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FPGA-based Firmware Implementation of MET Machine Learning Algorithm for the CMS Phase-2 Level-1 Correlator Trigger

This report summarizes my work as a CERN 2021 summer student, specifically my contributions to a machine learning MET algorithm which potentially will be implemented in the CMS’s level-1 correlator trigger as part of the phase-2 upgrades. I discuss several background topics and the general status of...

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Autor principal: Hiller, Han Slade
Lenguaje:eng
Publicado: 2021
Materias:
Acceso en línea:http://cds.cern.ch/record/2781017
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author Hiller, Han Slade
author_facet Hiller, Han Slade
author_sort Hiller, Han Slade
collection CERN
description This report summarizes my work as a CERN 2021 summer student, specifically my contributions to a machine learning MET algorithm which potentially will be implemented in the CMS’s level-1 correlator trigger as part of the phase-2 upgrades. I discuss several background topics and the general status of the project after compressing the model and developing a data generator. I show how we quantify the model’s predictive performance and share data from the most recently trained model. From this, I state several conclusions and ultimately determine that with more training data, L1METML will further outperform PUPPI, the MET algorithm currently in use at CMS.
id cern-2781017
institution Organización Europea para la Investigación Nuclear
language eng
publishDate 2021
record_format invenio
spelling cern-27810172021-09-14T21:14:24Zhttp://cds.cern.ch/record/2781017engHiller, Han SladeFPGA-based Firmware Implementation of MET Machine Learning Algorithm for the CMS Phase-2 Level-1 Correlator TriggerPhysics in GeneralThis report summarizes my work as a CERN 2021 summer student, specifically my contributions to a machine learning MET algorithm which potentially will be implemented in the CMS’s level-1 correlator trigger as part of the phase-2 upgrades. I discuss several background topics and the general status of the project after compressing the model and developing a data generator. I show how we quantify the model’s predictive performance and share data from the most recently trained model. From this, I state several conclusions and ultimately determine that with more training data, L1METML will further outperform PUPPI, the MET algorithm currently in use at CMS. CERN-STUDENTS-Note-2021-155oai:cds.cern.ch:27810172021-09-14
spellingShingle Physics in General
Hiller, Han Slade
FPGA-based Firmware Implementation of MET Machine Learning Algorithm for the CMS Phase-2 Level-1 Correlator Trigger
title FPGA-based Firmware Implementation of MET Machine Learning Algorithm for the CMS Phase-2 Level-1 Correlator Trigger
title_full FPGA-based Firmware Implementation of MET Machine Learning Algorithm for the CMS Phase-2 Level-1 Correlator Trigger
title_fullStr FPGA-based Firmware Implementation of MET Machine Learning Algorithm for the CMS Phase-2 Level-1 Correlator Trigger
title_full_unstemmed FPGA-based Firmware Implementation of MET Machine Learning Algorithm for the CMS Phase-2 Level-1 Correlator Trigger
title_short FPGA-based Firmware Implementation of MET Machine Learning Algorithm for the CMS Phase-2 Level-1 Correlator Trigger
title_sort fpga-based firmware implementation of met machine learning algorithm for the cms phase-2 level-1 correlator trigger
topic Physics in General
url http://cds.cern.ch/record/2781017
work_keys_str_mv AT hillerhanslade fpgabasedfirmwareimplementationofmetmachinelearningalgorithmforthecmsphase2level1correlatortrigger