Identification and Filtering of Uncharacteristic Noise in the CMS Hadron Calorimeter
The CMS Collaboration
TL;DR
This paper presents the identification and filtering of uncharacteristic HCAL noise observed during CMS commissioning. It introduces algorithms to flag intermittent HPD/RBX noise, PMT window hits, ADC saturation, and persistent hot/dead channels, validates them on CRUZET/CRAFT and MC datasets, and demonstrates substantial reductions in fake MET at both trigger and offline levels. The results show effective noise suppression with limited impact on physics signals, indicating practical benefits for CMS MET-based triggers during early LHC data taking. The methods provide a framework for ongoing HCAL noise management and potential deployment at the trigger level to maintain data quality in high-rate environments.
Abstract
Commissioning studies of the CMS hadron calorimeter have identified sporadic uncharacteristic noise and a small number of malfunctioning calorimeter channels. Algorithms have been developed to identify and address these problems in the data. The methods have been tested on cosmic ray muon data, calorimeter noise data, and single beam data collected with CMS in 2008. The noise rejection algorithms can be applied to LHC collision data at the trigger level or in the offline analysis. The application of the algorithms at the trigger level is shown to remove 90% of noise events with fake missing transverse energy above 100 GeV, which is sufficient for the CMS physics trigger operation.
