Test beam performance measurements for the Phase I upgrade of the CMS pixel detector

A new pixel detector for the CMS experiment was built in order to cope with the instantaneous luminosities anticipated for the Phase I Upgrade of the LHC. The new CMS pixel detector provides four-hit tracking with a reduced material budget as well as new cooling and powering schemes. A new front-end readout chip mitigates buffering and bandwidth limitations, and allows operation at low comparator thresholds. In this paper, comprehensive test beam studies are presented, which have been conducted to verify the design and to quantify the performance of the new detector assemblies in terms of tracking efficiency and spatial resolution. Under optimal conditions, the tracking efficiency is (99.95 ± 0.05) %, while the intrinsic spatial resolutions are (4.80 ± 0.25) μm and (7.99 ± 0.21) μm along the 100 μm and 150 μm pixel pitch, respectively. The findings are compared to a detailed Monte Carlo simulation of the pixel detector and good agreement is found.Peer reviewe

P-Type Silicon Strip Sensors for the new CMS Tracker at HL-L-HC

Abstract: The upgrade of the LHC to the High-Luminosity LHC (HL-LHC) is expected to increase the LHC design luminosity by an order of magnitude. This will require silicon tracking detectors with a significantly higher radiation hardness. The CMS Tracker Collaboration has conducted an irradiation and measurement campaign to identify suitable silicon sensor materials and strip designs for the future outer tracker at the CMS experiment. Based on these results, the collaboration has chosen to use n-in-p type silicon sensors and focus further investigations on the optimization of that sensor type

A Quantum Dot Neural Network

We present a mathematical implementation of a quantum mechanical artificial neural network, in the quasi-continuum regime, using the nonlinearity inherent in the real-time propagation of a quantum system coupled to its environment. Our model is that of a quantum dot molecule coupled to the substrate lattice through optical phonons, and subject to a timevarying external field. Using discretized Feynman path integrals, we find that the real time evolution of the system can be put into a form which resembles the equations for the virtual neuron activation levels of an artificial neural network. The timeline discretization points serve as virtual neurons. We then train the network using a simple gradient descent algorithm, and find it is possible in some regions of the phase space to perform any desired classical logic gate. Because the network is quantum mechanical we can also train purely quantum gates such as a phase shift. I. INTRODUCTION Many artificial neural networks are simulatio..