Interpretable machine learning methods applied to jet background subtraction in heavy-ion collisions

Mengel, Tanner; Steffanic, Patrick; Hughes, Charles; da Silva, Antonio Carlos Oliveira; Nattrass, Christine

doi:10.1103/PhysRevC.108.L021901

Title: Interpretable machine learning methods applied to jet background subtraction in heavy-ion collisions

Abstract

Jet measurements in heavy ion collisions can provide constraints on the properties of the quark gluon plasma, but the kinematic reach is limited by a large, fluctuating background. We present a novel application of symbolic regression to extract a functional representation of a deep neural network trained to subtract background from jets in heavy ion collisions. We show that the deep neural network is approximately the same as a method using the particle multiplicity in a jet. This demonstrates that interpretable machine learning methods can provide insight into underlying physical processes.

Authors:

;

Publication Date:: Tue Aug 22 00:00:00 EDT 2023

Research Org.:: Univ. of Tennessee, Knoxville, TN (United States)

Sponsoring Org.:: USDOE Office of Science (SC), Nuclear Physics (NP)

OSTI Identifier:: 1996309

Alternate Identifier(s):: OSTI ID: 2007766

Grant/Contract Number:: FG02-96ER40982

Resource Type:: Published Article

Journal Name:: Physical Review. C

Additional Journal Information:: Journal Name: Physical Review. C Journal Volume: 108 Journal Issue: 2; Journal ID: ISSN 2469-9985

Publisher:: American Physical Society

Country of Publication:: United States

Language:: English

Subject:: 73 NUCLEAR PHYSICS AND RADIATION PHYSICS; Charged-particle multiplicity; Hard scattering; Jet quenching; Jets & heavy flavor physics; Particle correlations & fluctuations; Quark-gluon plasma; Artificial neural networks; Deep learning; Machine learning; Nuclear data analysis & compilation

Citation Formats


                    Mengel, Tanner, Steffanic, Patrick, Hughes, Charles, da Silva, Antonio Carlos Oliveira, and Nattrass, Christine. Interpretable machine learning methods applied to jet background subtraction in heavy-ion collisions.  United States: N. p., 2023. 
Web.  doi:10.1103/PhysRevC.108.L021901.

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                    Mengel, Tanner, Steffanic, Patrick, Hughes, Charles, da Silva, Antonio Carlos Oliveira, & Nattrass, Christine. Interpretable machine learning methods applied to jet background subtraction in heavy-ion collisions.  United States.  https://doi.org/10.1103/PhysRevC.108.L021901

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                    Mengel, Tanner, Steffanic, Patrick, Hughes, Charles, da Silva, Antonio Carlos Oliveira, and Nattrass, Christine. Tue .  
"Interpretable machine learning methods applied to jet background subtraction in heavy-ion collisions".  United States.  https://doi.org/10.1103/PhysRevC.108.L021901.

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@article{osti_1996309,

  title        = {Interpretable machine learning methods applied to jet background subtraction in heavy-ion collisions},

  author       = {Mengel, Tanner and Steffanic, Patrick and Hughes, Charles and da Silva, Antonio Carlos Oliveira and Nattrass, Christine},

  abstractNote = {Jet measurements in heavy ion collisions can provide constraints on the properties of the quark gluon plasma, but the kinematic reach is limited by a large, fluctuating background. We present a novel application of symbolic regression to extract a functional representation of a deep neural network trained to subtract background from jets in heavy ion collisions. We show that the deep neural network is approximately the same as a method using the particle multiplicity in a jet. This demonstrates that interpretable machine learning methods can provide insight into underlying physical processes.},

  doi          = {10.1103/PhysRevC.108.L021901},

  journal      = {Physical Review. C},

  number       = 2,

  volume       = 108,

  place        = {United States},

  year         = {Tue Aug 22 00:00:00 EDT 2023},

  month        = {Tue Aug 22 00:00:00 EDT 2023}

}

Copy to clipboard

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Title: Interpretable machine learning methods applied to jet background subtraction in heavy-ion collisions

Abstract

Citation Formats

Measurements of inclusive jet spectra in p p and central Pb-Pb collisions at s N N = 5.02 TeV journal, March 2020

Measurement of inclusive jet cross sections in p p and PbPb collisions at s NN = 2.76 TeV journal, July 2017

Quark–gluon plasma and color glass condensate at RHIC? The perspective from the BRAHMS experiment journal, August 2005

Higher harmonic flow coefficients of identified hadrons in Pb-Pb collisions at s N N = 2.76 $$ \sqrt{s_{\mathrm{NN}}}=2.76 $$ TeV journal, September 2016

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Measurement of the higher-order anisotropic flow coefficients for identified hadrons in Au + Au collisions at s N N = 200 GeV journal, May 2016

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Measurement of jet suppression in central Pb–Pb collisions at s NN = 2.76 TeV journal, June 2015

Measurement of jet fragmentation in Pb+Pb and p p collisions at s N N = 5.02 TeV with the ATLAS detector journal, August 2018

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Quark–gluon plasma and color glass condensate at RHIC? The perspective from the BRAHMS experiment
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Higher harmonic flow coefficients of identified hadrons in Pb-Pb collisions at s N N = 2.76 $$ \sqrt{s_{\mathrm{NN}}}=2.76 $$ TeV
journal, September 2016

The catchment area of jets
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journal, November 2020

A multidimensional unfolding method based on Bayes' theorem
journal, August 1995

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journal, January 2008

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journal, June 2015

Measurement of jet fragmentation in Pb+Pb and $p p$ collisions at $\sqrt{s_{N N}} = 5.02$ TeV with the ATLAS detector
journal, August 2018

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journal, August 2014

Spectra and ratios of identified particles in Au+Au and $d$ +Au collisions at $\sqrt{s_{N N}} = 200$ GeV
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journal, October 1993

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