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

The Importance of Silicon Detectors for the Higgs Boson Discovery and the Study of its Properties

Recent studies are presented demonstrating the important role played by silicon detectors in the the discovery of the Higgs boson. CMS is planning to replace its in an extended technical stop of the LHC in the winter of 2016 . We present results showing that this replacement will significant increase the sample of Higgs bosons that will be reconstructed enabling precision studies of this particle.Comment: on behalf of the CMS Collaboratio

A new CMS pixel detector for the LHC luminosity upgrade

The CMS inner pixel detector system is planned to be replaced during the first phase of the LHC luminosity upgrade. The plans foresee an ultra low mass system with four barrel layers and three disks on either end. With the expected increase in particle rates, the electronic readout chain will be changed for fast digital signals. An overview of the envisaged design options for the upgraded CMS pixel detector is given, as well as estimates of the tracking and vertexing performance.Comment: 5 pages, 8 figures, proceedings of 8th International Conference on Radiation Effects on Semiconductor Materials Detectors and Device

Level-1 pixel based tracking trigger algorithm for LHC upgrade

The Pixel Detector is the innermost detector of the tracking system of the Compact Muon Solenoid (CMS) experiment at CERN Large Hadron Collider (LHC). It precisely determines the interaction point (primary vertex) of the events and the possible secondary vertexes due to heavy flavours ($b$ and $c$ quarks); it is part of the overall tracking system that allows reconstructing the tracks of the charged particles in the events and combined with the magnetic field to measure their impulsion. The pixel detector allows measuring the tracks in the region closest to the interaction point. The Level-1 (real-time) pixel based tracking trigger is a novel trigger system that is currently being studied for the LHC upgrade. An important goal is developing real-time track reconstruction algorithms able to cope with very high rates and high flux of data in a very harsh environment. The pixel detector has an especially crucial role in precisely identifying the primary vertex of the rare physics events from the large pile-up (PU) of events. The goal of adding the pixel information already at the real-time level of the selection is to help reducing the total level-1 trigger rate while keeping an high selection capability. This is quite an innovative and challenging objective for the experiments upgrade for the High Luminosity LHC (HL-LHC). The special case here addressed is the CMS experiment. This document describes exercises focusing on the development of a fast pixel track reconstruction where the pixel track matches with a Level-1 electron object using a ROOT-based simulation framework.Comment: Submitted to JINST; 12 pages, 10 figures, Contribution to the JINST proceedings for the INFIERI2014 School in Paris, France, July 14-25, 201

Massively Parallel Computing at the Large Hadron Collider up to the HL-LHC

As the Large Hadron Collider (LHC) continues its upward progression in energy and luminosity towards the planned High-Luminosity LHC (HL-LHC) in 2025, the challenges of the experiments in processing increasingly complex events will also continue to increase. Improvements in computing technologies and algorithms will be a key part of the advances necessary to meet this challenge. Parallel computing techniques, especially those using massively parallel computing (MPC), promise to be a significant part of this effort. In these proceedings, we discuss these algorithms in the specific context of a particularly important problem: the reconstruction of charged particle tracks in the trigger algorithms in an experiment, in which high computing performance is critical for executing the track reconstruction in the available time. We discuss some areas where parallel computing has already shown benefits to the LHC experiments, and also demonstrate how a MPC-based trigger at the CMS experiment could not only improve performance, but also extend the reach of the CMS trigger system to capture events which are currently not practical to reconstruct at the trigger level.Comment: 14 pages, 6 figures. Proceedings of 2nd International Summer School on Intelligent Signal Processing for Frontier Research and Industry (INFIERI2014), to appear in JINST. Revised version in response to referee comment

