Szczegóły publikacji
Opis bibliograficzny
Identification of heavy, energetic, hadronically decaying particles using machine-learning techniques / A. M. Sirunyan, [et al.], V. AVATI, L. GRZANKA, M. MALAWSKI, [et al.] // Journal of Instrumentation [Dokument elektroniczny]. — Czasopismo elektroniczne ; ISSN 1748-0221. — 2020 — vol. 15 iss. 6 art. no. P06005, s. [2], 1–85. — Wymagania systemowe: Adobe Reader. — Bibliogr. s. 60–66, Abstr. — Publikacja dostępna online od: 2020-06-03
Autorzy (2304)
- AGHAvati Valentina
- AGHGrzanka Leszek
- AGHMalawski Maciej
- Sirunyan A. M.
Słowa kluczowe
Dane bibliometryczne
| ID BaDAP | 129620 |
|---|---|
| Data dodania do BaDAP | 2020-09-22 |
| Tekst źródłowy | URL |
| DOI | 10.1088/1748-0221/15/06/P06005 |
| Rok publikacji | 2020 |
| Typ publikacji | artykuł w czasopiśmie |
| Otwarty dostęp |  |
| Creative Commons | |
| Czasopismo/seria | Journal of Instrumentation |
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 root 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.
