Mass agnostic jet taggers

Bradshaw, Layne; Mishra, Rashmish K.; Mitridate, Andrea; Ostdiek, Bryan

doi:10.21468/SciPostPhys.8.1.011

Title: Mass agnostic jet taggers

Abstract

Searching for new physics in large data sets needs a balance between two competing effects—signal identification vs background distortion. In this work, we perform a systematic study of both single variable and multivariate jet tagging methods that aim for this balance. The methods preserve the shape of the background distribution by either augmenting the training procedure or the data itself. Multiple quantitative metrics to compare the methods are considered, for tagging 2-, 3-, or 4-prong jets from the QCD background. This is the first study to show that the data augmentation techniques of Planing and PCA based scaling deliver similar performance as the augmented training techniques of Adversarial NN and uBoost, but are both easier to implement and computationally cheaper.

Authors:

Bradshaw, Layne ^[1]; Mishra, Rashmish K. ^[2]; Mitridate, Andrea ^[2]; Ostdiek, Bryan ^[1]

University of Oregon
National Institute for Nuclear Physics, Scuola Normale Superiore di Pisa

Publication Date:: 2020-01-24

Research Org.:: Univ. of Oregon, Eugene, OR (United States)

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

OSTI Identifier:: 1594194

Alternate Identifier(s):: OSTI ID: 1802234

Grant/Contract Number:: SC0011640

Resource Type:: Published Article

Journal Name:: SciPost Physics Proceedings

Additional Journal Information:: Journal Name: SciPost Physics Proceedings Journal Volume: 8 Journal Issue: 1; Journal ID: ISSN 2542-4653

Publisher:: Stichting SciPost

Country of Publication:: Netherlands

Language:: English

Subject:: 71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSICS; Physics

Citation Formats


                    Bradshaw, Layne, Mishra, Rashmish K., Mitridate, Andrea, and Ostdiek, Bryan. Mass agnostic jet taggers.  Netherlands: N. p., 2020. 
Web.  doi:10.21468/SciPostPhys.8.1.011.

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                    Bradshaw, Layne, Mishra, Rashmish K., Mitridate, Andrea, & Ostdiek, Bryan. Mass agnostic jet taggers.  Netherlands.  https://doi.org/10.21468/SciPostPhys.8.1.011

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                    Bradshaw, Layne, Mishra, Rashmish K., Mitridate, Andrea, and Ostdiek, Bryan. Fri .  
"Mass agnostic jet taggers".  Netherlands.  https://doi.org/10.21468/SciPostPhys.8.1.011.

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@article{osti_1594194,

  title        = {Mass agnostic jet taggers},

  author       = {Bradshaw, Layne and Mishra, Rashmish K. and Mitridate, Andrea and Ostdiek, Bryan},

  abstractNote = {Searching for new physics in large data sets needs a balance between two competing effects—signal identification vs background distortion. In this work, we perform a systematic study of both single variable and multivariate jet tagging methods that aim for this balance. The methods preserve the shape of the background distribution by either augmenting the training procedure or the data itself. Multiple quantitative metrics to compare the methods are considered, for tagging 2-, 3-, or 4-prong jets from the QCD background. This is the first study to show that the data augmentation techniques of Planing and PCA based scaling deliver similar performance as the augmented training techniques of Adversarial NN and uBoost, but are both easier to implement and computationally cheaper.},

  doi          = {10.21468/SciPostPhys.8.1.011},

  journal      = {SciPost Physics Proceedings},

  number       = 1,

  volume       = 8,

  place        = {Netherlands},

  year         = {2020},

  month        = {1}

}

Copy to clipboard

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Title: Mass agnostic jet taggers

Abstract

Citation Formats

LHC signals from cascade decays of warped vector resonances journal, May 2017

uBoost: a boosting method for producing uniform selection efficiencies from multivariate classifiers journal, December 2013

QCD or what? journal, January 2019

Boosting H → b b ¯ $$ H\to b\overline{b} $$ with machine learning journal, October 2018

What is the machine learning? journal, March 2018

Pulling out all the tops with computer vision and deep learning journal, October 2018

Parameterized neural networks for high-energy physics journal, April 2016

Techniques for improved heavy particle searches with jet substructure journal, September 2009

Maximizing boosted top identification by minimizing N-subjettiness journal, February 2012

Deep-learned Top Tagging with a Lorentz Layer journal, January 2018

Thinking outside the ROCs: Designing Decorrelated Taggers (DDT) for jet substructure journal, May 2016

Decorrelated jet substructure tagging using adversarial neural networks journal, October 2017

