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| Auteurs principaux: | , , , , , , , , , , , , , , , , , , , , , , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| Sujets: | |
| Accès en ligne: | https://arxiv.org/abs/2501.13789 |
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| _version_ | 1866910074276937728 |
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| author | Brinkerhoff, Andrew Sutantawibul, Chosila White, Robert Daumann, Caio Freer, Chad Suarez, Indara May, Samuel Nguyen, Vivan Guiang, Jonathan Marsh, Bennett Acosta, Darin Aubuchon, Alex Barberis, Emanuela Bundock, Aaron Collins, Evan Epps, Preston Erdmann, Johannes Flaecher, Henning Huang, Junshen Nie, Ryan Paramesvaran, Sudarshan Rotter, John Salyer, Kaitlin Sawant, Siddhesh Sheokand, Tanvi Wood, Darien |
| author_facet | Brinkerhoff, Andrew Sutantawibul, Chosila White, Robert Daumann, Caio Freer, Chad Suarez, Indara May, Samuel Nguyen, Vivan Guiang, Jonathan Marsh, Bennett Acosta, Darin Aubuchon, Alex Barberis, Emanuela Bundock, Aaron Collins, Evan Epps, Preston Erdmann, Johannes Flaecher, Henning Huang, Junshen Nie, Ryan Paramesvaran, Sudarshan Rotter, John Salyer, Kaitlin Sawant, Siddhesh Sheokand, Tanvi Wood, Darien |
| contents | Successful operation of large particle detectors like the Compact Muon Solenoid (CMS) at the CERN Large Hadron Collider requires rapid, in-depth assessment of data quality. We introduce the ``AutoDQM'' system for Automated Data Quality Monitoring using advanced statistical techniques and unsupervised machine learning. Anomaly detection algorithms based on the beta-binomial probability function, principal component analysis, and neural network autoencoder image evaluation are tested on the full set of proton-proton collision data collected by CMS in 2022. AutoDQM identifies anomalous ``bad'' data affected by significant detector malfunction at a rate 4 -- 6 times higher than ``good'' data, demonstrating its effectiveness as a general data quality monitoring tool. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_13789 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Anomaly Detection for Automated Data Quality Monitoring in the CMS Detector Brinkerhoff, Andrew Sutantawibul, Chosila White, Robert Daumann, Caio Freer, Chad Suarez, Indara May, Samuel Nguyen, Vivan Guiang, Jonathan Marsh, Bennett Acosta, Darin Aubuchon, Alex Barberis, Emanuela Bundock, Aaron Collins, Evan Epps, Preston Erdmann, Johannes Flaecher, Henning Huang, Junshen Nie, Ryan Paramesvaran, Sudarshan Rotter, John Salyer, Kaitlin Sawant, Siddhesh Sheokand, Tanvi Wood, Darien High Energy Physics - Experiment Data Analysis, Statistics and Probability Instrumentation and Detectors Successful operation of large particle detectors like the Compact Muon Solenoid (CMS) at the CERN Large Hadron Collider requires rapid, in-depth assessment of data quality. We introduce the ``AutoDQM'' system for Automated Data Quality Monitoring using advanced statistical techniques and unsupervised machine learning. Anomaly detection algorithms based on the beta-binomial probability function, principal component analysis, and neural network autoencoder image evaluation are tested on the full set of proton-proton collision data collected by CMS in 2022. AutoDQM identifies anomalous ``bad'' data affected by significant detector malfunction at a rate 4 -- 6 times higher than ``good'' data, demonstrating its effectiveness as a general data quality monitoring tool. |
| title | Anomaly Detection for Automated Data Quality Monitoring in the CMS Detector |
| topic | High Energy Physics - Experiment Data Analysis, Statistics and Probability Instrumentation and Detectors |
| url | https://arxiv.org/abs/2501.13789 |