journal Information:: Journal Volume: 35; Journal Issue: 15n16; Journal ID: ISSN 0217-751X

Publisher:: World Scientific

Country of Publication:: United States

Language:: English

Subject:: 72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS

Citation Formats


                    Brehmer, Johann, Cranmer, Kyle, and Kling, F. Improving inference with matrix elements and machine learning.  United States: N. p., 2020. 
Web.  doi:10.1142/s0217751x20410080.

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                    Brehmer, Johann, Cranmer, Kyle, & Kling, F. Improving inference with matrix elements and machine learning.  United States.  https://doi.org/10.1142/s0217751x20410080

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                    Brehmer, Johann, Cranmer, Kyle, and Kling, F. Thu .  
"Improving inference with matrix elements and machine learning".  United States.  https://doi.org/10.1142/s0217751x20410080.  https://www.osti.gov/servlets/purl/1647463.

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@article{osti_1647463,

  title        = {Improving inference with matrix elements and machine learning},

  author       = {Brehmer, Johann and Cranmer, Kyle and Kling, F.},

  abstractNote = {Particle physics processes bring together a high-energy amplitude described by quantum field theory and nonperturbative effects and detector interactions described by complex computer simulations. We review some recently developed multivariate inference techniques that leverage this structure and combine matrix-element information with machine learning. Automated by the MadMiner package, the new techniques have been applied to multiple problems in particle physics, allowing for stronger limits than traditional analysis methods and showing their potential to improve the sensitivity of the LHC legacy measurements.},

  doi          = {10.1142/s0217751x20410080},

  journal      = {International Journal of Modern Physics A},

  number       = 15n16,

  volume       = 35,

  place        = {United States},

  year         = {2020},

  month        = {6}

}

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https://doi.org/10.1142/s0217751x20410080

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Works referenced in this record:

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journal, July 2003

Agostinelli, S.; Allison, J.; Amako, K.
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Constraining Effective Field Theories with Machine Learning
journal, September 2018

Brehmer, Johann; Cranmer, Kyle; Louppe, Gilles
Physical Review Letters, Vol. 121, Issue 11
DOI: 10.1103/PhysRevLett.121.111801

Better Higgs boson measurements through information geometry
journal, April 2017

Brehmer, Johann; Cranmer, Kyle; Kling, Felix
Physical Review D, Vol. 95, Issue 7
DOI: 10.1103/PhysRevD.95.073002

Better Higgs- $C P$ tests through information geometry
journal, May 2018

Brehmer, Johann; Kling, Felix; Plehn, Tilman
Physical Review D, Vol. 97, Issue 9
DOI: 10.1103/PhysRevD.97.095017

The automated computation of tree-level and next-to-leading order differential cross sections, and their matching to parton shower simulations
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Alwall, J.; Frederix, R.; Frixione, S.
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A brief introduction to PYTHIA 8.1
journal, June 2008

Sjöstrand, Torbjörn; Mrenna, Stephen; Skands, Peter
Computer Physics Communications, Vol. 178, Issue 11
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Title: Improving inference with matrix elements and machine learning

Abstract

Citation Formats

Geant4—a simulation toolkit journal, July 2003

A guide to constraining effective field theories with machine learning journal, September 2018

Dynamical Likelihood Method for Reconstruction of Events with Missing Momentum. I. Method and Toy Models journal, December 1988

Analysis for magnetic moment and electric dipole moment form factors of the top quark via e + e − → tt ¯ journal, April 1992

Constraining Effective Field Theories with Machine Learning journal, September 2018

Better Higgs boson measurements through information geometry journal, April 2017

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The automated computation of tree-level and next-to-leading order differential cross sections, and their matching to parton shower simulations journal, July 2014

A brief introduction to PYTHIA 8.1 journal, June 2008

Geant4—a simulation toolkit
journal, July 2003

A guide to constraining effective field theories with machine learning
journal, September 2018

Dynamical Likelihood Method for Reconstruction of Events with Missing Momentum. I. Method and Toy Models
journal, December 1988

Analysis for magnetic moment and electric dipole moment form factors of the top quark via $e^{+}$ $e^{-}$ → tt ¯
journal, April 1992

Constraining Effective Field Theories with Machine Learning
journal, September 2018

Better Higgs boson measurements through information geometry
journal, April 2017

Better Higgs- $C P$ tests through information geometry
journal, May 2018

The automated computation of tree-level and next-to-leading order differential cross sections, and their matching to parton shower simulations
journal, July 2014

A brief introduction to PYTHIA 8.1
journal, June 2008