(CMS Collaboration) Sirunyan, Albert M; Tumasyan, A.; Antunović, Željko; Brigljević, Vuko; Ferenček, Dinko; Giljanović, Duje; Godinović, Nikola; Kadija, Krešo; Kovač, Marko; Lelas, Damir; Majumder, Devdatta; Mesić, Benjamin; Puljak, Ivica; Roguljić, Matej; Starodumov, Andrey; Đurić, Senka; Šuša, Tatjana; Šćulac, Toni; Trembath-Reichert, Stephen (2020) Identification of heavy, energetic, hadronically decaying particles using machine-learning techniques. Journal of Instrumentation, 15 (6). ISSN 1748-0221
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Abstract
Machine-learning (ML) techniques are explored to identify and classify hadronic decays of highly Lorentz-boosted W/Z/Higgs bosons and top quarks. Techniques without ML have also been evaluated and are included for comparison. The identification performances of a variety of algorithms are characterized in simulated events and directly compared with data. The algorithms are validated using proton-proton collision data at $\sqrt{; ; s}; ; =$ 13 TeV, corresponding to an integrated luminosity of 35.9 fb$^{; ; -1}; ; $. Systematic uncertainties are assessed by comparing the results obtained using simulation and collision data. The new techniques studied in this paper provide significant performance improvements over non-ML techniques, reducing the background rate by up to an order of magnitude at the same signal efficiency.
| Item Type: | Article |
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| Uncontrolled Keywords: | High energy physics ; Experimental particle physics ; LHC ; CMS ; Particle Physics Experiments ; Physics ; Vector boson scattering ; Hadron-Hadron scattering (experiments) ; Supersymmetry ; Higgs physics ; Particle and resonance production ; B physics ; Particle correlations and fluctuations ; Quarkonium ; Elementary Particles and Fields ; Beyond Standard Model ; Jets ; QCD ; Top physics ; Diboson ; Electroweak ; CKM matrix ; Top quark ; Large detector-systems performance ; Pattern recognition |
| Subjects: | NATURAL SCIENCES > Physics |
| Divisions: | Division of Experimental Physics |
| Depositing User: | Vuko Brigljević |
| Date Deposited: | 08 Feb 2021 12:20 |
| URI: | http://fulir.irb.hr/id/eprint/6312 |
| DOI: | 10.1088/1748-0221/15/06/P06005 |
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