The LHC Olympics 2020 a community challenge for anomaly detection in high energy physics

Kasieczka, Gregor; Nachman, Benjamin; Shih, David; Amram, Oz; Andreassen, Anders; Benkendorfer, Kees; Bortolato, Blaz; Brooijmans, Gustaaf; Canelli, Florencia; Collins, Jack H.; Dai, Biwei; De Freitas, Felipe F.; Dillon, Barry M.; Dinu, Ioan-Mihail; Dong, Zhongtian; Donini, Julien; Duarte, Javier; Faroughy, D. A.; Gonski, Julia; Harris, Philip; Kahn, Alan; Kamenik, Jernej F.; Khosa, Charanjit K.; Komiske, Patrick; Le Pottier, Luc; Martín-Ramiro, Pablo; Matevc, Andrej; Metodiev, Eric; Mikuni, Vinicius; Murphy, Christopher W.; Ochoa, Inês; Park, Sang Eon; Pierini, Maurizio; Rankin, Dylan; Sanz, Veronica; Sarda, Nilai; Seljak, Urŏ; Smolkovic, Aleks; Stein, George; Suarez, Cristina Mantilla; Szewc, Manuel; Thaler, Jesse; Tsan, Steven; Udrescu, Silviu-Marian; Vaslin, Louis; Vlimant, Jean-Roch; Williams, Daniel; Yunus, Mikaeel

doi:10.1088/1361-6633/ac36b9

Title: The LHC Olympics 2020 a community challenge for anomaly detection in high energy physics

Abstract

A new paradigm for data-driven, model-agnostic new physics searches at colliders is emerging, and aims to leverage recent breakthroughs in anomaly detection and machine learning. In order to develop and benchmark new anomaly detection methods within this framework, it is essential to have standard datasets. To this end, we have created the LHC Olympics 2020, a community challenge accompanied by a set of simulated collider events. Participants in these Olympics have developed their methods using an R&D dataset and then tested them on black boxes: datasets with an unknown anomaly (or not). Furthermore, methods made use of modern machine learning tools and were based on unsupervised learning (autoencoders, generative adversarial networks, normalizing flows), weakly supervised learning, and semi-supervised learning. This paper will review the LHC Olympics 2020 challenge, including an overview of the competition, a description of methods deployed in the competition, lessons learned from the experience, and implications for data analyses with future datasets as well as future colliders.

Authors:

;

; Andreassen, Anders; Benkendorfer, Kees; Bortolato, Blaz; Brooijmans, Gustaaf; Canelli, Florencia; Collins, Jack H.;

; De Freitas, Felipe F.; Dillon, Barry M.;

; Dong, Zhongtian;

;

; Faroughy, D. A.; Gonski, Julia;

Publication Date:: Tue Dec 07 00:00:00 EST 2021

Research Org.:: Massachusetts Inst. of Technology (MIT), Cambridge, MA (United States); Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States); SLAC National Accelerator Laboratory (SLAC), Menlo Park, CA (United States)

Sponsoring Org.:: USDOE Office of Science (SC), High Energy Physics (HEP)

Contributing Org.:: LHC Olympics Challenge Team

OSTI Identifier:: 1909683

Alternate Identifier(s):: OSTI ID: 1863790; OSTI ID: 1867888

Grant/Contract Number:: SC0011090; SC0012567; AC02-05CH11231; AC02-76SF00515

Resource Type:: Accepted Manuscript

Journal Name:: Reports on Progress in Physics

Additional Journal Information:: Journal Volume: 84; Journal Issue: 12; Journal ID: ISSN 0034-4885

