Szczegóły publikacji
Opis bibliograficzny
A new calibration method for charm jet identification validated with proton-proton collision events at ${\sqrt{s}}$=13 TeV / A. Tumasyan, [et al.], V. AVATI, L. GRZANKA, M. MALAWSKI, [et al.] // Journal of Instrumentation [Dokument elektroniczny]. — Czasopismo elektroniczne ; ISSN 1748-0221. — 2022 — vol. 17 iss. 3 art. no. P03014, s. 1–63. — Wymagania systemowe: Adobe Reader. — Bibliogr. s. 41–43, Abstr. — Publikacja dostępna online od: 2022-03-17
Autorzy (2363)
- AGHAvati Valentina
- AGHGrzanka Leszek
- AGHMalawski Maciej
- Tumasyan Armen
Słowa kluczowe
Dane bibliometryczne
| ID BaDAP | 139926 |
|---|---|
| Data dodania do BaDAP | 2022-04-21 |
| Tekst źródłowy | URL |
| DOI | 10.1088/1748-0221/17/03/P03014 |
| Rok publikacji | 2022 |
| Typ publikacji | artykuł w czasopiśmie |
| Otwarty dostęp |  |
| Creative Commons | |
| Czasopismo/seria | Journal of Instrumentation |
Abstract
Many measurements at the LHC require efficient identification of heavy-flavour jets, i.e. jets originating from bottom (b) or charm (c) quarks. An overview of the algorithms used to identify c jets is described and a novel method to calibrate them is presented. This new method adjusts the entire distributions of the outputs obtained when the algorithms are applied to jets of different flavours. It is based on an iterative approach exploiting three distinct control regions that are enriched with either b jets, c jets, or light-flavour and gluon jets. Results are presented in the form of correction factors evaluated using proton-proton collision data with an integrated luminosity of 41.5 fb(-1) at root s = 13 TeV, collected by the CMS experiment in 2017. The closure of the method is tested by applying the measured correction factors on simulated data sets and checking the agreement between the adjusted simulation and collision data. Furthermore, a validation is performed by testing the method on pseudodata, which emulate various mismodelling conditions. The calibrated results enable the use of the full distributions of heavy-flavour identification algorithm outputs, e.g. as inputs to machine-learning models. Thus, they are expected to increase the sensitivity of future physics analyses.
