Anomaly detection with data quality early warning systems in ATLAS
| dc.contributor.advisor | Yacoob, Sahal | |
| dc.contributor.advisor | Keaveney James | |
| dc.contributor.author | Msutwana, Senzo | |
| dc.date.accessioned | 2024-05-27T08:30:26Z | |
| dc.date.available | 2024-05-27T08:30:26Z | |
| dc.date.issued | 2023 | |
| dc.date.updated | 2024-05-22T08:41:20Z | |
| dc.description.abstract | 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 | |
| dc.identifier.apacitation | Msutwana, S. (2023). <i>Anomaly detection with data quality early warning systems in ATLAS</i>. (). ,Faculty of Science ,Department of Physics. Retrieved from http://hdl.handle.net/11427/39703 | en_ZA |
| dc.identifier.chicagocitation | Msutwana, Senzo. <i>"Anomaly detection with data quality early warning systems in ATLAS."</i> ., ,Faculty of Science ,Department of Physics, 2023. http://hdl.handle.net/11427/39703 | en_ZA |
| dc.identifier.citation | Msutwana, S. 2023. Anomaly detection with data quality early warning systems in ATLAS. . ,Faculty of Science ,Department of Physics. http://hdl.handle.net/11427/39703 | en_ZA |
| dc.identifier.ris | TY - Thesis / Dissertation AU - Msutwana, Senzo AB - 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 DA - 2023 DB - OpenUCT DP - University of Cape Town KW - Physics LK - https://open.uct.ac.za PY - 2023 T1 - Anomaly detection with data quality early warning systems in ATLAS TI - Anomaly detection with data quality early warning systems in ATLAS UR - http://hdl.handle.net/11427/39703 ER - | en_ZA |
| dc.identifier.uri | http://hdl.handle.net/11427/39703 | |
| dc.identifier.vancouvercitation | Msutwana S. Anomaly detection with data quality early warning systems in ATLAS. []. ,Faculty of Science ,Department of Physics, 2023 [cited yyyy month dd]. Available from: http://hdl.handle.net/11427/39703 | en_ZA |
| dc.language.rfc3066 | eng | |
| dc.publisher.department | Department of Physics | |
| dc.publisher.faculty | Faculty of Science | |
| dc.subject | Physics | |
| dc.title | Anomaly detection with data quality early warning systems in ATLAS | |
| dc.type | Thesis / Dissertation | |
| dc.type.qualificationlevel | Masters | |
| dc.type.qualificationlevel | MSc |