RPC radiation background simulations for the high luminosity phase in the CMS experiment

The high luminosity expected from the HL-LHC will be a challenge for the CMS detector. The increased rate of particles coming from the collisions and the radioactivity induced in the detector material could cause significant damage and result in a progressive degradation of its performance. Simulation studies are very useful in these scenarios as they allow one to study the radiation environment and the impact on detector performance. Results are presented for CMS RPC stations considering the operating conditions expected at the HL-LHC

The CMS RPC detector performance and stability during LHC RUN-2

The CMS experiment, located at the Large Hadron Collider (LHC) in CERN, has a redundant muon system composed by three different gaseous detector technologies: Cathode Strip Chambers (in the forward regions), Drift Tubes (in the central region), and Resistive Plate Chambers (both its central and forward regions). All three are used for muon reconstruction and triggering. The CMS RPC system confers robustness and redundancy to the muon trigger. The RPC system operation in the challenging background and pileup conditions of the LHC environment is presented. The RPC system provides information to all muon track finders and thus contributing to both muon trigger and reconstruction. The summary of the detector performance results obtained with proton-proton collision at root s = 13 TeV during 2016 and 2017 data taking have been presented. The stability of the system is presented in terms of efficiency and cluster size vs time and increasing instantaneous luminosity. Data-driven predictions about the expected performance during High Luminosity LHC (HL-LHC) stage have been reported

RPC upgrade project for CMS Phase II

The Muon Upgrade Phase II of the Compact Muon Solenoid (CMS) aims to guarantee the optimal conditions of the present system and extend the eta coverage to ensure a reliable system for the High Luminosity Large Hadron Collider (HL-LHC) period. The Resistive Plate Chambers (RPCs) system will upgrade the off-detector electronics (called link system) of the chambers currently installed chambers and place improved RPCs (iRPCs) to cover the high pseudo-rapidity region, a challenging region for muon reconstruction in terms of background and momentum resolution. In order to find the best option for the iRPCs, an R&D program for new detectors was performed and real size prototypes have been tested in the Gamma Irradiation Facility (GIF++) at CERN. The results indicated that the technology suitable for the high background conditions is based on High Pressure Laminate (HPL) double-gap RPC. The RPC Upgrade Phase II program is planned to be ready after the Long Shutdown 3 (LS3)

High voltage calibration method for the CMS RPC detector

The Resistive Plate Chambers (RPC) are used for muon triggers in the CMS experiment. To calibrate the high voltage working-points (WP) and identify degraded detectors due to radiation or chemical damage, a high voltage scan has been performed using 2017 data from pp collisions at a center-of-mass energy of 13 TeV. In this paper, we present the calibration method and the latest results obtained for the 2017 data. A comparison with all scans taken since 2011 is considered to investigate the stability of the detector performance in time

CMSRPC efficiency measurement using the tag-and-probe method

We measure the efficiency of CMS Resistive Plate Chamber (RPC) detectors in proton-proton collisions at the centre-of-mass energy of 13 TeV using the tag-and-probe method. A muon from a Z(0) boson decay is selected as a probe of efficiency measurement, reconstructed using the CMS inner tracker and the rest of CMS muon systems. The overall efficiency of CMS RPC chambers during the 2016-2017 collision runs is measured to be more than 96% for the nominal RPC chambers

Effects of the electronic threshold on the performance of the RPC system of the CMS experiment

Resistive Plate Chambers have a very important role for muon triggering both in the barrel and in the endcap regions of the CMS experiment at the Large Hadron Collider (LHC). In order to optimize their performance, it is of primary importance to tune the electronic threshold of the front-end boards reading the signals from these detectors. In this paper we present the results of a study aimed to evaluate the effects on the RPC efficiency, cluster size and detector intrinsic noise rate, of variations of the electronics threshold voltage

Machine Learning based tool for CMS RPC currents quality monitoring

The muon system of the CERN Compact Muon Solenoid (CMS) experiment includes more than a thousand Resistive Plate Chambers (RPC). They are gaseous detectors operated in the hostile environment of the CMS underground cavern on the Large Hadron Collider where pp luminosities of up to $2\times 10^{34}$ $\text{cm}^{-2}\text{s}^{-1}$ are routinely achieved. The CMS RPC system performance is constantly monitored and the detector is regularly maintained to ensure stable operation. The main monitorable characteristics are dark current, efficiency for muon detection, noise rate etc. Herein we describe an automated tool for CMS RPC current monitoring which uses Machine Learning techniques. We further elaborate on the dedicated generalized linear model proposed already and add autoencoder models for self-consistent predictions as well as hybrid models to allow for RPC current predictions in a distant future

A data mining approach for diagnosis of coronary artery disease

Cardiovascular diseases are very common and are one of the main reasons of death. Being among the major types of these diseases, correct and in-time diagnosis of coronary artery disease (CAD) is very important. Angiography is the most accurate CAD diagnosis method; however, it has many side effects and is costly. Existing studies have used several features in collecting data from patients, while applying different data mining algorithms to achieve methods with high accuracy and less side effects and costs. In this paper, a dataset called Z-Alizadeh Sani with 303 patients and 54 features, is introduced which utilizes several effective features. Also, a feature creation method is proposed to enrich the dataset. Then Information Gain and confidence were used to determine the effectiveness of features on CAD. Typical Chest Pain, Region RWMA2, and age were the most effective ones besides the created features by means of Information Gain. Moreover Q Wave and ST Elevation had the highest confidence. Using data mining methods and the feature creation algorithm, 94.08 accuracy is achieved, which is higher than the known approaches in the literature. © 2013 Elsevier Ireland Ltd