Identification of High p⊥\rm p_{\perp} Particles with the STAR-RICH Detector

The STAR-RICH detector extends the particle identification capapbilities of the STAR experiment for charged hadrons at mid-rapidity. This detector represents the first use of a proximity-focusing CsI-based RICH detector in a collider experiment. It provides identification of pions and kaons up to 3 GeV/c and protons up to 5 GeV/c. The characteristics and performance of the device in the inaugural RHIC run are described.Comment: 6 pages, 6 figure

Triple-GEM discharge probability studies at CHARM: Simulations and experimental results

The CMS muon system in the region with 2.03<|η|<2.82 is characterized by a very harsh radiation environment which can generate hit rates up to 144 kHz/cm$^{2}$ and an integrated charge of 8 C/cm$^{2}$ over ten years of operation. In order to increase the detector performance and acceptance for physics events including muons, a new muon station (ME0) has been proposed for installation in that region. The technology proposed is Triple—Gas Electron Multiplier (Triple-GEM), which has already been qualified for the operation in the CMS muon system. However, an additional set of studies focused on the discharge probability is necessary for the ME0 station, because of the large radiation environment mentioned above. A test was carried out in 2017 at the Cern High energy AcceleRator Mixed (CHARM) facility, with the aim of giving an estimation of the discharge probability of Triple-GEM detectors in a very intense radiation field environment, similar to the one of the CMS muon system. A dedicated standalone Geant4 simulation was performed simultaneously, to evaluate the behavior expected in the detector exposed to the CHARM field. The geometry of the detector has been carefully reproduced, as well as the background field present in the facility. This paper presents the results obtained from the Geant4 simulation, in terms of sensitivity of the detector to the CHARM environment, together with the analysis of the energy deposited in the gaps and of the processes developed inside the detector. The discharge probability test performed at CHARM will be presented, with a complete discussion of the results obtained, which turn out to be consistent with measurements performed by other groups

Detector Control System for the GE1/1 slice test

Gas Electron Multiplier (GEM) technology, in particular triple-GEM, was selected for the upgrade of the CMS endcap muon system following several years of intense effort on R&D. The triple-GEM chambers (GE1/1) are being installed at station 1 during the second long shutdown with the goal of reducing the Level-1 muon trigger rate and improving the tracking performance in the harsh radiation environment foreseen in the future LHC operation [1]. A first installation of a demonstrator system started at the beginning of 2017: 10 triple-GEM detectors were installed in the CMS muon system with the aim of gaining operational experience and demonstrating the integration of the GE1/1 system into the trigger. In this context, a dedicated Detector Control System (DCS) has been developed, to control and monitor the detectors installed and integrating them into the CMS operation. This paper presents the slice test DCS, describing in detail the different parts of the system and their implementation

Impact of magnetic field on the stability of the CMS GE1/1 GEM detector operation

The Gas Electron Multiplier (GEM) detectors of the GE1/1 station of the CMS experiment have been operated in the CMS magnetic field for the first time on the 7$^{th}$ of October 2021. During the magnetic field ramps, several discharge phenomena were observed, leading to instability in the GEM High Voltage (HV) power system. In order to reproduce the behavior, it was decided to conduct a dedicated test at the CERN North Area with the Goliath magnet, using four GE1/1 spare chambers. The test consisted in studying the characteristics of discharge events that occurred in different detector configurations and external conditions. Multiple magnetic field ramps were performed in sequence: patterns in the evolution of the discharge rates were observed with these data. The goal of this test is the understanding of the experimental conditions inducing discharges and short circuits in a GEM foil. The results of this test lead to the development of procedure for the optimal operation and performance of GEM detectors in the CMS experiment during the magnet ramps. Another important result is the estimation of the probability of short circuit generation, at 68 % confidence level, p$_{short}$$^{HV}$ $^{OFF}$ = 0.42$^{-0.35+0.94}$% with detector HV OFF and p$_{short}$$^{HV}$ $^{OFF}$ < 0.49% with the HV ON. These numbers are specific for the detectors used during this test, but they provide a first quantitative indication on the phenomenon, and a point of comparison for future studies adopting the same procedure

Benchmarking LHC background particle simulation with the CMS triple-GEM detector

In 2018, a system of large-size triple-GEM demonstrator chambers was installed in the CMS experiment at CERN\u27s Large Hadron Collider (LHC). The demonstrator\u27s design mimicks that of the final detector, installed for Run-3. A successful Monte Carlo (MC) simulation of the collision-induced background hit rate in this system in proton-proton collisions at 13 TeV is presented. The MC predictions are compared to CMS measurements recorded at an instantaneous luminosity of 1.5 ×10$^{34}$ cm$^{-2}$ s$^{-1}$. The simulation framework uses a combination of the FLUKA and GEANT4 packages. FLUKA simulates the radiation environment around the GE1/1 chambers. The particle flux by FLUKA covers energy spectra ranging from 10$^{-11}$ to 10$^{4}$ MeV for neutrons, 10$^{-3}$ to 10$^{4}$ MeV for γ\u27s, 10$^{-2}$ to 10$^{4}$ MeV for e$^{±}$, and 10$^{-1}$ to 10$^{4}$ MeV for charged hadrons. GEANT4 provides an estimate of the detector response (sensitivity) based on an accurate description of the detector geometry, the material composition, and the interaction of particles with the detector layers. The detector hit rate, as obtained from the simulation using FLUKA and GEANT4, is estimated as a function of the perpendicular distance from the beam line and agrees with data within the assigned uncertainties in the range 13.7-14.5%. This simulation framework can be used to obtain a reliable estimate of the background rates expected at the High Luminosity LHC

