Identification and Filtering of Uncharacteristic Noise in the CMS Hadron Calorimeter

TL;DR

This paper presents algorithms for identifying and filtering uncharacteristic noise in the CMS hadron calorimeter, improving data quality by removing noise events effectively at trigger level for better physics analysis.

Contribution

The paper introduces new noise rejection algorithms tested on various CMS data types, demonstrating their effectiveness in real-time noise suppression during LHC operations.

Findings

Algorithms remove 90% of noise events with fake missing transverse energy above 100 GeV

Effective noise filtering at trigger level enhances CMS data quality

Validated on cosmic ray, noise, and beam data from 2008

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.