Identification of heavy, energetic, hadronically decaying particles using machine-learning techniques; Journal of Instrumentation; Vol. 15, iss. 6
| Parent link: | Journal of Instrumentation Vol. 15, iss. 6.— 2020.— P06005, 88 p. |
|---|---|
| Altres autors: | , , , , , , , |
| Sumari: | Title screen 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 vs = 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 |
| Idioma: | anglès |
| Publicat: |
2020
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| Matèries: | |
| Accés en línia: | https://doi.org/10.1088/1748-0221/15/06/P06005 |
| Format: | Electrònic Capítol de llibre |
| KOHA link: | https://koha.lib.tpu.ru/cgi-bin/koha/opac-detail.pl?biblionumber=662526 |
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| 200 | 1 | |a Identification of heavy, energetic, hadronically decaying particles using machine-learning techniques |f A. M. Sirunyan, A. R. Tumasyan, W. Adam [et al.] | |
| 203 | |a Текст |c электронный | ||
| 300 | |a Title screen | ||
| 320 | |a References: 107 tit | ||
| 330 | |a 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 vs = 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 | ||
| 461 | 1 | |0 (RuTPU)RU\TPU\network\25113 |t Journal of Instrumentation | |
| 463 | 1 | |t Vol. 15, iss. 6 |v P06005, 88 p. |d 2020 | |
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