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Classification and Regression Studies for Flavour Tagging

The identification of jets containing b-hadrons, commonly referred to as b-tagging, plays a crucial role in high-energy physics experiments for both precise measurements of the Standard Model and for exploring scenarios of new physics. b-tagging algorithms are based on a variety of discriminant infor...

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Autor principal: Petrini, Leonardo
Lenguaje:eng
Publicado: 2018
Materias:
Acceso en línea:http://cds.cern.ch/record/2638843
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author Petrini, Leonardo
author_facet Petrini, Leonardo
author_sort Petrini, Leonardo
collection CERN
description The identification of jets containing b-hadrons, commonly referred to as b-tagging, plays a crucial role in high-energy physics experiments for both precise measurements of the Standard Model and for exploring scenarios of new physics. b-tagging algorithms are based on a variety of discriminant information related to the long b-hadron lifetime and are constructed by exploiting boosted-decision tree or deep neural-network techniques. he first part of this project aims at comparing the performance of different high-level tagging algorithms. The discrimination power of the b-tagging algorithms, however, is strongly dependent on the jet transverse momentum. The jet transverse momentum is being exploited by the b-tagging algorithms as a proxy for the b-hadron pT , the latter being a quantity not reconstructed by the software that inspects the events and builds the physics objects originating from the collisions. Nevertheless, the b-hadron pT could also be inferred from the outputs of the low level taggers employing a regression model. The second part of the project consists in building such models. The study is carried out in a Python environment, decision trees and neural network are implemented using Scikit-learn [8], XGBoost [4], LightGBM [6] and PyTorch [7].
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institution Organización Europea para la Investigación Nuclear
language eng
publishDate 2018
record_format invenio
spelling cern-26388432019-09-30T06:29:59Zhttp://cds.cern.ch/record/2638843engPetrini, LeonardoClassification and Regression Studies for Flavour TaggingPhysics in GeneralThe identification of jets containing b-hadrons, commonly referred to as b-tagging, plays a crucial role in high-energy physics experiments for both precise measurements of the Standard Model and for exploring scenarios of new physics. b-tagging algorithms are based on a variety of discriminant information related to the long b-hadron lifetime and are constructed by exploiting boosted-decision tree or deep neural-network techniques. he first part of this project aims at comparing the performance of different high-level tagging algorithms. The discrimination power of the b-tagging algorithms, however, is strongly dependent on the jet transverse momentum. The jet transverse momentum is being exploited by the b-tagging algorithms as a proxy for the b-hadron pT , the latter being a quantity not reconstructed by the software that inspects the events and builds the physics objects originating from the collisions. Nevertheless, the b-hadron pT could also be inferred from the outputs of the low level taggers employing a regression model. The second part of the project consists in building such models. The study is carried out in a Python environment, decision trees and neural network are implemented using Scikit-learn [8], XGBoost [4], LightGBM [6] and PyTorch [7].CERN-STUDENTS-Note-2018-142oai:cds.cern.ch:26388432018-09-18
spellingShingle Physics in General
Petrini, Leonardo
Classification and Regression Studies for Flavour Tagging
title Classification and Regression Studies for Flavour Tagging
title_full Classification and Regression Studies for Flavour Tagging
title_fullStr Classification and Regression Studies for Flavour Tagging
title_full_unstemmed Classification and Regression Studies for Flavour Tagging
title_short Classification and Regression Studies for Flavour Tagging
title_sort classification and regression studies for flavour tagging
topic Physics in General
url http://cds.cern.ch/record/2638843
work_keys_str_mv AT petrinileonardo classificationandregressionstudiesforflavourtagging