Inclusive Flavour Tagging Algorithm

17th International Workshop on Advanced Computing and Analysis Techniques in Physics Research

物理学;计算机科学

Likhomanenko, Tatiana^1,2,3 ; Derkach, Denis^1,2 ; Rogozhnikov, Alex^1,2

National Research University, Higher School of Economics (HSE), Russia^1

Yandex School of Data Analysis (YSDA), Russia^2

NRC Kurchatov Institute, Russia^3

关键词: B mesons;    CP violations;    Harsh environment;    Identification capacities;    Large Hadron Collider;    Probabilistic modeling;    Time dependent;   

Others  :  https://iopscience.iop.org/article/10.1088/1742-6596/762/1/012045/pdf
DOI  :  10.1088/1742-6596/762/1/012045

学科分类：计算机科学（综合）

来源: IOP

【 摘 要 】

Identifying the flavour of neutral B mesons production is one of the most important components needed in the study of time-dependent CP violation. The harsh environment of the Large Hadron Collider makes it particularly hard to succeed in this task. We present an inclusive flavour-tagging algorithm as an upgrade of the algorithms currently used by the LHCb experiment. Specifically, a probabilistic model which efficiently combines information from reconstructed vertices and tracks using machine learning is proposed. The algorithm does not use information about underlying physics process. It reduces the dependence on the performance of lower level identification capacities and thus increases the overall performance. The proposed inclusive flavour-tagging algorithm is applicable to tag the flavour of B mesons in any proton-proton experiment.