The CMS RPC muon detector utilizes a gas recirculation system called closed loop (CL) to cope with large gas mixture volumes and costs. A systematic study of CL gas purifiers has been carried out over 400 days between July 2008 and August 2009 at CER
N in a low-radiation test area, with the use of RPC chambers with currents monitoring, and gas analysis sampling points. The study aimed to fully clarify the presence of pollutants, the chemistry of purifiers used in the CL, and the regeneration procedure. Preliminary results on contaminants release and purifier characterization are reported.
The response of RPC detectors is highly sensitive to environmental variables. A novel approach is presented to model the response of RPC detectors in a variety of experimental conditions. The algorithm, based on Artificial Neural Networks, has been d
eveloped and tested on the CMS RPC gas gain monitoring system during commissioning.
