Built on

Simone Marzani, Gregory Soyez, and Michael Spannowsky, Looking inside jets: an introduction to jet substructure and boosted-object phenomenology , Vol. 958 (Springer, 2019) arXiv:1901.10342 [hep-ph]
Original
1901
Earlier work this paper cites.
Huilin Qu and Loukas Gouskos, “ParticleNet: Jet Tagging via Particle Clouds,” Phys. Rev. D 101
Original
1902
Earlier work this paper cites.
Anja Butter et al. , “The Machine Learning Landscape of Top Taggers,” SciPost Phys. 7
Original
1902
Earlier work this paper cites.
Kaustuv Datta, Andrew Larkoski, and Benjamin Nachman, “Automating the Construction of Jet Observables with Machine Learning,” Phys. Rev. D 100
Original
1902
Earlier work this paper cites.
Sven Bollweg, Manuel Haußmann, Gregor Kasieczka, Michel Luchmann, Tilman Plehn, and Jennifer Thompson, “Deep-Learning Jets with Uncertainties and More,” SciPost Phys. 8
Original
1904
Earlier work this paper cites.
Silviu-Marian Udrescu and Max Tegmark, “AI Feynman: a Physics-Inspired Method for Symbolic Regression,” (2019), arXiv:1905.11481 [physics.comp-ph]
Original
1905
Earlier work this paper cites.
Andrew J. Larkoski and Eric M. Metodiev, “A Theory of Quark vs. Gluon Discrimination,” JHEP 10
Original
1906
Earlier work this paper cites.
Eric A. Moreno, Olmo Cerri, Javier M. Duarte, Harvey B. Newman, Thong Q. Nguyen, Avikar Periwal, Maurizio Pierini, Aidana Serikova, Maria Spiropulu, and Jean-Roch Vlimant, “JEDI-net: a jet identification algorithm based on interaction networks,” Eur. Phys. J. C 80
Original
1908
Earlier work this paper cites.
Benjamin Nachman, “A guide for deploying Deep Learning in LHC searches: How to achieve optimality and account for uncertainty,” SciPost Phys. 8
Original
1909
Earlier work this paper cites.
Anders Andreassen, Patrick T. Komiske, Eric M. Metodiev, Benjamin Nachman, and Jesse Thaler, “OmniFold: A Method to Simultaneously Unfold All Observables,” Phys. Rev. Lett. 124
Original
1911
Earlier work this paper cites.
Patrick T. Komiske, Eric M. Metodiev, and Jesse Thaler, “Cutting Multiparticle Correlators Down to Size,” Phys. Rev. D 101
Original
1911
Earlier work this paper cites.
Stephen D. Ellis, Andrew Hornig, Tuhin S. Roy, David Krohn, and Matthew D. Schwartz, “Qjets: A Non-Deterministic Approach to Tree-Based Jet Substructure,” Phys. Rev. Lett. 108
Original
1914
Earlier work this paper cites.
M. G. KENDALL, “A new measure of rank correlation,” Biometrika 30
1938
Earlier work this paper cites.
Carsten Peterson, “Track Finding With Neural Networks,” Nucl. Instrum. Meth. A279
1989
Earlier work this paper cites.
Robert E. Schapire, “The strength of weak learnability,” Mach. Learn. 5
1990
Earlier work this paper cites.
M.H. Seymour, “Tagging a heavy Higgs boson,” in ECFA Large Hadron Collider (LHC) Workshop: Physics and Instrumentation (1991) pp. 557–569
1991
Earlier work this paper cites.
DELPHI Collaboration, Physics Letters B 295
1992
Earlier work this paper cites.
Michael H. Seymour, “Searches for new particles using cone and cluster jet algorithms: A Comparative study,” Z. Phys. C 62
1994
Earlier work this paper cites.
Halina Abramowicz, Allen Caldwell, and Ralph Sinkus, “Neural network based electron identification in the ZEUS calorimeter,” Nucl. Instrum. Meth. A365
1995
Earlier work this paper cites.
Y. Freund, “Boosting a weak learning algorithm by majority,” Information and Computation 121
1995
Earlier work this paper cites.
J. K. Kohne et al. , “Realization of a second level neural network trigger for the H1 experiment at HERA,” Nucl. Instrum. Meth. A389
1997
Earlier work this paper cites.
DZero Collaboration, “Search for single top quark production at dø using neural networks,” Physics Letters B 517
2001
Earlier work this paper cites.
Vinicius Mikuni and Florencia Canelli, “ABCNet: An attention-based method for particle tagging,” Eur. Phys. J. Plus 135
Original
2001
Earlier work this paper cites.
J.M. Butterworth, B.E. Cox, and Jeffrey R. Forshaw, “ W W WW scattering at the CERN LHC,” Phys. Rev. D 65
2002
Earlier work this paper cites.
J.A. Aguilar-Saavedra and B. Zaldívar, “Jet tagging made easy,” Eur. Phys. J. C 80
Original
2002
Earlier work this paper cites.
Gregor Kasieczka, Michel Luchmann, Florian Otterpohl, and Tilman Plehn, “Per-Object Systematics using Deep-Learned Calibration,” (2020a), arXiv:2003.11099 [hep-ph]
Original
2003
Earlier work this paper cites.
Albert M Sirunyan et al. (CMS), “Identification of heavy, energetic, hadronically decaying particles using machine-learning techniques,” JINST 15
Original
2004
Earlier work this paper cites.
V. M. Abazov et al. (D0), “First measurement of σ \sigma ( p p ¯ → Z ) p\bar{p}\to Z^{)} . Br ( Z → τ τ ) Z\to\tau\tau^{)} at s \sqrt{s} = 1.96- TeV,” Phys. Rev. D71
2005
Earlier work this paper cites.
Andrea Banfi, Gavin P. Salam, and Giulia Zanderighi, “Principles of general final-state resummation and automated implementation,” JHEP 03
2005
Earlier work this paper cites.
Marco Bellagente, Anja Butter, Gregor Kasieczka, Tilman Plehn, Armand Rousselot, Ramon Winterhalder, Lynton Ardizzone, and Ullrich Köthe, “Invertible Networks or Partons to Detector and Back Again,” (2020), arXiv:2006.06685 [hep-ph]
Original
2006
Earlier work this paper cites.
Cristian Buciluundefined, Rich Caruana, and Alexandru Niculescu-Mizil, “Model compression,” in Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining , KDD ’06 (Association for Computing Machinery, New York, NY, USA, 2006) p. 535–541
2006
Earlier work this paper cites.
Alexander Bogatskiy, Brandon Anderson, Jan T. Offermann, Marwah Roussi, David W. Miller, and Risi Kondor, “Lorentz Group Equivariant Neural Network for Particle Physics,” (2020), arXiv:2006.04780 [hep-ph]
Original
2006
Earlier work this paper cites.
Andy Buckley, Giuseppe Callea, Andrew J. Larkoski, and Simone Marzani, “An Optimal Observable for Color Singlet Identification,” SciPost Phys. 9
Original
2006
Earlier work this paper cites.
Torbjorn Sjostrand, Stephen Mrenna, and Peter Z. Skands, “PYTHIA 6.4 Physics and Manual,” JHEP 0605
2006
Earlier work this paper cites.
J.M. Butterworth, John R. Ellis, and A.R. Raklev, “Reconstructing sparticle mass spectra using hadronic decays,” JHEP 05
2007
Earlier work this paper cites.
Gregor Kasieczka, Simone Marzani, Gregory Soyez, and Giovanni Stagnitto, “Towards Machine Learning Analytics for Jet Substructure,” JHEP 09
Original
2007
Earlier work this paper cites.
Jonathan M. Butterworth, Adam R. Davison, Mathieu Rubin, and Gavin P. Salam, “Jet substructure as a new Higgs search channel at the LHC,” Phys.Rev.Lett. 100
Original
2008
Earlier work this paper cites.

