DOE PAGES title logo U.S. Department of Energy
Office of Scientific and Technical Information

Title: Boosting $$ H\to b\overline{b} $$ with machine learning

Abstract

High-pT Higgs production at hadron colliders provides a direct probe of the internal structure of the gg → H loop with the H→$$b\bar{b}$$ decay offering the most statistics due to the large branching ratio. Despite the overwhelming QCD background, recent advances in jet substructure have put the observation of the gg→H→$$b\bar{b}$$ channel at the LHC within the realm of possibility. In order to enhance the sensitivity to this process, we develop a two-stream convolutional neural network, with one stream acting on jet information and one using global event properties. The neural network significantly increases the discovery potential of a Higgs signal, both for high-pT Standard Model production as well for possible beyond the Standard Model contributions. Unlike most studies for boosted hadronically decaying massive particles, the boosted Higgs search is unique because double b-tagging rejects nearly all background processes that do not have two hard prongs. In this context — which goes beyond state-of-the-art two-prong tagging — the network is studied to identify the origin of the additional information leading to the increased significance. The procedures described here are also applicable to related final states where they can be used to identify additional sources of discrimination power that are not being exploited by current techniques.

Authors:
ORCiD logo [1];  [2];  [3];  [4]
  1. Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States); Univ. of California, Berkeley, CA (United States)
  2. Univ. of Oregon, Eugene, OR (United States)
  3. Univ. of California, Berkeley, CA (United States); Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
  4. Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
Publication Date:
Research Org.:
Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States). National Energy Research Scientific Computing Center (NERSC)
Sponsoring Org.:
USDOE Office of Science (SC), High Energy Physics (HEP)
OSTI Identifier:
1544187
Grant/Contract Number:  
AC02-05CH11231; SC0011640
Resource Type:
Accepted Manuscript
Journal Name:
Journal of High Energy Physics (Online)
Additional Journal Information:
Journal Name: Journal of High Energy Physics (Online); Journal Volume: 2018; Journal Issue: 10; Journal ID: ISSN 1029-8479
Publisher:
Springer Berlin
Country of Publication:
United States
Language:
English
Subject:
72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS; Jets

Citation Formats

Lin, Joshua, Freytsis, Marat, Moult, Ian, and Nachman, Benjamin. Boosting $ H\to b\overline{b} $ with machine learning. United States: N. p., 2018. Web. doi:10.1007/JHEP10(2018)101.
Lin, Joshua, Freytsis, Marat, Moult, Ian, & Nachman, Benjamin. Boosting $ H\to b\overline{b} $ with machine learning. United States. https://doi.org/10.1007/JHEP10(2018)101
Lin, Joshua, Freytsis, Marat, Moult, Ian, and Nachman, Benjamin. Tue . "Boosting $ H\to b\overline{b} $ with machine learning". United States. https://doi.org/10.1007/JHEP10(2018)101. https://www.osti.gov/servlets/purl/1544187.
@article{osti_1544187,
title = {Boosting $ H\to b\overline{b} $ with machine learning},
author = {Lin, Joshua and Freytsis, Marat and Moult, Ian and Nachman, Benjamin},
abstractNote = {High-pT Higgs production at hadron colliders provides a direct probe of the internal structure of the gg → H loop with the H→$b\bar{b}$ decay offering the most statistics due to the large branching ratio. Despite the overwhelming QCD background, recent advances in jet substructure have put the observation of the gg→H→$b\bar{b}$ channel at the LHC within the realm of possibility. In order to enhance the sensitivity to this process, we develop a two-stream convolutional neural network, with one stream acting on jet information and one using global event properties. The neural network significantly increases the discovery potential of a Higgs signal, both for high-pT Standard Model production as well for possible beyond the Standard Model contributions. Unlike most studies for boosted hadronically decaying massive particles, the boosted Higgs search is unique because double b-tagging rejects nearly all background processes that do not have two hard prongs. In this context — which goes beyond state-of-the-art two-prong tagging — the network is studied to identify the origin of the additional information leading to the increased significance. The procedures described here are also applicable to related final states where they can be used to identify additional sources of discrimination power that are not being exploited by current techniques.},
doi = {10.1007/JHEP10(2018)101},
journal = {Journal of High Energy Physics (Online)},
number = 10,
volume = 2018,
place = {United States},
year = {Tue Oct 16 00:00:00 EDT 2018},
month = {Tue Oct 16 00:00:00 EDT 2018}
}

