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The CMS experiment at the CERN LHC will be upgraded to accommodate the 5-fold increase in the instantaneous luminosity expected at the High-Luminosity LHC (HL-LHC). Concomitant with this increase will be an increase in the number of interactions in each bunch crossing and a significant increase in the total ionising dose and fluence. One part of this upgrade is the replacement of the current endcap calorimeters with a high granularity sampling calorimeter equipped with silicon sensors, designed to manage the high collision rates. As part of the development of this calorimeter, a series of beam tests have been conducted with different sampling configurations using prototype segmented silicon detectors. In the most recent of these tests, conducted in late 2018 at the CERN SPS, the performance of a prototype calorimeter equipped with ${approx}12,000rm{~channels}$ of silicon sensors was studied with beams of high-energy electrons, pions and muons. This paper describes the custom-built scalable data acquisition system that was built with readily available FPGA mezzanines and low-cost Raspberry PI computers.
A large prototype of 1.3m3 was designed and built as a demonstrator of the semi-digital hadronic calorimeter (SDHCAL) concept proposed for the future ILC experiments. The prototype is a sampling hadronic calorimeter of 48 units. Each unit is built of
In view of a possible extension of the forward CMS muon detector system and future LHC luminosity upgrades, Micro-Pattern Gas Detectors (MPGDs) are an appealing technology. They can simultaneously provide precision tracking and fast trigger informati
We report on the performance of a monitoring system for a prototype calorimeter for the BTeV experiment that uses Lead Tungstate crystals coupled with photomultiplier tubes. The tests were carried out at the 70 GeV accelerator complex at Protvino, Russia.
Gas Electron Multipliers (GEM) are an interesting technology under consideration for the future upgrade of the forward region of the CMS muon system, specifically in the $1.6<| eta |<2.4$ endcap region. With a sufficiently fine segmentation GEMs can
We present the 3DGAN for the simulation of a future high granularity calorimeter output as three-dimensional images. We prove the efficacy of Generative Adversarial Networks (GANs) for generating scientific data while retaining a high level of accura