Similar

Jonathan Shlomi, Sanmay Ganguly, Eilam Gross, Kyle Cranmer, Yaron Lipman, Hadar Serviansky, Haggai Maron, and Nimrod Segol, “Secondary Vertex Finding in Jets with Neural Networks,” (2020), arXiv:2008.02831 [hep-ex]
Original
2008
Cited alongside, same era.
David E. Kaplan, Keith Rehermann, Matthew D. Schwartz, and Brock Tweedie, “Top Tagging: A Method for Identifying Boosted Hadronically Decaying Top Quarks,” Phys.Rev.Lett. 101
Original
2008
Cited alongside, same era.
Matteo Cacciari, Gavin P. Salam, and Gregory Soyez, “The Anti-k(t) jet clustering algorithm,” JHEP 04
Original
2008
Cited alongside, same era.
Leandro G. Almeida, Seung J. Lee, Gilad Perez, George F. Sterman, Ilmo Sung, and Joseph Virzi, “Substructure of high-p_T Jets at the LHC,” Phys. Rev. D 79
Original
2009
Cited alongside, same era.

Then

Ian Moult, Lina Necib, and Jesse Thaler, “New Angles on Energy Correlation Functions,” JHEP 12
Original
2016
Later among the works it cites.
Roberto Santos, M. Nguyen, Jordan Webster, Soo Ryu, Jahred Adelman, Sergei Chekanov, and Jie Zhou, “Machine learning techniques in searches for t t ¯ h t\bar{t}h in the h → b b ¯ h\rightarrow b\bar{b} decay channel,” JINST 12
Original
2017
Later among the works it cites.
Gregor Kasieczka, Tilman Plehn, Michael Russell, and Torben Schell, “Deep-learning Top Taggers or The End of QCD?” JHEP 05
Original
2017
Later among the works it cites.
Identification of Jets Containing b b -Hadrons with Recurrent Neural Networks at the ATLAS Experiment , Tech. Rep. ATL-PHYS-PUB-2017-003 (CERN, Geneva, 2017)
2017
Later among the works it cites.

Beyond the bibliography

alphaXiv searches the wider corpus for related work and actual follow-ups.

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Reading the bibliography…

2020

Mapping Machine-Learned Physics into a Human-Readable Space

Faucett, Taylor, Thaler, Jesse, Whiteson, Daniel

Understand

We present a technique for translating a black-box machine-learned classifier operating on a high-dimensional input space into a small set of human-interpretable observables that can be combined to make the same classification decisions.

We iteratively select these observables from a large space of high-level discriminants by finding those with the highest decision similarity relative to the black box, quantified via a metric we introduce that evaluates the relative ordering of pairs of inputs.
Successive iterations focus only on the subset of input pairs that are misordered by the current set of observables.
This method enables simplification of the machine-learning strategy, interpretation of the results in terms of well-understood physical concepts, validation of the physical model, and the potential for new insights into the nature of the problem itself.