Journal Article:
Free Publicly Available Full Text
Publisher's Version of Record

Citation Metrics:
Cited by: 47 works
Citation information provided by
Web of Science

Save / Share:

Works referenced in this record:

The anti- k t jet clustering algorithm
journal, April 2008


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


Two-loop planar master integrals for Higgs → 3 partons with full heavy-quark mass dependence
text, January 2016


Dichroic subjettiness ratios to distinguish colour flows in boosted boson tagging
journal, March 2017

  • Salam, Gavin P.; Schunk, Lais; Soyez, Gregory
  • Journal of High Energy Physics, Vol. 2017, Issue 3
  • DOI: 10.1007/JHEP03(2017)022

Fast inference of deep neural networks in FPGAs for particle physics
journal, July 2018


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


Power counting to better jet observables
journal, December 2014

  • Larkoski, Andrew J.; Moult, Ian; Neill, Duff
  • Journal of High Energy Physics, Vol. 2014, Issue 12
  • DOI: 10.1007/JHEP12(2014)009

Two-loop amplitudes for processes gg → Hg, qg → Hq and q q ¯ → H g $$ \mathrm{q}\overline{\mathrm{q}}\to \mathrm{H}\mathrm{g} $$ at large Higgs transverse momentum
journal, February 2018

  • Kudashkin, Kirill; Melnikov, Kirill; Wever, Christopher
  • Journal of High Energy Physics, Vol. 2018, Issue 2
  • DOI: 10.1007/JHEP02(2018)135

Updated global SMEFT fit to Higgs, diboson and electroweak data
journal, June 2018

  • Ellis, John; Murphy, Christopher W.; Sanz, Verónica
  • Journal of High Energy Physics, Vol. 2018, Issue 6
  • DOI: 10.1007/JHEP06(2018)146

Seeing in Color: Jet Superstructure
journal, July 2010


Deep learning in color: towards automated quark/gluon jet discrimination
journal, January 2017

  • Komiske, Patrick T.; Metodiev, Eric M.; Schwartz, Matthew D.
  • Journal of High Energy Physics, Vol. 2017, Issue 1
  • DOI: 10.1007/JHEP01(2017)110

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
  • DOI: 10.1007/JHEP05(2017)006

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
  • DOI: 10.1007/JHEP07(2014)079

FastJet user manual: (for version 3.0.2)
journal, March 2012


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
  • DOI: 10.1007/JHEP11(2017)163

Energy flow polynomials: a complete linear basis for jet substructure
journal, April 2018

  • Komiske, Patrick T.; Metodiev, Eric M.; Thaler, Jesse
  • Journal of High Energy Physics, Vol. 2018, Issue 4
  • DOI: 10.1007/JHEP04(2018)013

A phenomenological profile of the Higgs boson
journal, January 1976


NNLO QCD corrections to Higgs boson production at large transverse momentum
journal, October 2016

  • Chen, X.; Cruz-Martinez, J.; Gehrmann, T.
  • Journal of High Energy Physics, Vol. 2016, Issue 10
  • DOI: 10.1007/JHEP10(2016)066

Two-loop planar master integrals for Higgs → 3 partons with full heavy-quark mass dependence
journal, December 2016

  • Bonciani, Roberto; Del Duca, Vittorio; Frellesvig, Hjalte
  • Journal of High Energy Physics, Vol. 2016, Issue 12
  • DOI: 10.1007/JHEP12(2016)096

Identifying boosted objects with N-subjettiness
journal, March 2011


Probing Higgs couplings with high p T Higgs production
journal, January 2014


Recursive Neural Networks in Quark/Gluon Tagging
journal, June 2018


Next-to-Leading-Order QCD Corrections to Higgs Boson Plus Jet Production with Full Top-Quark Mass Dependence
journal, April 2018


Analytic boosted boson discrimination
journal, May 2016

  • Larkoski, Andrew J.; Moult, Ian; Neill, Duff
  • Journal of High Energy Physics, Vol. 2016, Issue 5
  • DOI: 10.1007/JHEP05(2016)117

Herwig++ physics and manual
journal, November 2008


Boosted Higgs shapes
journal, October 2014

  • Schlaffer, Matthias; Spannowsky, Michael; Takeuchi, Michihisa
  • The European Physical Journal C, Vol. 74, Issue 10
  • DOI: 10.1140/epjc/s10052-014-3120-z

Jet Substructure as a New Higgs-Search Channel at the Large Hadron Collider
journal, June 2008


