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Data Compression Studies in the High Granularity Calorimeter

This document outlines the details of my project and contributions to the CMS experiment as a CERN Summer Student participating in the University of Michigan CERN Research Experience for Undergraduates (REU). I describe relevant updates to the CMS detector as part of the High-Luminosity LHC upgrade;...

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Autor principal: Farino, Mark Louis
Lenguaje:eng
Publicado: 2021
Materias:
Acceso en línea:http://cds.cern.ch/record/2779299
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author Farino, Mark Louis
author_facet Farino, Mark Louis
author_sort Farino, Mark Louis
collection CERN
description This document outlines the details of my project and contributions to the CMS experiment as a CERN Summer Student participating in the University of Michigan CERN Research Experience for Undergraduates (REU). I describe relevant updates to the CMS detector as part of the High-Luminosity LHC upgrade; particularly to the frontend electronics of the High Granularity Calorimeter (HGCAL) which will replace the existing endcap calorimeters. My project seeks to optimize the data compression algorithms in the front-end electronics via Machine Learning. I evaluate the physics performance of these algorithms by estimating the detector resolution when reconstructing electron or photon showers from the compressed data.
id cern-2779299
institution Organización Europea para la Investigación Nuclear
language eng
publishDate 2021
record_format invenio
spelling cern-27792992021-08-26T20:59:54Zhttp://cds.cern.ch/record/2779299engFarino, Mark LouisData Compression Studies in the High Granularity CalorimeterParticle Physics - ExperimentThis document outlines the details of my project and contributions to the CMS experiment as a CERN Summer Student participating in the University of Michigan CERN Research Experience for Undergraduates (REU). I describe relevant updates to the CMS detector as part of the High-Luminosity LHC upgrade; particularly to the frontend electronics of the High Granularity Calorimeter (HGCAL) which will replace the existing endcap calorimeters. My project seeks to optimize the data compression algorithms in the front-end electronics via Machine Learning. I evaluate the physics performance of these algorithms by estimating the detector resolution when reconstructing electron or photon showers from the compressed data.CERN-STUDENTS-Note-2021-056oai:cds.cern.ch:27792992021-08-26
spellingShingle Particle Physics - Experiment
Farino, Mark Louis
Data Compression Studies in the High Granularity Calorimeter
title Data Compression Studies in the High Granularity Calorimeter
title_full Data Compression Studies in the High Granularity Calorimeter
title_fullStr Data Compression Studies in the High Granularity Calorimeter
title_full_unstemmed Data Compression Studies in the High Granularity Calorimeter
title_short Data Compression Studies in the High Granularity Calorimeter
title_sort data compression studies in the high granularity calorimeter
topic Particle Physics - Experiment
url http://cds.cern.ch/record/2779299
work_keys_str_mv AT farinomarklouis datacompressionstudiesinthehighgranularitycalorimeter