Built on

Simone Marzani, Gregory Soyez, and Michael Spannowsky, Looking inside jets: an introduction to jet substructure and boosted-object phenomenology , Vol. 958 (Springer, 2019) arXiv:1901.10342 [hep-ph]
Original
1901
Earlier work this paper cites.
Huilin Qu and Loukas Gouskos, “ParticleNet: Jet Tagging via Particle Clouds,” Phys. Rev. D 101
Original
1902
Earlier work this paper cites.
Anja Butter et al. , “The Machine Learning Landscape of Top Taggers,” SciPost Phys. 7
Original
1902
Earlier work this paper cites.
Kaustuv Datta, Andrew Larkoski, and Benjamin Nachman, “Automating the Construction of Jet Observables with Machine Learning,” Phys. Rev. D 100
Original
1902
Earlier work this paper cites.
Sven Bollweg, Manuel Haußmann, Gregor Kasieczka, Michel Luchmann, Tilman Plehn, and Jennifer Thompson, “Deep-Learning Jets with Uncertainties and More,” SciPost Phys. 8
Original
1904
Earlier work this paper cites.
Silviu-Marian Udrescu and Max Tegmark, “AI Feynman: a Physics-Inspired Method for Symbolic Regression,” (2019), arXiv:1905.11481 [physics.comp-ph]
Original
1905
Earlier work this paper cites.
Andrew J. Larkoski and Eric M. Metodiev, “A Theory of Quark vs. Gluon Discrimination,” JHEP 10
Original
1906
Earlier work this paper cites.
Eric A. Moreno, Olmo Cerri, Javier M. Duarte, Harvey B. Newman, Thong Q. Nguyen, Avikar Periwal, Maurizio Pierini, Aidana Serikova, Maria Spiropulu, and Jean-Roch Vlimant, “JEDI-net: a jet identification algorithm based on interaction networks,” Eur. Phys. J. C 80
Original
1908
Earlier work this paper cites.
Benjamin Nachman, “A guide for deploying Deep Learning in LHC searches: How to achieve optimality and account for uncertainty,” SciPost Phys. 8
Original
1909
Earlier work this paper cites.
Anders Andreassen, Patrick T. Komiske, Eric M. Metodiev, Benjamin Nachman, and Jesse Thaler, “OmniFold: A Method to Simultaneously Unfold All Observables,” Phys. Rev. Lett. 124
Original
1911
Earlier work this paper cites.
Patrick T. Komiske, Eric M. Metodiev, and Jesse Thaler, “Cutting Multiparticle Correlators Down to Size,” Phys. Rev. D 101
Original
1911
Earlier work this paper cites.
Stephen D. Ellis, Andrew Hornig, Tuhin S. Roy, David Krohn, and Matthew D. Schwartz, “Qjets: A Non-Deterministic Approach to Tree-Based Jet Substructure,” Phys. Rev. Lett. 108
Original
1914
Earlier work this paper cites.
M. G. KENDALL, “A new measure of rank correlation,” Biometrika 30
1938
Earlier work this paper cites.
Carsten Peterson, “Track Finding With Neural Networks,” Nucl. Instrum. Meth. A279
1989
Earlier work this paper cites.
Robert E. Schapire, “The strength of weak learnability,” Mach. Learn. 5
1990
Earlier work this paper cites.
M.H. Seymour, “Tagging a heavy Higgs boson,” in ECFA Large Hadron Collider (LHC) Workshop: Physics and Instrumentation (1991) pp. 557–569
1991
Earlier work this paper cites.
DELPHI Collaboration, Physics Letters B 295
1992
Earlier work this paper cites.
Michael H. Seymour, “Searches for new particles using cone and cluster jet algorithms: A Comparative study,” Z. Phys. C 62
1994
Earlier work this paper cites.
Halina Abramowicz, Allen Caldwell, and Ralph Sinkus, “Neural network based electron identification in the ZEUS calorimeter,” Nucl. Instrum. Meth. A365
1995
Earlier work this paper cites.
