Spin and energy transfer in nanocrystals without transport of charge

We describe a mechanism of spin transfer between individual quantum dots that does not require tunneling. Incident circularly-polarized photons create inter-band excitons with non-zero electron spin in the first quantum dot. When the quantum-dot pair is properly designed, this excitation can be transferred to the neighboring dot via the Coulomb interaction with either {\it conservation} or {\it flipping} of the electron spin. The second dot can radiate circularly-polarized photons at lower energy. Selection rules for spin transfer are determined by the resonant conditions and by the strong spin-orbit interaction in the valence band of nanocrystals. Coulomb-induced energy and spin transfer in pairs and chains of dots can become very efficient under resonant conditions. The electron can preserve its spin orientation even in randomly-oriented nanocrystals.Comment: 13 pages, 3 figure

Spin transport of electrons through quantum wires with spatially-modulated strength of the Rashba spin-orbit interaction

We study ballistic transport of spin-polarized electrons through quantum wires in which the strength of the Rashba spin-orbit interaction (SOI) is spatially modulated. Subband mixing, due to SOI, between the two lowest subbands is taken into account. Simplified approximate expressions for the transmission are obtained for electron energies close to the bottom of the first subband and near the value for which anticrossing of the two lowest subbands occurs. In structures with periodically varied SOI strength, {\it square-wave} modulation on the spin transmission is found when only one subband is occupied and its possible application to the spin transistor is discussed. When two subbands are occupied the transmission is strongly affected by the existence of SOI interfaces as well as by the subband mixing

Measurement of Two-Qubit States by a Two-Island Single Electron Transistor

We solve the master equations of two charged qubits measured by a single-electron transistor (SET) consisted of two islands. We show that in the sequential tunneling regime the SET current can be used for reading out results of quantum calculations and providing evidences of two-qubit entanglement, especially when the interaction between the two qubits is weak

Using graph neural networks to reconstruct charged pion showers in the CMS High Granularity Calorimeter

International audienceA novel method to reconstruct the energy of hadronic showers in the CMS High Granularity Calorimeter (HGCAL) is presented. The HGCAL is a sampling calorimeter with very fine transverse and longitudinal granularity. The active media are silicon sensors and scintillator tiles readout by SiPMs and the absorbers are a combination of lead and Cu/CuW in the electromagnetic section, and steel in the hadronic section. The shower reconstruction method is based on graph neural networks and it makes use of a dynamic reduction network architecture. It is shown that the algorithm is able to capture and mitigate the main effects that normally hinder the reconstruction of hadronic showers using classical reconstruction methods, by compensating for fluctuations in the multiplicity, energy, and spatial distributions of the shower's constituents. The performance of the algorithm is evaluated using test beam data collected in 2018 prototype of the CMS HGCAL accompanied by a section of the CALICE AHCAL prototype. The capability of the method to mitigate the impact of energy leakage from the calorimeter is also demonstrated

Using graph neural networks to reconstruct charged pion showers in the CMS High Granularity Calorimeter

International audienceA novel method to reconstruct the energy of hadronic showers in the CMS High Granularity Calorimeter (HGCAL) is presented. The HGCAL is a sampling calorimeter with very fine transverse and longitudinal granularity. The active media are silicon sensors and scintillator tiles readout by SiPMs and the absorbers are a combination of lead and Cu/CuW in the electromagnetic section, and steel in the hadronic section. The shower reconstruction method is based on graph neural networks and it makes use of a dynamic reduction network architecture. It is shown that the algorithm is able to capture and mitigate the main effects that normally hinder the reconstruction of hadronic showers using classical reconstruction methods, by compensating for fluctuations in the multiplicity, energy, and spatial distributions of the shower's constituents. The performance of the algorithm is evaluated using test beam data collected in 2018 prototype of the CMS HGCAL accompanied by a section of the CALICE AHCAL prototype. The capability of the method to mitigate the impact of energy leakage from the calorimeter is also demonstrated

Using graph neural networks to reconstruct charged pion showers in the CMS High Granularity Calorimeter

International audienceA novel method to reconstruct the energy of hadronic showers in the CMS High Granularity Calorimeter (HGCAL) is presented. The HGCAL is a sampling calorimeter with very fine transverse and longitudinal granularity. The active media are silicon sensors and scintillator tiles readout by SiPMs and the absorbers are a combination of lead and Cu/CuW in the electromagnetic section, and steel in the hadronic section. The shower reconstruction method is based on graph neural networks and it makes use of a dynamic reduction network architecture. It is shown that the algorithm is able to capture and mitigate the main effects that normally hinder the reconstruction of hadronic showers using classical reconstruction methods, by compensating for fluctuations in the multiplicity, energy, and spatial distributions of the shower's constituents. The performance of the algorithm is evaluated using test beam data collected in 2018 prototype of the CMS HGCAL accompanied by a section of the CALICE AHCAL prototype. The capability of the method to mitigate the impact of energy leakage from the calorimeter is also demonstrated

Search for new Higgs bosons via same-sign top quark pair production in association with a jet in proton-proton collisions at s=13TeV

A search is presented for new Higgs bosons in proton-proton (pp) collision events in which a same-sign top quark pair is produced in association with a jet, via the pp→tH/A→ttc‾ and pp→tH/A→ttu‾ processes. Here, H and A represent the extra scalar and pseudoscalar boson, respectively, of the second Higgs doublet in the generalized two-Higgs-doublet model (g2HDM). The search is based on pp collision data collected at a center-of-mass energy of 13 TeV with the CMS detector at the LHC, corresponding to an integrated luminosity of 138fb−1. Final states with a same-sign lepton pair in association with jets and missing transverse momentum are considered. New Higgs bosons in the 200–1000 GeV mass range and new Yukawa couplings between 0.1 and 1.0 are targeted in the search, for scenarios in which either H or A appear alone, or in which they coexist and interfere. No significant excess above the standard model prediction is observed. Exclusion limits are derived in the context of the g2HDM

Portable Acceleration of CMS Computing Workflows with Coprocessors as a Service

Computing demands for large scientific experiments, such as the CMS experiment at the CERN LHC, will increase dramatically in the next decades. To complement the future performance increases of software running on central processing units (CPUs), explorations of coprocessor usage in data processing hold great potential and interest. Coprocessors are a class of computer processors that supplement CPUs, often improving the execution of certain functions due to architectural design choices. We explore the approach of Services for Optimized Network Inference on Coprocessors (SONIC) and study the deployment of this as-a-service approach in large-scale data processing. In the studies, we take a data processing workflow of the CMS experiment and run the main workflow on CPUs, while offloading several machine learning (ML) inference tasks onto either remote or local coprocessors, specifically graphics processing units (GPUs). With experiments performed at Google Cloud, the Purdue Tier-2 computing center, and combinations of the two, we demonstrate the acceleration of these ML algorithms individually on coprocessors and the corresponding throughput improvement for the entire workflow. This approach can be easily generalized to different types of coprocessors and deployed on local CPUs without decreasing the throughput performance. We emphasize that the SONIC approach enables high coprocessor usage and enables the portability to run workflows on different types of coprocessors