Publisher:: IOP Publishing

Country of Publication:: United States

Language:: English

Subject:: 72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS

Citation Formats


                    Kasieczka, Gregor, Nachman, Benjamin, Shih, David, Amram, Oz, Andreassen, Anders, Benkendorfer, Kees, Bortolato, Blaz, Brooijmans, Gustaaf, Canelli, Florencia, Collins, Jack H., Dai, Biwei, De Freitas, Felipe F., Dillon, Barry M., Dinu, Ioan-Mihail, Dong, Zhongtian, Donini, Julien, Duarte, Javier, Faroughy, D. A., Gonski, Julia, Harris, Philip, Kahn, Alan, Kamenik, Jernej F., Khosa, Charanjit K., Komiske, Patrick, Le Pottier, Luc, Martín-Ramiro, Pablo, Matevc, Andrej, Metodiev, Eric, Mikuni, Vinicius, Murphy, Christopher W., Ochoa, Inês, Park, Sang Eon, Pierini, Maurizio, Rankin, Dylan, Sanz, Veronica, Sarda, Nilai, Seljak, Urŏ, Smolkovic, Aleks, Stein, George, Suarez, Cristina Mantilla, Szewc, Manuel, Thaler, Jesse, Tsan, Steven, Udrescu, Silviu-Marian, Vaslin, Louis, Vlimant, Jean-Roch, Williams, Daniel, and Yunus, Mikaeel. The LHC Olympics 2020 a community challenge for anomaly detection in high energy physics.  United States: N. p., 2021. 
Web.  doi:10.1088/1361-6633/ac36b9.

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                    Kasieczka, Gregor, Nachman, Benjamin, Shih, David, Amram, Oz, Andreassen, Anders, Benkendorfer, Kees, Bortolato, Blaz, Brooijmans, Gustaaf, Canelli, Florencia, Collins, Jack H., Dai, Biwei, De Freitas, Felipe F., Dillon, Barry M., Dinu, Ioan-Mihail, Dong, Zhongtian, Donini, Julien, Duarte, Javier, Faroughy, D. A., Gonski, Julia, Harris, Philip, Kahn, Alan, Kamenik, Jernej F., Khosa, Charanjit K., Komiske, Patrick, Le Pottier, Luc, Martín-Ramiro, Pablo, Matevc, Andrej, Metodiev, Eric, Mikuni, Vinicius, Murphy, Christopher W., Ochoa, Inês, Park, Sang Eon, Pierini, Maurizio, Rankin, Dylan, Sanz, Veronica, Sarda, Nilai, Seljak, Urŏ, Smolkovic, Aleks, Stein, George, Suarez, Cristina Mantilla, Szewc, Manuel, Thaler, Jesse, Tsan, Steven, Udrescu, Silviu-Marian, Vaslin, Louis, Vlimant, Jean-Roch, Williams, Daniel, & Yunus, Mikaeel. The LHC Olympics 2020 a community challenge for anomaly detection in high energy physics.  United States.  https://doi.org/10.1088/1361-6633/ac36b9

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                    Kasieczka, Gregor, Nachman, Benjamin, Shih, David, Amram, Oz, Andreassen, Anders, Benkendorfer, Kees, Bortolato, Blaz, Brooijmans, Gustaaf, Canelli, Florencia, Collins, Jack H., Dai, Biwei, De Freitas, Felipe F., Dillon, Barry M., Dinu, Ioan-Mihail, Dong, Zhongtian, Donini, Julien, Duarte, Javier, Faroughy, D. A., Gonski, Julia, Harris, Philip, Kahn, Alan, Kamenik, Jernej F., Khosa, Charanjit K., Komiske, Patrick, Le Pottier, Luc, Martín-Ramiro, Pablo, Matevc, Andrej, Metodiev, Eric, Mikuni, Vinicius, Murphy, Christopher W., Ochoa, Inês, Park, Sang Eon, Pierini, Maurizio, Rankin, Dylan, Sanz, Veronica, Sarda, Nilai, Seljak, Urŏ, Smolkovic, Aleks, Stein, George, Suarez, Cristina Mantilla, Szewc, Manuel, Thaler, Jesse, Tsan, Steven, Udrescu, Silviu-Marian, Vaslin, Louis, Vlimant, Jean-Roch, Williams, Daniel, and Yunus, Mikaeel. Tue .  
"The LHC Olympics 2020 a community challenge for anomaly detection in high energy physics".  United States.  https://doi.org/10.1088/1361-6633/ac36b9.  https://www.osti.gov/servlets/purl/1909683.

