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LHC physics dataset for unsupervised New Physics detection at 40 MHz
In the particle detectors at the Large Hadron Collider, hundreds of millions of proton-proton collisions are produced every second. If one could store the whole data stream produced in these collisions, tens of terabytes of data would be written to disk every second. The general-purpose experiments...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9070018/ https://www.ncbi.nlm.nih.gov/pubmed/35351897 http://dx.doi.org/10.1038/s41597-022-01187-8 |
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author | Govorkova, Ekaterina Puljak, Ema Aarrestad, Thea Pierini, Maurizio Woźniak, Kinga Anna Ngadiuba, Jennifer |
author_facet | Govorkova, Ekaterina Puljak, Ema Aarrestad, Thea Pierini, Maurizio Woźniak, Kinga Anna Ngadiuba, Jennifer |
author_sort | Govorkova, Ekaterina |
collection | PubMed |
description | In the particle detectors at the Large Hadron Collider, hundreds of millions of proton-proton collisions are produced every second. If one could store the whole data stream produced in these collisions, tens of terabytes of data would be written to disk every second. The general-purpose experiments ATLAS and CMS reduce this overwhelming data volume to a sustainable level, by deciding in real-time whether each collision event should be kept for further analysis or be discarded. We introduce a dataset of proton collision events that emulates a typical data stream collected by such a real-time processing system, pre-filtered by requiring the presence of at least one electron or muon. This dataset could be used to develop novel event selection strategies and assess their sensitivity to new phenomena. In particular, we intend to stimulate a community-based effort towards the design of novel algorithms for performing unsupervised new physics detection, customized to fit the bandwidth, latency and computational resource constraints of the real-time event selection system of a typical particle detector. |
format | Online Article Text |
id | pubmed-9070018 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-90700182022-05-05 LHC physics dataset for unsupervised New Physics detection at 40 MHz Govorkova, Ekaterina Puljak, Ema Aarrestad, Thea Pierini, Maurizio Woźniak, Kinga Anna Ngadiuba, Jennifer Sci Data Data Descriptor In the particle detectors at the Large Hadron Collider, hundreds of millions of proton-proton collisions are produced every second. If one could store the whole data stream produced in these collisions, tens of terabytes of data would be written to disk every second. The general-purpose experiments ATLAS and CMS reduce this overwhelming data volume to a sustainable level, by deciding in real-time whether each collision event should be kept for further analysis or be discarded. We introduce a dataset of proton collision events that emulates a typical data stream collected by such a real-time processing system, pre-filtered by requiring the presence of at least one electron or muon. This dataset could be used to develop novel event selection strategies and assess their sensitivity to new phenomena. In particular, we intend to stimulate a community-based effort towards the design of novel algorithms for performing unsupervised new physics detection, customized to fit the bandwidth, latency and computational resource constraints of the real-time event selection system of a typical particle detector. Nature Publishing Group UK 2022-03-29 /pmc/articles/PMC9070018/ /pubmed/35351897 http://dx.doi.org/10.1038/s41597-022-01187-8 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Data Descriptor Govorkova, Ekaterina Puljak, Ema Aarrestad, Thea Pierini, Maurizio Woźniak, Kinga Anna Ngadiuba, Jennifer LHC physics dataset for unsupervised New Physics detection at 40 MHz |
title | LHC physics dataset for unsupervised New Physics detection at 40 MHz |
title_full | LHC physics dataset for unsupervised New Physics detection at 40 MHz |
title_fullStr | LHC physics dataset for unsupervised New Physics detection at 40 MHz |
title_full_unstemmed | LHC physics dataset for unsupervised New Physics detection at 40 MHz |
title_short | LHC physics dataset for unsupervised New Physics detection at 40 MHz |
title_sort | lhc physics dataset for unsupervised new physics detection at 40 mhz |
topic | Data Descriptor |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9070018/ https://www.ncbi.nlm.nih.gov/pubmed/35351897 http://dx.doi.org/10.1038/s41597-022-01187-8 |
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