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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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Lenguaje: | eng |
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2021
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Acceso en línea: | http://cds.cern.ch/record/2781017 |
_version_ | 1780971911858094080 |
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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 |