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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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Lenguaje: | eng |
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2021
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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 |