Y. Freund, “Boosting a weak learning algorithm by majority,” Information and Computation 121
1995
Earlier work this paper cites.
J. K. Kohne et al. , “Realization of a second level neural network trigger for the H1 experiment at HERA,” Nucl. Instrum. Meth. A389
1997
Earlier work this paper cites.
DZero Collaboration, “Search for single top quark production at dø using neural networks,” Physics Letters B 517
2001
Earlier work this paper cites.
Vinicius Mikuni and Florencia Canelli, “ABCNet: An attention-based method for particle tagging,” Eur. Phys. J. Plus 135
Original
2001
Earlier work this paper cites.
J.M. Butterworth, B.E. Cox, and Jeffrey R. Forshaw, “ W W WW scattering at the CERN LHC,” Phys. Rev. D 65
2002
Earlier work this paper cites.
J.A. Aguilar-Saavedra and B. Zaldívar, “Jet tagging made easy,” Eur. Phys. J. C 80
Original
2002
Earlier work this paper cites.
Gregor Kasieczka, Michel Luchmann, Florian Otterpohl, and Tilman Plehn, “Per-Object Systematics using Deep-Learned Calibration,” (2020a), arXiv:2003.11099 [hep-ph]
Original
2003
Earlier work this paper cites.
Albert M Sirunyan et al. (CMS), “Identification of heavy, energetic, hadronically decaying particles using machine-learning techniques,” JINST 15
Original
2004
Earlier work this paper cites.
V. M. Abazov et al. (D0), “First measurement of σ \sigma ( p p ¯ → Z ) p\bar{p}\to Z^{)} . Br ( Z → τ τ ) Z\to\tau\tau^{)} at s \sqrt{s} = 1.96- TeV,” Phys. Rev. D71
2005
Earlier work this paper cites.
Andrea Banfi, Gavin P. Salam, and Giulia Zanderighi, “Principles of general final-state resummation and automated implementation,” JHEP 03
2005
Earlier work this paper cites.
Marco Bellagente, Anja Butter, Gregor Kasieczka, Tilman Plehn, Armand Rousselot, Ramon Winterhalder, Lynton Ardizzone, and Ullrich Köthe, “Invertible Networks or Partons to Detector and Back Again,” (2020), arXiv:2006.06685 [hep-ph]
Original
2006
Earlier work this paper cites.
Cristian Buciluundefined, Rich Caruana, and Alexandru Niculescu-Mizil, “Model compression,” in Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining , KDD ’06 (Association for Computing Machinery, New York, NY, USA, 2006) p. 535–541
2006
Earlier work this paper cites.
Alexander Bogatskiy, Brandon Anderson, Jan T. Offermann, Marwah Roussi, David W. Miller, and Risi Kondor, “Lorentz Group Equivariant Neural Network for Particle Physics,” (2020), arXiv:2006.04780 [hep-ph]
Original
2006
Earlier work this paper cites.
Andy Buckley, Giuseppe Callea, Andrew J. Larkoski, and Simone Marzani, “An Optimal Observable for Color Singlet Identification,” SciPost Phys. 9
Original
2006
Earlier work this paper cites.
Torbjorn Sjostrand, Stephen Mrenna, and Peter Z. Skands, “PYTHIA 6.4 Physics and Manual,” JHEP 0605
2006
Earlier work this paper cites.
J.M. Butterworth, John R. Ellis, and A.R. Raklev, “Reconstructing sparticle mass spectra using hadronic decays,” JHEP 05
2007
Earlier work this paper cites.
Gregor Kasieczka, Simone Marzani, Gregory Soyez, and Giovanni Stagnitto, “Towards Machine Learning Analytics for Jet Substructure,” JHEP 09
Original
2007
Earlier work this paper cites.
Jonathan M. Butterworth, Adam R. Davison, Mathieu Rubin, and Gavin P. Salam, “Jet substructure as a new Higgs search channel at the LHC,” Phys.Rev.Lett. 100
Original
2008
Earlier work this paper cites.

