Ultra-fast deep learning algorithms on FPGA for the phase-II level-0 trigger of the ATLAS experiment

The LHC accelerator will face, during the following years, a complete upgrade with the main purpose of rising up the instantaneous luminosity by a factor of almost five. Though this will permit to collect an incredible amount of data, the complexity of each event will greatly intensifies going from...

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Detalles Bibliográficos
Autor principal: Sabetta, L
Lenguaje:eng
Publicado: 2020
Materias:
Detectors and Experimental Techniques
Acceso en línea:https://dx.doi.org/10.1393/ncc/i2020-20061-0
http://cds.cern.ch/record/2765546
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author Sabetta, L
author_facet Sabetta, L
author_sort Sabetta, L
collection CERN
description The LHC accelerator will face, during the following years, a complete upgrade with the main purpose of rising up the instantaneous luminosity by a factor of almost five. Though this will permit to collect an incredible amount of data, the complexity of each event will greatly intensifies going from an average number of interactions per bunch crossing of 40 to an average of 200. To cope with this problem and be able to handle this large amount of information, both the detectors and the trigger algorithms of the ATLAS experiment will be updated. A machine learning approach for the level-0 trigger algorithm is presented.
id oai-inspirehep.net-1829163
institution Organización Europea para la Investigación Nuclear
language eng
publishDate 2020
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spelling oai-inspirehep.net-18291632021-05-10T17:29:09Zdoi:10.1393/ncc/i2020-20061-0http://cds.cern.ch/record/2765546engSabetta, LUltra-fast deep learning algorithms on FPGA for the phase-II level-0 trigger of the ATLAS experimentDetectors and Experimental TechniquesThe LHC accelerator will face, during the following years, a complete upgrade with the main purpose of rising up the instantaneous luminosity by a factor of almost five. Though this will permit to collect an incredible amount of data, the complexity of each event will greatly intensifies going from an average number of interactions per bunch crossing of 40 to an average of 200. To cope with this problem and be able to handle this large amount of information, both the detectors and the trigger algorithms of the ATLAS experiment will be updated. A machine learning approach for the level-0 trigger algorithm is presented.oai:inspirehep.net:18291632020
spellingShingle Detectors and Experimental Techniques
Sabetta, L
Ultra-fast deep learning algorithms on FPGA for the phase-II level-0 trigger of the ATLAS experiment
title Ultra-fast deep learning algorithms on FPGA for the phase-II level-0 trigger of the ATLAS experiment
title_full Ultra-fast deep learning algorithms on FPGA for the phase-II level-0 trigger of the ATLAS experiment
title_fullStr Ultra-fast deep learning algorithms on FPGA for the phase-II level-0 trigger of the ATLAS experiment
title_full_unstemmed Ultra-fast deep learning algorithms on FPGA for the phase-II level-0 trigger of the ATLAS experiment
title_short Ultra-fast deep learning algorithms on FPGA for the phase-II level-0 trigger of the ATLAS experiment
title_sort ultra-fast deep learning algorithms on fpga for the phase-ii level-0 trigger of the atlas experiment
topic Detectors and Experimental Techniques
url https://dx.doi.org/10.1393/ncc/i2020-20061-0
http://cds.cern.ch/record/2765546
work_keys_str_mv AT sabettal ultrafastdeeplearningalgorithmsonfpgaforthephaseiilevel0triggeroftheatlasexperiment

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