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Abstract The CMS detector is a general-purpose apparatus that detects high-energy collisions produced at the LHC. Online data quality monitoring of the CMS electromagnetic calorimeter is a vital operational tool that allows detector experts to quickly identify, localize, and diagnose a broad range of detector issues that could affect the quality of physics data. A real-time autoencoder-based anomaly detection system using semi-supervised machine learning is presented enabling the detection of anomalies in the CMS electromagnetic calorimeter data. A novel method is introduced which maximizes the anomaly detection performance by exploiting the time-dependent evolution of anomalies as well as spatial variations in the detector response. The autoencoder-based system is able to efficiently detect anomalies, while maintaining a very low false discovery rate. The performance of the system is validated with anomalies found in 2018 and 2022 LHC collision data. In addition, the first results from deploying the autoencoder-based system in the CMS online data quality monitoring workflow during the beginning of Run 3 of the LHC are presented, showing its ability to detect issues missed by the existing system. 

Abstract The operation and performance of the Compact Muon Solenoid (CMS) electromagnetic calorimeter (ECAL) are presented, based on data collected in pp collisions at √_s=13 TeV at the CERN LHC, in the years from 2015 to 2018 (LHC Run 2), corresponding to an integrated luminosity of 151 fb^-1. The CMS ECAL is a scintillating lead-tungstate crystal calorimeter, with a silicon strip preshower detector in the forward region that provides precise measurements of the energy and the time-of-arrival of electrons and photons. The successful operation of the ECAL is crucial for a broad range of physics goals, ranging from observing the Higgs boson and measuring its properties, to other standard model measurements and searches for new phenomena. Precise calibration, alignment, and monitoring of the ECAL response are important ingredients to achieve these goals. To face the challenges posed by the higher luminosity, which characterized the operation of the LHC in Run 2, the procedures established during the 2011–2012 run of the LHC have been revisited and new methods have been developed for the energy measurement and for the ECAL calibration. The energy resolution of the calorimeter, for electrons from Z boson decays reaching the ECAL without significant loss of energy by bremsstrahlung, was better than 1.8%, 3.0%, and 4.5% in the |η| intervals [0.0,0.8], [0.8,1.5], [1.5, 2.5], respectively. This resulting performance is similar to that achieved during Run 1 in 2011–2012, in spite of the more severe running conditions. 

Abstract A Large Ion Collider Experiment (ALICE) has been conceived and constructed as a heavy-ion experiment at the LHC. During LHC Runs 1 and 2, it has produced a wide range of physics results using all collision systems available at the LHC. In order to best exploit new physics opportunities opening up with the upgraded LHC and new detector technologies, the experiment has undergone a major upgrade during the LHC Long Shutdown 2 (2019–2022). This comprises the move to continuous readout, the complete overhaul of core detectors, as well as a new online event processing farm with a redesigned online-offline software framework. These improvements will allow to record Pb-Pb collisions at rates up to 50 kHz, while ensuring sensitivity for signals without a triggerable signature.