Superinjection of holes in homojunction diodes based on wide-bandgap semiconductors

Electrically driven light sources are essential in a wide range of applications, from indication and display technologies to high-speed data communication and quantum information processing. Wide-bandgap semiconductors promise to advance solid-state lighting by delivering novel light sources. However, electrical pumping of these devices is still a challenging problem. Many wide-bandgap semiconductor materials, such as SiC, GaN, AlN, ZnS, and Ga2O3, can be easily doped n-type, but their efficient p-type doping is extremely difficult. The lack of holes due to the high activation energy of acceptors greatly limits the performance and practical applicability of wide-bandgap semiconductor devices. Here, we study a novel effect which allows homojunction semiconductors devices, such as p-i-n diodes, to operate well above the limit imposed by doping of the p-type material. Using a rigorous numerical approach, we show that the density of injected holes can exceed the density of holes in the p-type injection layer by up to three orders of magnitude, which gives the possibility to significantly overcome the doping problem. We present a clear physical explanation of this unexpected feature of wide-bandgap semiconductor p-i-n diodes and closely examine it in 4H-SiC, 3C-SiC, AlN and ZnS structures. The predicted effect can be exploited to develop bright light emitting devices, especially electrically driven non-classical light sources based on color centers in SiC, AlN, ZnO and other wide-bandgap semiconductors.Comment: 6 figure

Thermalization after holographic bilocal quench

We study thermalization in the holographic (1+1)-dimensional CFT after simultaneous generation of two high-energy excitations in the antipodal points on the circle. The holographic picture of such quantum quench is the creation of BTZ black hole from a collision of two massless particles. We perform holographic computation of entanglement entropy and mutual information in the boundary theory and analyze their evolution with time. We show that equilibration of the entanglement in the regions which contained one of the initial excitations is generally similar to that in other holographic quench models, but with some important distinctions. We observe that entanglement propagates along a sharp effective light cone from the points of initial excitations on the boundary. The characteristics of entanglement propagation in the global quench models such as entanglement velocity and the light cone velocity also have a meaning in the bilocal quench scenario. We also observe the loss of memory about the initial state during the equilibration process. We find that the memory loss reflects on the time behavior of the entanglement similarly to the global quench case, and it is related to the universal linear growth of entanglement, which comes from the interior of the forming black hole. We also analyze general two-point correlation functions in the framework of the geodesic approximation, focusing on the study of the late time behavior.Comment: 75 pages, 41 figure, v2: typos corrected, references and minor comments added, v3: published versio

The complete reducibility of some GF(2)A7-modules

It is proved that, if G is a finite group with a nontrivial normal 2-subgroup Q such that G/Q ∼= A 7 and an element of order 5 from G acts freely on Q, then the extension G over Q is splittable, Q is an elementary abelian group, and Q is the direct product of minimal normal subgroups of G each of which is isomorphic, as a G/Q-module, to one of the two 4-dimensional irreducible GF(2)A7-modules that are conjugate with respect to an outer automorphism of the group A7. © 2013 Pleiades Publishing, Ltd

VEXAS: VISTA EXtension to Auxiliary Surveys -- Data Release 2: Machine-learning based classification of sources in the Southern Hemisphere

We present the second public data release (DR) of the VISTA EXtension to Auxiliary Surveys (VEXAS), where we classify objects into stars, galaxies and quasars based on an ensemble of machine learning algorithms. The aim of VEXAS is to build the widest multi-wavelength catalogue, providing reference magnitudes, colours and morphological information for a large number of scientific uses. We apply an ensemble of 32 different machine learning models, based on three different algorithms and on different magnitude sets, training samples and classification problems on the three VEXAS DR1 optical+infrared (IR) tables. The tables were created in DR1 cross-matching VISTA near-IR data with WISE far-IR data and with optical magnitudes from the Dark Energy Survey (VEXAS-DESW), the Sky Mapper Survey (VEXAS-SMW), and the PanSTARRS (VEXAS-PSW). We assemble a large table of spectroscopically confirmed objects (415 628 unique objects), based on the combination of 6 different spectroscopic surveys that we use for training. We develop feature imputation to classify also objects for which magnitudes in one or more bands are missing. We classify in total ~90 million objects in the Southern Hemisphere. Among these,~62.9M (~52.6M) are classified as 'high confidence' ('secure') stars, ~920k (~750k) as 'high confidence' ('secure') quasars and ~34.8M (~34.1M) as 'high confidence' ('secure') galaxies, with probabilities $p_{\rm class}\ge 0.7$ ($p_{\rm class}\ge 0.9$). The density of high-confidence extragalactic objects varies strongly with the survey depth: at $p_{\rm class}\ge 0.7$, there are 111/deg$^2$ quasars in the VEXAS-DESW footprint and 103/deg$^2$ in the VEXAS-PSW footprint, while only 10.7/deg$^2$ in the VEXAS-SM footprint. Improved depth in the midIR and coverage in the optical and nearIR are needed for the SM footprint that is not already covered by DESW and PSW.Comment: 25 pages, 18 figures, 8 tables. Accepted for publication on A&A. The VEXAS tables are publicly available through the ESO Phase 3 here: https://archive.eso.org/scienceportal/home?data_collection=VEXAS. The DR2 tables update the DR1 with the addition of imputed magnitudes and membership probabilities to each of the three classe

Machine learning technique for morphological classification of galaxies at z<0.1 from the SDSS

Methods. We used different galaxy classification techniques: human labeling, multi-photometry diagrams, Naive Bayes, Logistic Regression, Support Vector Machine, Random Forest, k-Nearest Neighbors, and k-fold validation. Results. We present results of a binary automated morphological classification of galaxies conducted by human labeling, multiphotometry, and supervised Machine Learning methods. We applied its to the sample of galaxies from the SDSS DR9 with redshifts of 0.02 < z < 0.1 and absolute stellar magnitudes of 24m < Mr < 19.4m. To study the classifier, we used absolute magnitudes: Mu, Mg, Mr , Mi, Mz, Mu-Mr , Mg-Mi, Mu-Mg, Mr-Mz, and inverse concentration index to the center R50/R90. Using the Support vector machine classifier and the data on color indices, absolute magnitudes, inverse concentration index of galaxies with visual morphological types, we were able to classify 316 031 galaxies from the SDSS DR9 with unknown morphological types. Conclusions. The methods of Support Vector Machine and Random Forest with Scikit-learn machine learning in Python provide the highest accuracy for the binary galaxy morphological classification: 96.4% correctly classified (96.1% early E and 96.9% late L types) and 95.5% correctly classified (96.7% early E and 92.8% late L types), respectively. Applying the Support Vector Machine for the sample of 316 031 galaxies from the SDSS DR9 at z < 0.1, we found 141 211 E and 174 820 L types among them.Comment: 10 pages, 5 figures. The presentation of these results was given during the EWASS-2017, Symposium "Astroinformatics: From Big Data to Understanding the Universe at Large". It is vailable through \url{http://space.asu.cas.cz/~ewass17-soc/Presentations/S14/Dobrycheva_987.pdf

KiDS0239-3211: A new gravitational quadruple lens candidate

We report the discovery of a candidate to quadrupole gravitationally lensed system KiDS0239-3211 based on the public data release 3 of the KiDS survey and machine learning techniques