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@article{osti_1909683,

  title        = {The LHC Olympics 2020 a community challenge for anomaly detection in high energy physics},

  author       = {Kasieczka, Gregor and Nachman, Benjamin and Shih, David and Amram, Oz and Andreassen, Anders and Benkendorfer, Kees and Bortolato, Blaz and Brooijmans, Gustaaf and Canelli, Florencia and Collins, Jack H. and Dai, Biwei and De Freitas, Felipe F. and Dillon, Barry M. and Dinu, Ioan-Mihail and Dong, Zhongtian and Donini, Julien and Duarte, Javier and Faroughy, D. A. and Gonski, Julia and Harris, Philip and Kahn, Alan and Kamenik, Jernej F. and Khosa, Charanjit K. and Komiske, Patrick and Le Pottier, Luc and Martín-Ramiro, Pablo and Matevc, Andrej and Metodiev, Eric and Mikuni, Vinicius and Murphy, Christopher W. and Ochoa, Inês and Park, Sang Eon and Pierini, Maurizio and Rankin, Dylan and Sanz, Veronica and Sarda, Nilai and Seljak, Urŏ and Smolkovic, Aleks and Stein, George and Suarez, Cristina Mantilla and Szewc, Manuel and Thaler, Jesse and Tsan, Steven and Udrescu, Silviu-Marian and Vaslin, Louis and Vlimant, Jean-Roch and Williams, Daniel and Yunus, Mikaeel},

  abstractNote = {A new paradigm for data-driven, model-agnostic new physics searches at colliders is emerging, and aims to leverage recent breakthroughs in anomaly detection and machine learning. In order to develop and benchmark new anomaly detection methods within this framework, it is essential to have standard datasets. To this end, we have created the LHC Olympics 2020, a community challenge accompanied by a set of simulated collider events. Participants in these Olympics have developed their methods using an R&D dataset and then tested them on black boxes: datasets with an unknown anomaly (or not). Furthermore, methods made use of modern machine learning tools and were based on unsupervised learning (autoencoders, generative adversarial networks, normalizing flows), weakly supervised learning, and semi-supervised learning. This paper will review the LHC Olympics 2020 challenge, including an overview of the competition, a description of methods deployed in the competition, lessons learned from the experience, and implications for data analyses with future datasets as well as future colliders.},

  doi          = {10.1088/1361-6633/ac36b9},

  journal      = {Reports on Progress in Physics},

  number       = 12,

  volume       = 84,

  place        = {United States},

  year         = {Tue Dec 07 00:00:00 EST 2021},

  month        = {Tue Dec 07 00:00:00 EST 2021}

}

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Uncovering latent jet substructure
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Title: The LHC Olympics 2020 a community challenge for anomaly detection in high energy physics

Abstract

Citation Formats

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

A general search for new phenomena in ep scattering at HERA journal, November 2004

RECAST — extending the impact of existing analyses journal, April 2011

Official Datasets for LHC Olympics 2020 Anomaly Detection Challenge dataset, January 2019

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

Official Datasets for LHC Olympics 2020 Anomaly Detection Challenge dataset, January 2019

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

Quasi-model-independent search for new physics at large transverse momentum journal, June 2001

Focal Loss for Dense Object Detection conference, October 2017

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

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Flavor universal resonances and warped gravity journal, January 2017

R&D Dataset for LHC Olympics 2020 Anomaly Detection Challenge dataset, January 2019

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

Learning new physics from a machine journal, January 2019

DELPHES 3: A modular framework for fast-simulation of generic collider experiments journal, June 2014

Observation of a new boson at a mass of 125 GeV with the CMS experiment at the LHC journal, September 2012

PYTHIA 6.4 physics and manual journal, May 2006

Observation of a new particle in the search for the Standard Model Higgs boson with the ATLAS detector at the LHC journal, September 2012

OmniFold: A Method to Simultaneously Unfold All Observables journal, May 2020

Mining gold from implicit models to improve likelihood-free inference journal, February 2020

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

A general search for new phenomena at HERA journal, April 2009

ABCNet: An attention-based method for particle tagging text, January 2020

Pileup per particle identification journal, October 2014

A measurement of the calorimeter response to single hadrons and determination of the jet energy scale uncertainty using LHC Run-1 pp-collision data with the ATLAS detector journal, January 2017

New features in Delphes 3 journal, May 2015

Dynamic Graph CNN for Learning on Point Clouds journal, October 2019

Search for narrow and broad dijet resonances in proton-proton collisions at s = 13 $$ \sqrt{s}=13 $$ TeV and constraints on dark matter mediators and other new particles journal, August 2018

Uncovering latent jet substructure journal, September 2019

Pion-Pion Interaction in the Reaction p ¯ + p → 2 π + + 2 π − + n π 0 journal, June 1962

