14,864 research outputs found
First-principles study of native point defects in Bi2Se3
Using first-principles method within the framework of the density functional
theory, we study the influence of native point defect on the structural and
electronic properties of BiSe. Se vacancy in BiSe is a double
donor, and Bi vacancy is a triple acceptor. Se antisite (Se) is always
an active donor in the system because its donor level ((+1/0))
enters into the conduction band. Interestingly, Bi antisite(Bi) in
BiSe is an amphoteric dopant, acting as a donor when
0.119eV (the material is typical p-type) and as an acceptor when
0.251eV (the material is typical n-type). The formation energies
under different growth environments (such as Bi-rich or Se-rich) indicate that
under Se-rich condition, Se is the most stable native defect independent
of electron chemical potential . Under Bi-rich condition, Se vacancy
is the most stable native defect except for under the growth window as
0.262eV (the material is typical n-type) and
-0.459eV(Bi-rich), under such growth windows one
negative charged Bi is the most stable one.Comment: 7 pages, 4 figure
Wall-modeled large-eddy simulation integrated with synthetic turbulence generator for multiple-relaxation-time lattice Boltzmann method
The synthetic turbulence generator (STG) lies at the interface of the Reynolds averaged Navier-Stokes (RANS) simulation and large-eddy simulation (LES). This paper presents an STG for the multiple-relaxation-time lattice Boltzmann method (LBM) framework at high friction Reynolds numbers, with consideration of near-wall modeling. The Reichardt wall law, in combination with a force-based method, is used to model the near-wall field. The STG wall-modeled LES results are compared with turbulent channel flow simulations at R e Ï„ = 1000 , 2000 , 5200 at different resolutions. The results demonstrate good agreement with direct numerical simulation, with the adaptation length of 6-8 boundary layer thickness. This method has a wide range of potentials for hybrid RANS/LES-LBM related applications at high friction Reynolds numbers
Photometric identification of blue horizontal branch stars
We investigate the performance of some common machine learning techniques in
identifying BHB stars from photometric data. To train the machine learning
algorithms, we use previously published spectroscopic identifications of BHB
stars from SDSS data. We investigate the performance of three different
techniques, namely k nearest neighbour classification, kernel density
estimation and a support vector machine (SVM). We discuss the performance of
the methods in terms of both completeness and contamination. We discuss the
prospect of trading off these values, achieving lower contamination at the
expense of lower completeness, by adjusting probability thresholds for the
classification. We also discuss the role of prior probabilities in the
classification performance, and we assess via simulations the reliability of
the dataset used for training. Overall it seems that no-prior gives the best
completeness, but adopting a prior lowers the contamination. We find that the
SVM generally delivers the lowest contamination for a given level of
completeness, and so is our method of choice. Finally, we classify a large
sample of SDSS DR7 photometry using the SVM trained on the spectroscopic
sample. We identify 27,074 probable BHB stars out of a sample of 294,652 stars.
We derive photometric parallaxes and demonstrate that our results are
reasonable by comparing to known distances for a selection of globular
clusters. We attach our classifications, including probabilities, as an
electronic table, so that they can be used either directly as a BHB star
catalogue, or as priors to a spectroscopic or other classification method. We
also provide our final models so that they can be directly applied to new data.Comment: To appear in A&A. 19 pages, 22 figures. Tables 7, A3 and A4 available
electronically onlin
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