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The Higgs Machine Learning Challenge
The Higgs Machine Learning Challenge was an open data analysis competition that took place between May and September 2014. Samples of simulated data from the ATLAS Experiment at the LHC corresponding to signal events with Higgs bosons decaying to $\tau^+\tau^-$ together with background events were m...
Autores principales: | , |
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Lenguaje: | eng |
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2015
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Materias: | |
Acceso en línea: | https://dx.doi.org/10.1088/1742-6596/664/7/072015 http://cds.cern.ch/record/2016636 |
_version_ | 1780946703108538368 |
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author | Cowan, Glen Rousseau, David |
author_facet | Cowan, Glen Rousseau, David |
author_sort | Cowan, Glen |
collection | CERN |
description | The Higgs Machine Learning Challenge was an open data analysis competition that took place between May and September 2014. Samples of simulated data from the ATLAS Experiment at the LHC corresponding to signal events with Higgs bosons decaying to $\tau^+\tau^-$ together with background events were made available to the public through the website of the data science organization Kaggle (\verb=kaggle.com=). Participants attempted to identify the search region in a space of 30 kinematic variables that would maximize the expected discovery significance of the signal process. One of the primary goals of the Challenge was promote communication of new ideas between the Machine Learning (ML) and HEP communities. In this regard it was a resounding success, with almost 2,000 participants from HEP, ML and other areas. The process of understanding and integrating the new ideas, particularly from ML into HEP, is currently underway. |
id | cern-2016636 |
institution | Organización Europea para la Investigación Nuclear |
language | eng |
publishDate | 2015 |
record_format | invenio |
spelling | cern-20166362022-08-10T12:55:12Zdoi:10.1088/1742-6596/664/7/072015http://cds.cern.ch/record/2016636engCowan, GlenRousseau, DavidThe Higgs Machine Learning ChallengeParticle Physics - ExperimentThe Higgs Machine Learning Challenge was an open data analysis competition that took place between May and September 2014. Samples of simulated data from the ATLAS Experiment at the LHC corresponding to signal events with Higgs bosons decaying to $\tau^+\tau^-$ together with background events were made available to the public through the website of the data science organization Kaggle (\verb=kaggle.com=). Participants attempted to identify the search region in a space of 30 kinematic variables that would maximize the expected discovery significance of the signal process. One of the primary goals of the Challenge was promote communication of new ideas between the Machine Learning (ML) and HEP communities. In this regard it was a resounding success, with almost 2,000 participants from HEP, ML and other areas. The process of understanding and integrating the new ideas, particularly from ML into HEP, is currently underway.ATL-SOFT-PROC-2015-044oai:cds.cern.ch:20166362015-05-17 |
spellingShingle | Particle Physics - Experiment Cowan, Glen Rousseau, David The Higgs Machine Learning Challenge |
title | The Higgs Machine Learning Challenge |
title_full | The Higgs Machine Learning Challenge |
title_fullStr | The Higgs Machine Learning Challenge |
title_full_unstemmed | The Higgs Machine Learning Challenge |
title_short | The Higgs Machine Learning Challenge |
title_sort | higgs machine learning challenge |
topic | Particle Physics - Experiment |
url | https://dx.doi.org/10.1088/1742-6596/664/7/072015 http://cds.cern.ch/record/2016636 |
work_keys_str_mv | AT cowanglen thehiggsmachinelearningchallenge AT rousseaudavid thehiggsmachinelearningchallenge AT cowanglen higgsmachinelearningchallenge AT rousseaudavid higgsmachinelearningchallenge |