Similar

Jonathan Shlomi, Sanmay Ganguly, Eilam Gross, Kyle Cranmer, Yaron Lipman, Hadar Serviansky, Haggai Maron, and Nimrod Segol, “Secondary Vertex Finding in Jets with Neural Networks,” (2020), arXiv:2008.02831 [hep-ex]
Original
2008
Cited alongside, same era.
David E. Kaplan, Keith Rehermann, Matthew D. Schwartz, and Brock Tweedie, “Top Tagging: A Method for Identifying Boosted Hadronically Decaying Top Quarks,” Phys.Rev.Lett. 101
Original
2008
Cited alongside, same era.
Matteo Cacciari, Gavin P. Salam, and Gregory Soyez, “The Anti-k(t) jet clustering algorithm,” JHEP 04
Original
2008
Cited alongside, same era.
Leandro G. Almeida, Seung J. Lee, Gilad Perez, George F. Sterman, Ilmo Sung, and Joseph Virzi, “Substructure of high-p_T Jets at the LHC,” Phys. Rev. D 79
Original
2009
Cited alongside, same era.

Then

Ian Moult, Lina Necib, and Jesse Thaler, “New Angles on Energy Correlation Functions,” JHEP 12
Original
2016
Later among the works it cites.
Roberto Santos, M. Nguyen, Jordan Webster, Soo Ryu, Jahred Adelman, Sergei Chekanov, and Jie Zhou, “Machine learning techniques in searches for t t ¯ h t\bar{t}h in the h → b b ¯ h\rightarrow b\bar{b} decay channel,” JINST 12
Original
2017
Later among the works it cites.
Gregor Kasieczka, Tilman Plehn, Michael Russell, and Torben Schell, “Deep-learning Top Taggers or The End of QCD?” JHEP 05
Original
2017
Later among the works it cites.
Identification of Jets Containing b b -Hadrons with Recurrent Neural Networks at the ATLAS Experiment , Tech. Rep. ATL-PHYS-PUB-2017-003 (CERN, Geneva, 2017)
2017
Later among the works it cites.

Beyond the bibliography

alphaXiv searches the wider corpus for related work and actual follow-ups.

Open on alphaXiv

alphaXiv is searching for related work…

CDF Collaboration, Phys. Rev. Lett. 104

Original

2010

Cited alongside, same era.

Stephen D. Ellis, Christopher K. Vermilion, and Jonathan R. Walsh, “Recombination Algorithms and Jet Substructure: Pruning as a Tool for Heavy Particle Searches,” Phys. Rev. D 81

Original

2010

Cited alongside, same era.

Tilman Plehn, Michael Spannowsky, Michihisa Takeuchi, and Dirk Zerwas, “Stop Reconstruction with Tagged Tops,” JHEP 1010

Original

2010

Cited alongside, same era.

David Krohn, Jesse Thaler, and Lian-Tao Wang, “Jet Trimming,” JHEP 02

Original

2010

Cited alongside, same era.

A. Abdesselam et al. , “Boosted objects: A Probe of beyond the Standard Model physics,” Boost 2010 Oxford, United Kingdom, June 22-25, 2010 , Eur. Phys. J. C71

Original

2011

Cited alongside, same era.

Jesse Thaler and Ken Van Tilburg, “Identifying Boosted Objects with N-subjettiness,” JHEP 03

Original

2011

Cited alongside, same era.

Johan Alwall, Michel Herquet, Fabio Maltoni, Olivier Mattelaer, and Tim Stelzer, “MadGraph 5 : Going Beyond,” JHEP 1106

Original

2011

Cited alongside, same era.

Guy Gur-Ari, Michele Papucci, and Gilad Perez, “Classification of Energy Flow Observables in Narrow Jets,” (2011), arXiv:1101.2905 [hep-ph]

Original

2011

Cited alongside, same era.

CMS Collaboration, Heavy flavor identification at CMS with deep neural networks , Tech. Rep. CMS-DP-2017-005 (2017)

2017