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


Soft drop
journal, May 2014

  • Larkoski, Andrew J.; Marzani, Simone; Soyez, Gregory
  • Journal of High Energy Physics, Vol. 2014, Issue 5
  • DOI: 10.1007/JHEP05(2014)146

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


Resolving gluon fusion loops at current and future hadron colliders
journal, September 2016

  • Azatov, Aleksandr; Grojean, Christophe; Paul, Ayan
  • Journal of High Energy Physics, Vol. 2016, Issue 9
  • DOI: 10.1007/JHEP09(2016)123

The catchment area of jets
journal, April 2008


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
  • DOI: 10.1007/JHEP05(2016)156

Very boosted Higgs in gluon fusion
journal, May 2014

  • Grojean, C.; Salvioni, E.; Schlaffer, M.
  • Journal of High Energy Physics, Vol. 2014, Issue 5
  • DOI: 10.1007/JHEP05(2014)022

Jet activity as a probe of high-mass resonance production
journal, November 2016


Energy correlation functions for jet substructure
journal, June 2013

  • Larkoski, Andrew J.; Salam, Gavin P.; Thaler, Jesse
  • Journal of High Energy Physics, Vol. 2013, Issue 6
  • DOI: 10.1007/JHEP06(2013)108

Classification without labels: learning from mixed samples in high energy physics
journal, October 2017

  • Metodiev, Eric M.; Nachman, Benjamin; Thaler, Jesse
  • Journal of High Energy Physics, Vol. 2017, Issue 10
  • DOI: 10.1007/JHEP10(2017)174

Enhanced Higgs Boson to τ + τ Search with Deep Learning
journal, March 2015


New angles on energy correlation functions
journal, December 2016

  • Moult, Ian; Necib, Lina; Thaler, Jesse
  • Journal of High Energy Physics, Vol. 2016, Issue 12
  • DOI: 10.1007/JHEP12(2016)153

Pileup Mitigation with Machine Learning (PUMML)
journal, December 2017

  • Komiske, Patrick T.; Metodiev, Eric M.; Nachman, Benjamin
  • Journal of High Energy Physics, Vol. 2017, Issue 12
  • DOI: 10.1007/JHEP12(2017)051

Jet-images — deep learning edition
journal, July 2016

  • de Oliveira, Luke; Kagan, Michael; Mackey, Lester
  • Journal of High Energy Physics, Vol. 2016, Issue 7
  • DOI: 10.1007/JHEP07(2016)069

Jet-images: computer vision inspired techniques for jet tagging
journal, February 2015

  • Cogan, Josh; Kagan, Michael; Strauss, Emanuel
  • Journal of High Energy Physics, Vol. 2015, Issue 2
  • DOI: 10.1007/JHEP02(2015)118

NLO Higgs+jet production at large transverse momenta including top quark mass effects
journal, September 2018


NNLO QCD corrections to Higgs boson production at large transverse momentum
text, January 2016


Novel jet observables from machine learning
journal, March 2018

  • Datta, Kaustuv; Larkoski, Andrew J.
  • Journal of High Energy Physics, Vol. 2018, Issue 3
  • DOI: 10.1007/JHEP03(2018)086

Learning Particle Physics by Example: Location-Aware Generative Adversarial Networks for Physics Synthesis
journal, September 2017

  • de Oliveira, Luke; Paganini, Michela; Nachman, Benjamin
  • Computing and Software for Big Science, Vol. 1, Issue 1
  • DOI: 10.1007/s41781-017-0004-6

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

  • Moult, Ian; Nachman, Benjamin; Neill, Duff
  • Journal of High Energy Physics, Vol. 2018, Issue 5
  • DOI: 10.1007/JHEP05(2018)002

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
  • DOI: 10.1007/JHEP07(2015)086

Theoretical constraints on the Higgs effective couplings
journal, April 2010

  • Low, Ian; Rattazzi, Riccardo; Vichi, Alessandro
  • Journal of High Energy Physics, Vol. 2010, Issue 4
  • DOI: 10.1007/JHEP04(2010)126

Jet substructure with analytical methods
journal, November 2013


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
  • DOI: 10.1038/ncomms5308

Towards an understanding of jet substructure
journal, September 2013

  • Dasgupta, Mrinal; Fregoso, Alessandro; Marzani, Simone
  • Journal of High Energy Physics, Vol. 2013, Issue 9
  • DOI: 10.1007/JHEP09(2013)029

Factorization and resummation for groomed multi-prong jet shapes
journal, February 2018

