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Exploration of Jet Substructure with Energy Flow Polynomials

The Energy Flow Polynomials (EFPs) are a set of jet substructure observables which forms an overcomplete basis for all infrared- and collinear-safe jet observables. In this study, an in-depth look into the EFPs is conducted. Comparisons of EFPs distributions are made between experimental and simulat...

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Autor principal: Yu, Yu-Heng
Lenguaje:eng
Publicado: 2019
Materias:
Acceso en línea:http://cds.cern.ch/record/2689625
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author Yu, Yu-Heng
author_facet Yu, Yu-Heng
author_sort Yu, Yu-Heng
collection CERN
description The Energy Flow Polynomials (EFPs) are a set of jet substructure observables which forms an overcomplete basis for all infrared- and collinear-safe jet observables. In this study, an in-depth look into the EFPs is conducted. Comparisons of EFPs distributions are made between experimental and simulated dataset; strong correlations between different EFPs are found; Principal Component Analysis is done to 53 different EFPs, where they can be reduced to 6 principal components without much loss of information; Linear Regression is done to the same set of EFPs, and also to the 6 reduced components, to find the coefficients for them to linearly combine into some traditional jet shapes. Similar performances are found between the 53 EFPs and the 6 principal components.
id cern-2689625
institution Organización Europea para la Investigación Nuclear
language eng
publishDate 2019
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spelling cern-26896252019-09-30T06:29:59Zhttp://cds.cern.ch/record/2689625engYu, Yu-HengExploration of Jet Substructure with Energy Flow PolynomialsPhysics in GeneralThe Energy Flow Polynomials (EFPs) are a set of jet substructure observables which forms an overcomplete basis for all infrared- and collinear-safe jet observables. In this study, an in-depth look into the EFPs is conducted. Comparisons of EFPs distributions are made between experimental and simulated dataset; strong correlations between different EFPs are found; Principal Component Analysis is done to 53 different EFPs, where they can be reduced to 6 principal components without much loss of information; Linear Regression is done to the same set of EFPs, and also to the 6 reduced components, to find the coefficients for them to linearly combine into some traditional jet shapes. Similar performances are found between the 53 EFPs and the 6 principal components.CERN-STUDENTS-Note-2019-206oai:cds.cern.ch:26896252019-09-16
spellingShingle Physics in General
Yu, Yu-Heng
Exploration of Jet Substructure with Energy Flow Polynomials
title Exploration of Jet Substructure with Energy Flow Polynomials
title_full Exploration of Jet Substructure with Energy Flow Polynomials
title_fullStr Exploration of Jet Substructure with Energy Flow Polynomials
title_full_unstemmed Exploration of Jet Substructure with Energy Flow Polynomials
title_short Exploration of Jet Substructure with Energy Flow Polynomials
title_sort exploration of jet substructure with energy flow polynomials
topic Physics in General
url http://cds.cern.ch/record/2689625
work_keys_str_mv AT yuyuheng explorationofjetsubstructurewithenergyflowpolynomials