A brief introduction to PYTHIA 8.1 journal, June 2008

How much information is in a jet? journal, June 2017

(Machine) learning to do more with less journal, February 2018

DELPHES 3: a modular framework for fast simulation of a generic collider experiment journal, February 2014

The Machine Learning landscape of top taggers journal, January 2019

Jet-images — deep learning edition journal, July 2016

Deep-learning top taggers or the end of QCD? journal, May 2017

Infrared safety of a neural-net top tagging algorithm journal, February 2019

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

Jet charge and machine learning journal, October 2018

Flavor universal resonances and warped gravity journal, January 2017

A generic anti-QCD jet tagger journal, November 2017

Weakly supervised classification in high energy physics journal, May 2017

Anomaly Detection for Resonant New Physics with Machine Learning journal, December 2018

Dedicated strategies for triboson signals from cascade decays of vector resonances journal, April 2019

New approaches for boosting to uniformity journal, March 2015

Detecting a boosted diboson resonance journal, November 2018

Identifying boosted objects with N-subjettiness journal, March 2011

Jet substructure at the Large Hadron Collider: A review of recent advances in theory and machine learning journal, November 2019

Convolved substructure: analytically decorrelating jet substructure observables journal, May 2018

Playing tag with ANN: boosted top identification with pattern recognition journal, July 2015

Identification of high-momentum top quarks, Higgs bosons, and W and Z bosons using boosted event shapes journal, November 2016

N Jettiness: An Inclusive Event Shape to Veto Jets journal, August 2010

Extending the search for new resonances with machine learning journal, January 2019

Automating the construction of jet observables with machine learning journal, November 2019

Learning to classify from impure samples with high-dimensional data journal, July 2018

Deep Learning and Its Application to LHC Physics journal, October 2018

Stealth multiboson signals journal, October 2017

Spectral analysis of jet substructure with neural networks: boosted Higgs case journal, October 2018

LHC signals from cascade decays of warped vector resonances
journal, May 2017

uBoost: a boosting method for producing uniform selection efficiencies from multivariate classifiers
journal, December 2013

QCD or what?
journal, January 2019

Boosting H → b b ¯ $$ H\to b\overline{b} $$ with machine learning
journal, October 2018

What is the machine learning?
journal, March 2018

Pulling out all the tops with computer vision and deep learning
journal, October 2018

Parameterized neural networks for high-energy physics
journal, April 2016

Techniques for improved heavy particle searches with jet substructure
journal, September 2009

Maximizing boosted top identification by minimizing N-subjettiness
journal, February 2012

Deep-learned Top Tagging with a Lorentz Layer
journal, January 2018

Thinking outside the ROCs: Designing Decorrelated Taggers (DDT) for jet substructure
journal, May 2016

Decorrelated jet substructure tagging using adversarial neural networks
journal, October 2017

A brief introduction to PYTHIA 8.1
journal, June 2008

How much information is in a jet?
journal, June 2017

(Machine) learning to do more with less
journal, February 2018

DELPHES 3: a modular framework for fast simulation of a generic collider experiment
journal, February 2014

The Machine Learning landscape of top taggers
journal, January 2019

Jet-images — deep learning edition
journal, July 2016

Deep-learning top taggers or the end of QCD?
journal, May 2017

Infrared safety of a neural-net top tagging algorithm
journal, February 2019

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

Jet charge and machine learning
journal, October 2018

Flavor universal resonances and warped gravity
journal, January 2017

A generic anti-QCD jet tagger
journal, November 2017

Weakly supervised classification in high energy physics
journal, May 2017

Anomaly Detection for Resonant New Physics with Machine Learning
journal, December 2018

Dedicated strategies for triboson signals from cascade decays of vector resonances
journal, April 2019

New approaches for boosting to uniformity
journal, March 2015

Detecting a boosted diboson resonance
journal, November 2018

Identifying boosted objects with N-subjettiness
journal, March 2011

Jet substructure at the Large Hadron Collider: A review of recent advances in theory and machine learning
journal, November 2019

Convolved substructure: analytically decorrelating jet substructure observables
journal, May 2018

Playing tag with ANN: boosted top identification with pattern recognition
journal, July 2015

Identification of high-momentum top quarks, Higgs bosons, and $W$ and $Z$ bosons using boosted event shapes
journal, November 2016

$N$ Jettiness: An Inclusive Event Shape to Veto Jets
journal, August 2010

Extending the search for new resonances with machine learning
journal, January 2019

Automating the construction of jet observables with machine learning
journal, November 2019

Learning to classify from impure samples with high-dimensional data
journal, July 2018

Deep Learning and Its Application to LHC Physics
journal, October 2018

Stealth multiboson signals
journal, October 2017

Spectral analysis of jet substructure with neural networks: boosted Higgs case
journal, October 2018