Conveners
CMS
- Niam Patel (Lancaster University)
Novel machine learning-based anomaly detection Level 1 (L1) triggers are currently under development at CMS, namely AXOL1TL and CICADA. The former employs a variational autoencoder, while the latter utilizes a convolutional autoencoder. These triggers aim to balance rate reduction with model independence, enabling the selection of potentially significant events that might be overlooked by...
The CMS detector at the High-Luminosity Large Hadron Collider (HL-LHC) will operate in challenging conditions with expected pile-up of up to 200 collisions per bunch crossing, necessitating the development of a more resilient primary vertex (PV) reconstruction method to ensure the integrity of data analysis and the efficiency of the CMS triggering system. This contribution describes...
The Large Hadron Collider will undergo a luminosity upgrade targeting a peak instantaneous luminosity ranging from 5 to 7.5x10$^{34}$cm$^{-2}$. The overall goal of the High Luminosity LHC (HL-LHC) is to achieve 3000 to 4000 fb$^{-1}$ proton collisions at a 13 to14 TeV center of mass energy. Due to the hard environmental condition in the HL-LHC, the outer tracker of the CMS experiment will also...
Current data quality monitoring (DQM) tools at CMS offer granularity limited to per-run analysis. Consequently, issues manifesting at the per-lumisection level can go unnoticed or, even if detectable, often lead to the classification of the whole run as bad, resulting in unnecessary data loss. Additionally, shifters have to evaluate a large set of monitoring elements during their long shifts,...