Magnetoresistance Dynamics in Superparamagnetic Co-Fe- B Nanodots

Individual disk-shaped Co-Fe-B nanodots are driven into a superparamagnetic state by a spin-transfer torque, and their time-dependent magnetoresistance fluctuations are measured as a function of current. A thin layer of oxidation at the edges has a dramatic effect on the magnetization dynamics. A combination of experimental results and atomistic spin simulations shows that pinning to oxide grains can reduce the likelihood that fluctuations lead to reversal, and can even change the easy-axis direction. Exchange-bias loop shifts and training effects are observed even at room temperature after brief exposure to small fields. The results have implications for studies of core-shell nanoparticles and small magnetic tunnel junctions and spin-torque oscillators

Molecular typing of Dengue virus circulating in Kuching district of Sarawak, Malaysian Borneo, from 2014 to2016

: Dengue fever is endemic to Malaysia and the past five years has seen a large increase in recorded dengue cases. All four dengue serotypes have been recorded in Malaysia and the state of Sarawak. Historically for Sarawak, DENV-1 and DENV-2 were first serologically detected in 1962, while DENV-3 and DENV-4 were picked up by PCR and Sanger sequencing in 1997–1999. However,no serotype sequence data for Sarawak has been published in recent years

BBN bounds on active-sterile neutrino mixing

Nucleosynthesis restrictions on mixing of active neutrinos with possible sterile ones are obtained with the account of experimentally determined mixing between all active neutrinos. The earlier derived bounds, valid in the absence of active-active mixing, are reanalyzed and significant difference is found in the resonance case. The results are obtained both analytically and numerically by solution of complete system of integro-differential kinetic equations. A good agreement between analytical and numerical approaches is demonstrated. A role of possibly large cosmological lepton asymmetry is discussed.Comment: 38 pages, 10 figure

The performance of the jet trigger for the ATLAS detector during 2011 data taking

The performance of the jet trigger for the ATLAS detector at the LHC during the 2011 data taking period is described. During 2011 the LHC provided proton–proton collisions with a centre-of-mass energy of 7 TeV and heavy ion collisions with a 2.76 TeV per nucleon–nucleon collision energy. The ATLAS trigger is a three level system designed to reduce the rate of events from the 40 MHz nominal maximum bunch crossing rate to the approximate 400 Hz which can be written to offline storage. The ATLAS jet trigger is the primary means for the online selection of events containing jets. Events are accepted by the trigger if they contain one or more jets above some transverse energy threshold. During 2011 data taking the jet trigger was fully efficient for jets with transverse energy above 25 GeV for triggers seeded randomly at Level 1. For triggers which require a jet to be identified at each of the three trigger levels, full efficiency is reached for offline jets with transverse energy above 60 GeV. Jets reconstructed in the final trigger level and corresponding to offline jets with transverse energy greater than 60 GeV, are reconstructed with a resolution in transverse energy with respect to offline jets, of better than 4 % in the central region and better than 2.5 % in the forward direction

Pileup mitigation at CMS in 13 TeV data

With increasing instantaneous luminosity at the LHC come additional reconstruction challenges. At high luminosity, many collisions occur simultaneously within one proton-proton bunch crossing. The isolation of an interesting collision from the additional "pileup" collisions is needed for effective physics performance. In the CMS Collaboration, several techniques capable of mitigating the impact of these pileup collisions have been developed. Such methods include charged-hadron subtraction, pileup jet identification, isospin-based neutral particle "δβ" correction, and, most recently, pileup per particle identification. This paper surveys the performance of these techniques for jet and missing transverse momentum reconstruction, as well as muon isolation. The analysis makes use of data corresponding to 35.9 fb$^{-1}$ collected with the CMS experiment in 2016 at a center-of-mass energy of 13 TeV. The performance of each algorithm is discussed for up to 70 simultaneous collisions per bunch crossing. Significant improvements are found in the identification of pileup jets, the jet energy, mass, and angular resolution, missing transverse momentum resolution, and muon isolation when using pileup per particle identification

Identification of heavy, energetic, hadronically decaying particles using machine-learning techniques

Machine-learning (ML) techniques are explored to identify and classify hadronic decays of highly Lorentz-boosted W/Z/Higgs bosons and top quarks. Techniques without ML have also been evaluated and are included for comparison. The identification performances of a variety of algorithms are characterized in simulated events and directly compared with data. The algorithms are validated using proton-proton collision data at √s = 13TeV, corresponding to an integrated luminosity of 35.9 fb−1. Systematic uncertainties are assessed by comparing the results obtained using simulation and collision data. The new techniques studied in this paper provide significant performance improvements over non-ML techniques, reducing the background rate by up to an order of magnitude at the same signal efficiency