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

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Identification of heavy, energetic, hadronically decaying particles using machine-learning techniques. / The CMS collaboration.

In: Journal of Instrumentation, Vol. 15, No. 6, P06005, 01.06.2020.

Research output: Contribution to journal › Article › peer-review

Harvard

The CMS collaboration 2020, 'Identification of heavy, energetic, hadronically decaying particles using machine-learning techniques', Journal of Instrumentation, vol. 15, no. 6, P06005. https://doi.org/10.1088/1748-0221/15/06/P06005

APA

The CMS collaboration (2020). Identification of heavy, energetic, hadronically decaying particles using machine-learning techniques. Journal of Instrumentation, 15(6), [P06005]. https://doi.org/10.1088/1748-0221/15/06/P06005

Vancouver

The CMS collaboration. Identification of heavy, energetic, hadronically decaying particles using machine-learning techniques. Journal of Instrumentation. 2020 Jun 1;15(6):P06005. doi: 10.1088/1748-0221/15/06/P06005

Author

The CMS collaboration. / Identification of heavy, energetic, hadronically decaying particles using machine-learning techniques. In: Journal of Instrumentation. 2020 ; Vol. 15, No. 6.

BibTeX

@article{6d65cc09c9514a5f980b7d699cbe5906,

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

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 s = 13TeV, 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.",

keywords = "Large detector-systems performance, Pattern recognition, cluster finding, calibration and fitting methods, ANNIHILATION, ALGORITHMS",

author = "{The CMS collaboration} and Sirunyan, {A. M.} and A. Tumasyan and W. Adam and F. Ambrogi and T. Bergauer and M. Dragicevic and J. Er{\"o} and {Del Valle}, {A. Escalante} and M. Flechl and R. Fr{\"u}hwirth and M. Jeitler and N. Krammer and I. Kr{\"a}tschmer and D. Liko and T. Madlener and I. Mikulec and N. Rad and J. Schieck and R. Sch{\"o}fbeck and M. Spanring and W. Waltenberger and Wulz, {C. E.} and M. Zarucki and V. Drugakov and V. Mossolov and Gonzalez, {J. Suarez} and Darwish, {M. R.} and {De Wolf}, {E. A.} and {Di Croce}, {D. D.} and X. Janssen and A. Lelek and M. Pieters and Sfar, {H. Rejeb} and {Van Haevermaet}, H. and {Van Mechelen}, P. and {Van Putte}, S. and {Van Remortel}, N. and F. Blekman and Bols, {E. S.} and Chhibra, {S. S.} and J. D'Hondt and {De Clercq}, {J. D.} and D. Lontkovskyi and S. Lowette and I. Marchesini and A. Barnyakov and V. Blinov and T. Dimova and L. Kardapoltsev and Y. Skovpen",

year = "2020",

month = jun,

day = "1",

doi = "10.1088/1748-0221/15/06/P06005",

language = "English",

volume = "15",

journal = "Journal of Instrumentation",

issn = "1748-0221",

publisher = "IOP Publishing Ltd.",

number = "6",

}

RIS

TY - JOUR

T1 - Identification of heavy, energetic, hadronically decaying particles using machine-learning techniques

AU - The CMS collaboration

AU - Sirunyan, A. M.

AU - Tumasyan, A.

AU - Adam, W.

AU - Ambrogi, F.

AU - Bergauer, T.

AU - Dragicevic, M.

AU - Erö, J.

AU - Del Valle, A. Escalante

AU - Flechl, M.

AU - Frühwirth, R.

AU - Jeitler, M.

AU - Krammer, N.

AU - Krätschmer, I.

AU - Liko, D.

AU - Madlener, T.

AU - Mikulec, I.

AU - Rad, N.

AU - Schieck, J.

AU - Schöfbeck, R.

AU - Spanring, M.

AU - Waltenberger, W.

AU - Wulz, C. E.

AU - Zarucki, M.

AU - Drugakov, V.

AU - Mossolov, V.

AU - Gonzalez, J. Suarez

AU - Darwish, M. R.

AU - De Wolf, E. A.

AU - Di Croce, D. D.

AU - Janssen, X.

AU - Lelek, A.

AU - Pieters, M.

AU - Sfar, H. Rejeb

AU - Van Haevermaet, H.

AU - Van Mechelen, P.

AU - Van Putte, S.

AU - Van Remortel, N.

AU - Blekman, F.

AU - Bols, E. S.

AU - Chhibra, S. S.

AU - D'Hondt, J.

AU - De Clercq, J. D.

AU - Lontkovskyi, D.

AU - Lowette, S.

AU - Marchesini, I.

AU - Barnyakov, A.

AU - Blinov, V.

AU - Dimova, T.

AU - Kardapoltsev, L.

AU - Skovpen, Y.

PY - 2020/6/1

Y1 - 2020/6/1

N2 - 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 s = 13TeV, 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.

AB - 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 s = 13TeV, 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.

KW - Large detector-systems performance

KW - Pattern recognition, cluster finding, calibration and fitting methods

KW - ANNIHILATION

KW - ALGORITHMS

UR - http://www.scopus.com/inward/record.url?scp=85088524436&partnerID=8YFLogxK

U2 - 10.1088/1748-0221/15/06/P06005

DO - 10.1088/1748-0221/15/06/P06005

M3 - Article

AN - SCOPUS:85088524436

VL - 15

JO - Journal of Instrumentation

JF - Journal of Instrumentation

SN - 1748-0221

IS - 6

M1 - P06005

ER -

ID: 24871219