New angles on energy correlation functions

Moult, Ian; Necib, Lina; Thaler, Jesse

doi:10.1007/JHEP12(2016)153

Title: New angles on energy correlation functions

Journal Article · Thu Dec 29 00:00:00 EST 2016 · Journal of High Energy Physics (Online)

DOI:https://doi.org/10.1007/JHEP12(2016)153· OSTI ID:1379622

Moult, Ian ^[1]; Necib, Lina ^[2]; Thaler, Jesse ^[2]

Univ. of California, Berkeley, CA (United States); Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States); Massachusetts Inst. of Technology (MIT), Cambridge, MA (United States)
Massachusetts Inst. of Technology (MIT), Cambridge, MA (United States)

Jet substructure observables, designed to identify specific features within jets, play an essential role at the Large Hadron Collider (LHC), both for searching for signals beyond the Standard Model and for testing QCD in extreme phase space regions. In this paper, we systematically study the structure of infrared and collinear safe substructure observables, defining a generalization of the energy correlation functions to probe n-particle correlations within a jet. These generalized correlators provide a flexible basis for constructing new substructure observables optimized for specific purposes. Focusing on three major targets of the jet substructure community — boosted top tagging, boosted W/Z/H tagging, and quark/gluon discrimination — we use power-counting techniques to identify three new series of powerful discriminants: M_i, N_i, and U_i. The Mi series is designed for use on groomed jets, providing a novel example of observables with improved discrimination power after the removal of soft radiation. The N_i series behave parametrically like the N -subjettiness ratio observables, but are defined without respect to subjet axes, exhibiting improved behavior in the unresolved limit. Finally, the U_i series improves quark/gluon discrimination by using higher-point correlators to simultaneously probe multiple emissions within a jet. Taken together, these observables broaden the scope for jet substructure studies at the LHC.

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Research Organization:: Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)

Sponsoring Organization:: USDOE Office of Science (SC)

Grant/Contract Number:: AC02-05CH11231

OSTI ID:: 1379622

Journal Information:: Journal of High Energy Physics (Online), Vol. 2016, Issue 12; ISSN 1029-8479