Modeling the triple-GEM detector response to background particles for the CMS Experiment

An estimate of environmental background hit rate on triple-GEM chambers is performed using Monte Carlo (MC) simulation and compared to data taken by test chambers installed in the CMS experiment (GE1/1) during Run-2 at the Large Hadron Collider (LHC). The hit rate is measured using data collected with proton-proton collisions at 13 TeV and a luminosity of 1.5$\times10^{34}$ cm$^{-2}$ s$^{-1}$. The simulation framework uses a combination of the FLUKA and Geant4 packages to obtain the hit rate. FLUKA provides the radiation environment around the GE1/1 chambers, which is comprised of the particle flux with momentum direction and energy spectra ranging from $10^{-11}$ to $10^{4}$ MeV for neutrons, $10^{-3}$ to $10^{4}$ MeV for $\gamma$'s, $10^{-2}$ to $10^{4}$ MeV for $e^{\pm}$, and $10^{-1}$ to $10^{4}$ MeV for charged hadrons. Geant4 provides an estimate of detector response (sensitivity) based on an accurate description of detector geometry, material composition and interaction of particles with the various detector layers. The MC simulated hit rate is estimated as a function of the perpendicular distance from the beam line and agrees with data within the assigned uncertainties of 10-14.5%. This simulation framework can be used to obtain a reliable estimate of background rates expected at the High Luminosity LHC.Comment: 16 pages, 9 figures, 6 table

CAD-based computer vision: the automatic generation of recognition stragtegies

Journal ArticleThree-dimensional model-based computer vision uses geometric models of objects and sensed data to recognize objects in a scene. Likewise, Computer Aided Design (CAD) systems are used to interactively generate three-dimensional models during these fields. Recently, the unification of CAD and vision systems has become the focus of research in the context of manufacturing automation. This paper explores the connection between CAD and computer vision. A method for the automatic generation of recognition strategies based on the geometric properties of shape has been devised and implemented. This uses a novel technique developed for quantifying the following properties of features which compose models used in computer vision: robustness, completeness, consistency, cost, and uniqueness. By utilizing this information, the automatic synthesis of a specialized recognition scheme, called a Strategy Tree, is accomplished. Strategy Trees describe, in a systematic and robust manner. the search process used for recognition and localization of particular objects in the given scene. They consist of selected features which satisfy system constraints and Corroborating Evidence Subtrees which are used in the formation of hypotheses. Verification techniques, used to substantiate or refute these hypotheses, are explored. Experiments utilizing 3-D data are presented

Validation of a novel multivariate method of defining HIV-associated cognitive impairment

Background. The optimum method of defining cognitive impairment in virally suppressed people living with HIV is unknown. We evaluated the relationships between cognitive impairment, including using a novel multivariate method (NMM), patient– reported outcome measures (PROMs), and neuroimaging markers of brain structure across 3 cohorts. Methods. Differences in the prevalence of cognitive impairment, PROMs, and neuroimaging data from the COBRA, CHARTER, and POPPY cohorts (total n = 908) were determined between HIV-positive participants with and without cognitive impairment defined using the HIV-associated neurocognitive disorders (HAND), global deficit score (GDS), and NMM criteria. Results. The prevalence of cognitive impairment varied by up to 27% between methods used to define impairment (eg, 48% for HAND vs 21% for NMM in the CHARTER study). Associations between objective cognitive impairment and subjective cognitive complaints generally were weak. Physical and mental health summary scores (SF-36) were lowest for NMM-defined impairment (P < .05). There were no differences in brain volumes or cortical thickness between participants with and without cognitive impairment defined using the HAND and GDS measures. In contrast, those identified with cognitive impairment by the NMM had reduced mean cortical thickness in both hemispheres (P < .05), as well as smaller brain volumes (P < .01). The associations with measures of white matter microstructure and brain-predicted age generally were weaker. Conclusion. Different methods of defining cognitive impairment identify different people with varying symptomatology and measures of brain injury. Overall, NMM-defined impairment was associated with most neuroimaging abnormalities and poorer selfreported health status. This may be due to the statistical advantage of using a multivariate approac

Rete GPS per allertamento di subsidenza nel campo geotermico di Casaglia (Ferrara)

In questo lavoro vengono presentati i risultati di elaborazioni di reti GPS per il controllo di movimenti di subsidenza nel campo geotermico di Casaglia (Ferrara), monitorato mediante una rete di allertamento di microsismicita'