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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 and principal component analysis 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. The online version contains supplementary material available at 10.1007/s41781-025-00147-2.
The proposed construction of new particle accelerator-based facilities in the coming decades-and upgrades to existing facilities-provides the unique opportunity to embed innovative environmental impact reduction techniques into their design. This living document provides high-level guidelines to improve environmental sustainability in the planning, construction, operational and decommissioning stages of large accelerator facilities. A collection of various resources is provided, with examples of some existing and suggested practices.