Publisher:: Springer BerlinCopyright Statement

Country of Publication:: United States

Language:: English

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Cited by: 89 works

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Jet substructure with analytical methods Dasgupta, Mrinal; Fregoso, Alessandro; Marzani, Simone arXiv https://doi.org/10.48550/arxiv.1307.0013	text	January 2013
Unsafe but Calculable: Ratios of Angularities in Perturbative QCD Larkoski, Andrew J.; Thaler, Jesse arXiv https://doi.org/10.48550/arxiv.1307.1699	text	January 2013
Jet-Images: Computer Vision Inspired Techniques for Jet Tagging Cogan, Josh; Kagan, Michael; Strauss, Emanuel arXiv https://doi.org/10.48550/arxiv.1407.5675	text	January 2014
Pileup Per Particle Identification Bertolini, Daniele; Harris, Philip; Low, Matthew arXiv https://doi.org/10.48550/arxiv.1407.6013	text	January 2014
Associated jet and subjet rates in light-quark and gluon jet discrimination Bhattacherjee, Biplob; Mukhopadhyay, Satyanarayan; Nojiri, Mihoko M. arXiv https://doi.org/10.48550/arxiv.1501.04794	text	January 2015
On jet substructure methods for signal jets Dasgupta, Mrinal; Powling, Alexander; Siodmok, Andrzej arXiv https://doi.org/10.48550/arxiv.1503.01088	text	January 2015
Tracking down hyper-boosted top quarks Larkoski, Andrew J.; Maltoni, Fabio; Selvaggi, Michele arXiv https://doi.org/10.48550/arxiv.1503.03347	preprint	January 2015
N-jettiness Subtractions for NNLO QCD Calculations Gaunt, Jonathan; Stahlhofen, Maximilian; Tackmann, Frank J. arXiv https://doi.org/10.48550/arxiv.1505.04794	preprint	January 2015
Factorization for groomed jet substructure beyond the next-to-leading logarithm Frye, Christopher; Larkoski, Andrew J.; Schwartz, Matthew D. arXiv https://doi.org/10.48550/arxiv.1603.09338	text	January 2016
Factorization for Jet Radius Logarithms in Jet Mass Spectra at the LHC Kolodrubetz, Daniel W.; Pietrulewicz, Piotr; Stewart, Iain W. arXiv https://doi.org/10.48550/arxiv.1605.08038	text	January 2016
A new tagger for hadronically decaying heavy particles at the LHC Lapsien, Tobias; Kogler, Roman; Haller, Johannes arXiv https://doi.org/10.48550/arxiv.1606.04961	text	January 2016
A study of the energy evolution of event shape distributions and their means with the DELPHI detector at LEP Collaboration, The DELPHI; Abdallah, J. arXiv https://doi.org/10.48550/arxiv.hep-ex/0307048	text	January 2003
On power corrections to the event shape distributions in QCD Korchemsky, G. P.; Tafat, S. arXiv https://doi.org/10.48550/arxiv.hep-ph/0007005	text	January 2000
Soft-Collinear Factorization in Effective Field Theory Bauer, Christian W.; Pirjol, Dan; Stewart, Iain W. arXiv https://doi.org/10.48550/arxiv.hep-ph/0109045	text	January 2001
Jets and Quantum Field Theory Sveshnikov, N. A.; Tkachov, F. V. arXiv https://doi.org/10.48550/arxiv.hep-ph/9512370	text	January 1995
Measuring Multijet Structure of Hadronic Energy Flow Or What IS A Jet? Tkachov, Fyodor V. arXiv https://doi.org/10.48550/arxiv.hep-ph/9601308	text	January 1996
On the QCD analysis of Jet Broadening Dokshitzer, Yu. L.; Lucenti, A.; Marchesini, G. arXiv https://doi.org/10.48550/arxiv.hep-ph/9801324	text	January 1998
Power corrections to event shapes and factorization Korchemsky, G. P.; Sterman, G. arXiv https://doi.org/10.48550/arxiv.hep-ph/9902341	text	January 1999
Evolution of average multiplicities of quark and gluon jets Capella, A.; Dremin, I. M.; Gary, J. W. arXiv https://doi.org/10.48550/arxiv.hep-ph/9910226	text	January 1999

