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Pregled bibliografske jedinice broj: 1108537

Identification of heavy, energetic, hadronically decaying particles using machine-learning techniques

(CMS Collaboration) Sirunyan, Albert M; ...; Antunović, Željko; Brigljević, Vuko; Ferenček, Dinko; Giljanović, Duje; Godinović, Nikola; Kadija, Krešo; Kovač, Marko; Lelas, Damir et al.
Identification of heavy, energetic, hadronically decaying particles using machine-learning techniques // Journal of Instrumentation, 15 (2020), 6; P06005, 87 doi:10.1088/1748-0221/15/06/P06005 (međunarodna recenzija, članak, znanstveni)

CROSBI ID: 1108537 Za ispravke kontaktirajte CROSBI podršku putem web obrasca

Naslov
Identification of heavy, energetic, hadronically decaying particles using machine-learning techniques

Autori
Sirunyan, Albert M ; ... ; 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

Kolaboracija
CMS Collaboration

Izvornik
Journal of Instrumentation (1748-0221) 15 (2020), 6; P06005, 87

Vrsta, podvrsta i kategorija rada
Radovi u časopisima, članak, znanstveni

Ključne riječi
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

Sažetak
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.

Izvorni jezik
Engleski

Znanstvena područja
Fizika



POVEZANOST RADA

Ustanove:
Fakultet elektrotehnike, strojarstva i brodogradnje, Split,
Institut "Ruđer Bošković", Zagreb,
Prirodoslovno-matematički fakultet, Split

Profili:

Avatar Url Matej Roguljić (autor)

Avatar Url Damir Lelas (autor)

Avatar Url Krešo Kadija (autor)

Avatar Url Toni Šćulac (autor)

Avatar Url Vuko Brigljević (autor)

Avatar Url Željko Antunović (autor)

Avatar Url Marko Kovač (autor)

Avatar Url Duje Giljanović (autor)

Avatar Url Ivica Puljak (autor)

Avatar Url Tatjana Šuša (autor)

Avatar Url Nikola Godinović (autor)

Avatar Url Benjamin Mesić (autor)

Avatar Url Dinko Ferenček (autor)

Avatar Url Devdatta Majumder (autor)

Avatar Url Senka Đurić (autor)

Poveznice na cjeloviti tekst rada:

Pristup cjelovitom tekstu rada doi iopscience.iop.org fulir.irb.hr

Citiraj ovu publikaciju:

(CMS Collaboration) Sirunyan, Albert M; ...; Antunović, Željko; Brigljević, Vuko; Ferenček, Dinko; Giljanović, Duje; Godinović, Nikola; Kadija, Krešo; Kovač, Marko; Lelas, Damir et al.
Identification of heavy, energetic, hadronically decaying particles using machine-learning techniques // Journal of Instrumentation, 15 (2020), 6; P06005, 87 doi:10.1088/1748-0221/15/06/P06005 (međunarodna recenzija, članak, znanstveni)
(CMS Collaboration) (CMS Collaboration) Sirunyan, A., ..., Antunović, Ž., Brigljević, V., Ferenček, D., Giljanović, D., Godinović, N., Kadija, K., Kovač, M. & Lelas, D. (2020) Identification of heavy, energetic, hadronically decaying particles using machine-learning techniques. Journal of Instrumentation, 15 (6), P06005, 87 doi:10.1088/1748-0221/15/06/P06005.
@article{article, author = {Sirunyan, Albert M and Antunovi\'{c}, \v{Z}eljko and Brigljevi\'{c}, Vuko and Feren\v{c}ek, Dinko and Giljanovi\'{c}, Duje and Godinovi\'{c}, Nikola and Kadija, Kre\v{s}o and Kova\v{c}, Marko and Lelas, Damir and Majumder, Devdatta and Mesi\'{c}, Benjamin and Puljak, Ivica and Rogulji\'{c}, Matej and Starodumov, Andrey and \DJuri\'{c}, Senka and \v{S}u\v{s}a, Tatjana and \v{S}\'{c}ulac, Toni and Trembath-reichert, Stephen}, year = {2020}, pages = {87}, DOI = {10.1088/1748-0221/15/06/P06005}, chapter = {P06005}, 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}, journal = {Journal of Instrumentation}, doi = {10.1088/1748-0221/15/06/P06005}, volume = {15}, number = {6}, issn = {1748-0221}, title = {Identification of heavy, energetic, hadronically decaying particles using machine-learning techniques}, keyword = {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}, chapternumber = {P06005} }
@article{article, author = {Sirunyan, Albert M and Antunovi\'{c}, \v{Z}eljko and Brigljevi\'{c}, Vuko and Feren\v{c}ek, Dinko and Giljanovi\'{c}, Duje and Godinovi\'{c}, Nikola and Kadija, Kre\v{s}o and Kova\v{c}, Marko and Lelas, Damir and Majumder, Devdatta and Mesi\'{c}, Benjamin and Puljak, Ivica and Rogulji\'{c}, Matej and Starodumov, Andrey and \DJuri\'{c}, Senka and \v{S}u\v{s}a, Tatjana and \v{S}\'{c}ulac, Toni and Trembath-reichert, Stephen}, year = {2020}, pages = {87}, DOI = {10.1088/1748-0221/15/06/P06005}, chapter = {P06005}, 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}, journal = {Journal of Instrumentation}, doi = {10.1088/1748-0221/15/06/P06005}, volume = {15}, number = {6}, issn = {1748-0221}, title = {Identification of heavy, energetic, hadronically decaying particles using machine-learning techniques}, keyword = {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}, chapternumber = {P06005} }

Časopis indeksira:


  • Current Contents Connect (CCC)
  • Web of Science Core Collection (WoSCC)
    • Science Citation Index Expanded (SCI-EXP)
    • SCI-EXP, SSCI i/ili A&HCI
  • Scopus

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