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Talk
Title	Inclusive tagging of B-flavour at LHCb [Vidyo]
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Author(s)	Rogozhnikov, Aleksei (speaker) (Yandex School of Data Analysis (RU))
Corporate author(s)	CERN. Geneva
Imprint	2017-03-21. - Streaming video.
Series	(Machine Learning) (IML Machine Learning Workshop)
Lecture note	on 2017-03-21T11:55:00
Subject category	Machine Learning
Abstract	One of the most important procedure needed for the study of CP violation in Beauty sector is the tagging of the flavour of neutral B-mesons at production. The harsh environment of the Large Hadron Collider makes it particularly hard to succeed in this task. We present a proposal to upgrade current flavour tagging strategy in LHCb experiment. This strategy consists of inclusive tagging ensemble methods (i.e: the use inclusive information about the event without a firm selection rule), which are combined using a probabilistic model for each event. The probabilistic model uses all reconstructed tracks and secondary vertices to obtain well-determined probability of B flavour at production. Such approach reduces the dependence on the performance of lower level identification capacities and thus has the potential to increase the overall performance.
Copyright/License	© 2017-2024 CERN
Submitted by	sergei.gleyzer@cern.ch