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Anomaly detection with data quality early warning systems in ATLAS
In this dissertation, the implementation of a Data-Quality Early Warning System (DQEWS) is explored. We use unsupervised Machine Learning (ML) methods to evaluate Data-Quality (DQ) in the ATLAS detector. We do so by observing and quantifying the evolution of Luminosity-Block (LB) data from Inner Det...
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| Format: | Thesis |
| Language: | English |
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Department of Physics
2024
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| _version_ | 1867613217654571008 |
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| access_status_str | Open Access |
| author | Msutwana, Senzo |
| author2 | Yacoob, Sahal |
| author_browse | Msutwana, Senzo Yacoob, Sahal |
| author_facet | Yacoob, Sahal Msutwana, Senzo |
| author_sort | Msutwana, Senzo |
| collection | Thesis |
| description | In this dissertation, the implementation of a Data-Quality Early Warning System (DQEWS) is explored. We use unsupervised Machine Learning (ML) methods to evaluate Data-Quality (DQ) in the ATLAS detector. We do so by observing and quantifying the evolution of Luminosity-Block (LB) data from Inner Detector (ID) tracking information, with a single LB towards the beginning of a run used as the reference. In this way, we obtain a trajectory that describes how the recorded LB data drift over the course of a run. Within the scope of this project thus far, the following will be shown. The version of the DQEWS algorithm defined as of the presentation of the results shown in this dissertation is shown to sufficiently flag good LBs as 'good', and bad LBs as 'bad' under the condition that the flagging criteria are evaluated on LB datasets that lie within a similar range of instantaneous luminosity as the LB datasets used to construct the criteria |
| format | Thesis |
| id | oai:open.uct.ac.za:11427/39703 |
| institution | University of Cape Town (South Africa) |
| language | eng |
| last_indexed | 2026-06-10T12:32:38.580Z |
| license_str | Not specified — see source repository |
| provenance_str_mv | Harvested via OAI-PMH from UCTD — University of Cape Town Open Access Repository |
| publishDate | 2024 |
| publishDateRange | 2024 |
| publishDateSort | 2024 |
| publisher | Department of Physics |
| publisherStr | Department of Physics |
| record_format | dspace |
| source_str | UCTD — University of Cape Town Open Access Repository |
| spelling | oai:open.uct.ac.za:11427/39703 Anomaly detection with data quality early warning systems in ATLAS Msutwana, Senzo Yacoob, Sahal Keaveney James Physics In this dissertation, the implementation of a Data-Quality Early Warning System (DQEWS) is explored. We use unsupervised Machine Learning (ML) methods to evaluate Data-Quality (DQ) in the ATLAS detector. We do so by observing and quantifying the evolution of Luminosity-Block (LB) data from Inner Detector (ID) tracking information, with a single LB towards the beginning of a run used as the reference. In this way, we obtain a trajectory that describes how the recorded LB data drift over the course of a run. Within the scope of this project thus far, the following will be shown. The version of the DQEWS algorithm defined as of the presentation of the results shown in this dissertation is shown to sufficiently flag good LBs as 'good', and bad LBs as 'bad' under the condition that the flagging criteria are evaluated on LB datasets that lie within a similar range of instantaneous luminosity as the LB datasets used to construct the criteria 2024-05-27T08:30:26Z 2024-05-27T08:30:26Z 2023 2024-05-22T08:41:20Z Thesis / Dissertation Masters MSc http://hdl.handle.net/11427/39703 eng application/pdf Department of Physics Faculty of Science |
| spellingShingle | Physics Msutwana, Senzo Anomaly detection with data quality early warning systems in ATLAS |
| thesis_degree_str | Master's |
| title | Anomaly detection with data quality early warning systems in ATLAS |
| title_full | Anomaly detection with data quality early warning systems in ATLAS |
| title_fullStr | Anomaly detection with data quality early warning systems in ATLAS |
| title_full_unstemmed | Anomaly detection with data quality early warning systems in ATLAS |
| title_short | Anomaly detection with data quality early warning systems in ATLAS |
| title_sort | anomaly detection with data quality early warning systems in atlas |
| topic | Physics |
| url | http://hdl.handle.net/11427/39703 |
| work_keys_str_mv | AT msutwanasenzo anomalydetectionwithdataqualityearlywarningsystemsinatlas |