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Subleading power factorization with radiative functions Moult, Ian; Stewart, Iain W.; Vita, Gherardo Journal of High Energy Physics, Vol. 2019, Issue 11 https://doi.org/10.1007/jhep11(2019)153	journal	November 2019
Search for dark matter produced in association with a Higgs boson decaying to a pair of bottom quarks in proton–proton collisions at $$\sqrt{s}=13\,\text {Te}\text {V} $$ s = 13 Te Sirunyan, A. M.; Tumasyan, A.; Adam, W. The European Physical Journal C, Vol. 79, Issue 3 https://doi.org/10.1140/epjc/s10052-019-6730-7	journal	March 2019
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Inclusive search for a highly boosted Higgs boson decaying to a bottom quark-antiquark pair Sirunyan, Albert M.; Tumasyan, Armen; Adam, Wolfgang RWTH Aachen University https://doi.org/10.18154/rwth-2018-224218	text	January 2018
Quark jet versus gluon jet: fully-connected neural networks with high-level features Luo, Hui; Luo, Ming-Xing; Wang, Kai Science China Physics, Mechanics & Astronomy, Vol. 62, Issue 9 https://doi.org/10.1007/s11433-019-9390-8	journal	June 2019
Identification of heavy, energetic, hadronically decaying particles using machine-learning techniques Sirunyan, Albert M.; Tumasyan, Armen; Adam, Wolfgang Deutsches Elektronen-Synchrotron, DESY, Hamburg https://doi.org/10.3204/pubdb-2020-02644	text	January 2020
Search for low-mass resonances decaying into bottom quark-antiquark pairs in proton-proton collisions at √s 13 TeV Sirunyan, A. M.; Tumasyan, A.; Adam, W. Karlsruhe https://doi.org/10.5445/ir/1000089891	text	January 2019
An equation-of-state-meter of quantum chromodynamics transition from deep learning Pang, Long-Gang; Zhou, Kai; Su, Nan Nature Communications, Vol. 9, Issue 1 https://doi.org/10.1038/s41467-017-02726-3	journal	January 2018
Search for dark matter produced in association with a Higgs boson decaying to a pair of bottom quarks in proton–proton collisions at $\sqrt{s}=13\,\text {Te}\text {V} $ Sirunyan, Albert M.; Tumasyan, Armen; Adam, Wolfgang Deutsches Elektronen-Synchrotron, DESY, Hamburg https://doi.org/10.3204/pubdb-2019-02795	text	January 2019
Measurement of jet substructure observables in t¯t events from proton-proton collisions at √s=13 TeV Collaboration, Cms; Sirunyan, Albert M.; Backhaus, Malte ETH Zurich https://doi.org/10.3929/ethz-b-000311069	text	January 2018
Search for Low-Mass Quark-Antiquark Resonances Produced in Association with a Photon at √s = 13 TeV Sirunyan, A. M.; Tumasyan, A.; Adam, W. Karlsruhe https://doi.org/10.5445/ir/1000100634	text	January 2019
The Machine Learning landscape of top taggers Kasieczka, Gregor; Plehn, Tilman; Butter, Anja RWTH Aachen University https://doi.org/10.18154/rwth-2019-07568	text	January 2019
Identification of heavy, energetic, hadronically decaying particles using machine-learning techniques Collaboration, Cms; Sirunyan, Albert M.; Tumasyan, Armen R. ETH Zurich https://doi.org/10.3929/ethz-b-000429003	text	January 2020
Search for dark matter produced in association with a Higgs boson decaying to a pair of bottom quarks in proton-proton collisions at $\sqrt{s}=13\,\text {Te}\text {V}$ Collaboration, Cms; Canelli, Maria Florencia; Kilminster, Benjamin Springer https://doi.org/10.5167/uzh-180051	text	January 2019
Inclusive Search for a Highly Boosted Higgs Boson Decaying to a Bottom Quark-Antiquark Pair Sirunyan, A. M.; Tumasyan, A.; Adam, W. American Physical Society (APS) https://doi.org/10.5445/ir/1000081229	text	January 2018
Aspects of track-assisted mass Elder, Benjamin T.; Thaler, Jesse Journal of High Energy Physics, Vol. 2019, Issue 3 https://doi.org/10.1007/jhep03(2019)104	journal	March 2019
Search for low-mass resonances decaying into bottom quark-antiquark pairs in proton-proton collisions at $\sqrt{s} = 13 TeV$ Sirunyan, A. M.; Tumasyan, A.; Adam, W. Physical Review D, Vol. 99, Issue 1 https://doi.org/10.1103/physrevd.99.012005	journal	January 2019
Systematics of quark/gluon tagging Gras, Philippe; Höche, Stefan; Kar, Deepak arXiv https://doi.org/10.48550/arxiv.1704.03878	text	January 2017
Jet Substructure Studies with CMS Open Data Tripathee, Aashish; Xue, Wei; Larkoski, Andrew arXiv https://doi.org/10.48550/arxiv.1704.05842	text	January 2017
Convolved Substructure: Analytically Decorrelating Jet Substructure Observables Moult, Ian; Nachman, Benjamin; Neill, Duff arXiv https://doi.org/10.48550/arxiv.1710.06859	text	January 2017
Energy flow polynomials: A complete linear basis for jet substructure Komiske, Patrick T.; Metodiev, Eric M.; Thaler, Jesse arXiv https://doi.org/10.48550/arxiv.1712.07124	text	January 2017
Interpretable Deep Learning for Two-Prong Jet Classification with Jet Spectra Chakraborty, Amit; Lim, Sung Hak; Nojiri, Mihoko M. arXiv https://doi.org/10.48550/arxiv.1904.02092	text	January 2019
Lund jet images from generative and cycle-consistent adversarial networks Carrazza, Stefano; Dreyer, Frédéric A. arXiv https://doi.org/10.48550/arxiv.1909.01359	text	January 2019

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