Performance verification of the CMS Phase-1 Upgrade Pixel detector

The CMS tracker consists of two tracking systems utilizing semiconductor technology: the inner pixel and the outer strip detectors. The tracker detectors occupy the volume around the beam interaction region between 3 cm and 110 cm in radius and up to 280 cm along the beam axis. The pixel detector consists of 124 million pixels, corresponding to about 2 m$^{2}$ total area. It plays a vital role in the seeding of the track reconstruction algorithms and in the reconstruction of primary interactions and secondary decay vertices. It is surrounded by the strip tracker with 10 million read-out channels, corresponding to 200 m$^{2}$ total area. The tracker is operated in a high-occupancy and high-radiation environment established by particle collisions in the LHC. The performance of the silicon strip detector continues to be of high quality. The pixel detector that has been used in Run 1 and in the first half of Run 2 was, however, replaced with the so-called Phase-1 Upgrade detector. The new system is better suited to match the increased instantaneous luminosity the LHC would reach before 2023. It was built to operate at an instantaneous luminosity of around 2$\times$10$^{34}$cm$^{-2}$s$^{-1}$. The detector's new layout has an additional inner layer with respect to the previous one; it allows for more efficient tracking with smaller fake rate at higher event pile-up. The paper focuses on the first results obtained during the commissioning of the new detector. It also includes challenges faced during the first data taking to reach the optimal measurement efficiency. Details will be given on the performance at high occupancy with respect to observables such as data-rate, hit reconstruction efficiency, and resolution.Comment: 11 pages, 8 figures, 11th International Conference of Position Sensitive Detectors (PSD11

Track finding in gamma conversions in CMS

A track finding algorithm has been developed for reconstruction of e+e- pairs. It combines the information of the electromagnetic calorimeter with the information provided by the Tracker. Results on reconstruction efficiency of converted photons, as well as on fake rate are shown for single isolated photons and for photons from H->gamma gamma events with pile-up events at 10^33 cm^-2 s^-1 LHC luminosity.Comment: Presented at the 10th International Conference on Advanced Technology and Particle Physics (ICATPP 07), 6 pages, 4 figure

Prospects for Higgs Boson Searches in the Channel WH -> lnbb

We present a method how to detect the WH -> lnbb in the high luminosity LHC environment with the CMS detector. This study is performed with fast detector response simulation including high luminosity event pile up. The main aspects of reconstruction are pile up jet rejection, identification of b-jets and improvement of Higgs mass resolution. The detection potential in the SM for m(H) < 130 GeV and in the MSSM is only encouraging for high integrated luminosity. Nevertheless it is possible to extract important Higgs parameters which are useful to elucidate the nature of the Higgs sector. In combination with other channels, this channel provides valuable information on Higgs boson couplings.Comment: 8 pages, 8 figure

Systems and algorithms for low-latency event reconsturction for upgrades of the level-1 triger of the CMS experiment at CERN

With the increasing centre-of-mass energy and luminosity of the Large Hadron Collider (LHC), the Compact Muon Experiment (CMS) is undertaking upgrades to its triggering system in order to maintain its data-taking efficiency. In 2016, the Phase-1 upgrade to the CMS Level- 1 Trigger (L1T) was commissioned which required the development of tools for validation of changes to the trigger algorithm firmware and for ongoing monitoring of the trigger system during data-taking. A Phase-2 upgrade to the CMS L1T is currently underway, in preparation for the High-Luminosity upgrade of the LHC (HL-LHC). The HL-LHC environment is expected to be particularly challenging for the CMS L1T due to the increased number of simultaneous interactions per bunch crossing, known as pileup. In order to mitigate the effect of pileup, the CMS Phase-2 Outer Tracker is being upgraded with capabilities which will allow it to provide tracks to the L1T for the first time. A key to mitigating pileup is the ability to identify the location and decay products of the signal vertex in each event. For this purpose, two conventional algorithms have been investigated, with a baseline being proposed and demonstrated in FPGA hardware. To extend and complement the baseline vertexing algorithm, Machine Learning techniques were used to evaluate how different track parameters can be included in the vertex reconstruction process. This work culminated in the creation of a deep convolutional neural network, capable of both position reconstruction and association through the intermediate storage of tracks into a z histogram where the optimal weighting of each track can be learned. The position reconstruction part of this end-to-end model was implemented and when compared to the baseline algorithm, a 30% improvement on the vertex position resolution in tt̄ events was observed.Open Acces