Herwig++ physics and manual journal, November 2008

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

How to GAN LHC events journal, January 2019

Semi-supervised anomaly detection – towards model-independent searches of new physics journal, June 2012

DELPHES 3: A modular framework for fast-simulation of generic collider experiments journal, June 2014

Search for new resonances in mass distributions of jet pairs using 139 fb−1 of pp collisions at s$$ \sqrt{\mathrm{s}} $$ = 13 TeV with the ATLAS detector journal, March 2020

Adversarially-trained autoencoders for robust unsupervised new physics searches journal, October 2019

Simulation assisted likelihood-free anomaly detection journal, May 2020

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

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

Model-independent and quasi-model-independent search for new physics at CDF journal, July 2008

ABCNet: an attention-based method for particle tagging journal, June 2020

Measurement of the t t ‾ b b ‾ production cross section in the all-jet final state in pp collisions at s = 13 TeV journal, April 2020

On hypothesis testing, trials factor, hypertests and the BumpHunter preprint, January 2011

The Machine Learning landscape of top taggers journal, January 2019

Properties of jet fragmentation using charged particles measured with the ATLAS detector in p p collisions at s = 13 TeV journal, September 2019

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

An operational definition of quark and gluon jets text, January 2018

IPython: A System for Interactive Scientific Computing journal, January 2007

Trial factors for the look elsewhere effect in high energy physics journal, October 2010

Learning the latent structure of collider events journal, October 2020

Muon reconstruction performance of the ATLAS detector in proton–proton collision data at $$\sqrt{s}$$ s =13 TeV journal, May 2016

A brief introduction to weakly supervised learning journal, August 2017

Algorithm AS 136: A K-Means Clustering Algorithm journal, January 1979

Global search for new physics with 2.0 fb − 1 at CDF journal, January 2009

An introduction to PYTHIA 8.2 journal, June 2015

Identifying boosted objects with N-subjettiness journal, March 2011

GAPNet: Graph Attention based Point Neural Network for Exploiting Local Feature of Point Cloud preprint, January 2019

The Unexplored Landscape of Two-body Resonances journal, January 2019

Quasi-Model-Independent Search for New High p T Physics at D0 journal, April 2001

Search for massive resonances decaying into WW, WZ or ZZ bosons in proton-proton collisions at s = 13 $$ \sqrt{s}=13 $$ TeV journal, March 2017

Deep Learning and its Application to LHC Physics text, January 2018

The RooStats project conference, February 2011

The motivation and status of two-body resonance decays after the LHC Run 2 and beyond journal, April 2020

Robotic swarm cooperation by co-adaptation conference, July 2012

Adversarially-trained autoencoders for robust unsupervised new physics searches text, January 2019

A strategy for a general search for new phenomena using data-derived signal regions and its application within the ATLAS experiment journal, February 2019

Uncovering latent jet substructure text, January 2019

Search for new physics in e μ X data at DØ using SLEUTH: A quasi-model-independent search strategy for new physics journal, October 2000

Search for diboson resonances with boson-tagged jets in pp collisions at s = 13 TeV with the ATLAS detector journal, February 2018

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

A general search for new phenomena in ep scattering at HERA
journal, November 2004

RECAST — extending the impact of existing analyses
journal, April 2011

Official Datasets for LHC Olympics 2020 Anomaly Detection Challenge
dataset, January 2019

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

Official Datasets for LHC Olympics 2020 Anomaly Detection Challenge
dataset, January 2019

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

Quasi-model-independent search for new physics at large transverse momentum
journal, June 2001

Focal Loss for Dense Object Detection
conference, October 2017

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

Neural networks for full phase-space reweighting and parameter tuning
journal, May 2020

Flavor universal resonances and warped gravity
journal, January 2017

R&D Dataset for LHC Olympics 2020 Anomaly Detection Challenge
dataset, January 2019

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

Learning new physics from a machine
journal, January 2019

DELPHES 3: A modular framework for fast-simulation of generic collider experiments
journal, June 2014

Observation of a new boson at a mass of 125 GeV with the CMS experiment at the LHC
journal, September 2012

PYTHIA 6.4 physics and manual
journal, May 2006

Observation of a new particle in the search for the Standard Model Higgs boson with the ATLAS detector at the LHC
journal, September 2012