  • Larkoski, Andrew J.; Moult, Ian; Neill, Duff
  • Journal of High Energy Physics, Vol. 2018, Issue 2
  • DOI: 10.1007/JHEP02(2018)144

A brief introduction to PYTHIA 8.1
journal, June 2008

  • Sjöstrand, Torbjörn; Mrenna, Stephen; Skands, Peter
  • Computer Physics Communications, Vol. 178, Issue 11
  • DOI: 10.1016/j.cpc.2008.01.036

Event generation with SHERPA 1.1
journal, February 2009


Matching matrix elements and shower evolution for top-pair production in hadronic collisions
journal, January 2007

  • Mangano, Michelangelo L.; Moretti, Mauro; Piccinini, Fulvio
  • Journal of High Energy Physics, Vol. 2007, Issue 01
  • DOI: 10.1088/1126-6708/2007/01/013

Resolving gluon fusion loops at current and future hadron colliders
text, January 2016

  • Azatov, Aleksandr; Grojean, Christophe; Paul, Ayan
  • Deutsches Elektronen-Synchrotron, DESY, Hamburg
  • DOI: 10.3204/pubdb-2016-04954

Deep-learning top taggers or the end of QCD
text, January 2017


The anti-k_t jet clustering algorithm
text, January 2008


Herwig++ Physics and Manual
text, January 2008


Seeing in Color: Jet Superstructure
text, January 2010


Maximizing Boosted Top Identification by Minimizing N-subjettiness
text, January 2011


Energy Correlation Functions for Jet Substructure
text, January 2013


Jet substructure with analytical methods
text, January 2013


Searching for Exotic Particles in High-Energy Physics with Deep Learning
text, January 2014


Jet-Images: Computer Vision Inspired Techniques for Jet Tagging
text, January 2014


Jet activity as a probe of high-mass resonance production
text, January 2016


Resolving gluon fusion loops at current and future hadron colliders
text, January 2016


Deep learning in color: towards automated quark/gluon jet discrimination
text, January 2016


Learning Particle Physics by Example: Location-Aware Generative Adversarial Networks for Physics Synthesis
text, January 2017


Deep-learning Top Taggers or The End of QCD?
text, January 2017


Factorization and Resummation for Groomed Multi-Prong Jet Shapes
text, January 2017


Convolved Substructure: Analytically Decorrelating Jet Substructure Observables
text, January 2017


Energy flow polynomials: A complete linear basis for jet substructure
text, January 2017


Works referencing / citing this record:

Portraying double Higgs at the Large Hadron Collider
journal, September 2019

  • Kim, Jeong Han; Kim, Minho; Kong, Kyoungchul
  • Journal of High Energy Physics, Vol. 2019, Issue 9
  • DOI: 10.1007/jhep09(2019)047

Calculating pull for non-singlet jets
journal, December 2019

  • Bao, Yunjia; Larkoski, Andrew J.
  • Journal of High Energy Physics, Vol. 2019, Issue 12
  • DOI: 10.1007/jhep12(2019)035

Supervised Deep Learning in High Energy Phenomenology: a Mini Review
journal, August 2019


Automating the construction of jet observables with machine learning
text, January 2019


Topology Classification with Deep Learning to Improve Real-Time Event Selection at the LHC
journal, August 2019

  • Nguyen, T. Q.; Weitekamp, D.; Anderson, D.
  • Computing and Software for Big Science, Vol. 3, Issue 1
  • DOI: 10.1007/s41781-019-0028-1

Study of energy deposition patterns in hadron calorimeter for prompt and displaced jets using convolutional neural network
text, January 2019


Portraying Double Higgs at the Large Hadron Collider
conference, January 2021

  • Kim, Minho; Kim, Jeonghan; Kong, K. C.
  • Proceedings of Artificial Intelligence for Science, Industry and Society — PoS(AISIS2019)
  • DOI: 10.22323/1.372.0013

Topology classification with deep learning to improve real-time event selection at the LHC
text, January 2018


Reweighting a parton shower using a neural network: the final-state case
text, January 2018


Energy Flow Networks: Deep Sets for Particle Jets
text, January 2018


Quark-Gluon Tagging: Machine Learning vs Detector
text, January 2018


Automating the Construction of Jet Observables with Machine Learning
text, January 2019


Interpretable Deep Learning for Two-Prong Jet Classification with Jet Spectra
text, January 2019


CapsNets Continuing the Convolutional Quest
text, January 2019