OmniFold: A Method to Simultaneously Unfold All Observables
journal, May 2020

Mining gold from implicit models to improve likelihood-free inference
journal, February 2020

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

A general search for new phenomena at HERA
journal, April 2009

ABCNet: An attention-based method for particle tagging
text, January 2020

Pileup per particle identification
journal, October 2014

A measurement of the calorimeter response to single hadrons and determination of the jet energy scale uncertainty using LHC Run-1 pp-collision data with the ATLAS detector
journal, January 2017

New features in Delphes 3
journal, May 2015

Dynamic Graph CNN for Learning on Point Clouds
journal, October 2019

Search for narrow and broad dijet resonances in proton-proton collisions at s = 13 $$ \sqrt{s}=13 $$ TeV and constraints on dark matter mediators and other new particles
journal, August 2018

Uncovering latent jet substructure
journal, September 2019

Pion-Pion Interaction in the Reaction $\bar{p} + p \to 2 π^{+} + 2 π^{-} + n π^{0}$
journal, June 1962

Herwig++ physics and manual
journal, November 2008

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

How to GAN LHC events
journal, January 2019

Semi-supervised anomaly detection – towards model-independent searches of new physics
journal, June 2012

DELPHES 3: A modular framework for fast-simulation of generic collider experiments
journal, June 2014

Search for new resonances in mass distributions of jet pairs using 139 fb−1 of pp collisions at s$$ \sqrt{\mathrm{s}} $$ = 13 TeV with the ATLAS detector
journal, March 2020

Adversarially-trained autoencoders for robust unsupervised new physics searches
journal, October 2019

Simulation assisted likelihood-free anomaly detection
journal, May 2020

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

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

Model-independent and quasi-model-independent search for new physics at CDF
journal, July 2008

ABCNet: an attention-based method for particle tagging
journal, June 2020

Measurement of the $t \overline{t} b \overline{b}$ production cross section in the all-jet final state in pp collisions at $\sqrt{s} = 13 TeV$
journal, April 2020

On hypothesis testing, trials factor, hypertests and the BumpHunter
preprint, January 2011

The Machine Learning landscape of top taggers
journal, January 2019

Properties of jet fragmentation using charged particles measured with the ATLAS detector in $p p$ collisions at $\sqrt{s} = 13 TeV$
journal, September 2019

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

An operational definition of quark and gluon jets
text, January 2018

IPython: A System for Interactive Scientific Computing
journal, January 2007

Trial factors for the look elsewhere effect in high energy physics
journal, October 2010

Learning the latent structure of collider events
journal, October 2020

Muon reconstruction performance of the ATLAS detector in proton–proton collision data at $$\sqrt{s}$$ s =13 TeV
journal, May 2016

A brief introduction to weakly supervised learning
journal, August 2017

Algorithm AS 136: A K-Means Clustering Algorithm
journal, January 1979

Global search for new physics with $2.0 {fb}^{- 1}$ at CDF
journal, January 2009

An introduction to PYTHIA 8.2
journal, June 2015

Identifying boosted objects with N-subjettiness
journal, March 2011

GAPNet: Graph Attention based Point Neural Network for Exploiting Local Feature of Point Cloud
preprint, January 2019

The Unexplored Landscape of Two-body Resonances
journal, January 2019

Quasi-Model-Independent Search for New High $p_{T}$ Physics at D0
journal, April 2001

Search for massive resonances decaying into WW, WZ or ZZ bosons in proton-proton collisions at s = 13 $$ \sqrt{s}=13 $$ TeV
journal, March 2017

Deep Learning and its Application to LHC Physics
text, January 2018

The RooStats project
conference, February 2011

The motivation and status of two-body resonance decays after the LHC Run 2 and beyond
journal, April 2020

Robotic swarm cooperation by co-adaptation
conference, July 2012

Adversarially-trained autoencoders for robust unsupervised new physics searches
text, January 2019

A strategy for a general search for new phenomena using data-derived signal regions and its application within the ATLAS experiment
journal, February 2019

Uncovering latent jet substructure
text, January 2019

Search for new physics in $e μ X$ data at DØ using SLEUTH: A quasi-model-independent search strategy for new physics
journal, October 2000

Search for diboson resonances with boson-tagged jets in pp collisions at $\sqrt{s} = 13 TeV$ with the ATLAS detector
journal, February 2018