arXiv subjects

Zhiyong Zhang

Publications and source records attributed to Zhiyong Zhang.

At least 73 records · Page 4Linked to original sources

arXiv2020

Measurement of the neutron beam profile of the Back-n white neutron facility at CSNS with a Micromegas detector

The Back-n white neutron beam line, which uses back-streaming white neutrons from the spallation target of the China Spallation Neutron Source, is used for nuclear data measurements. A Micromegas-based neutron detector with two variants was specially developed to measure the beam spot distribution for this beam line. In this article, the design, fabrication, and characterization of the detector are described. The results of the detector performance tests are presented, which include the relative electron transparency, the gain and the gain uniformity, and the neutron beam profile reconstruction capability. The result of the first measurement of the Back-n neutron beam spot distribution is also presented.

physics.ins-det↗

arXiv2019

Network Mediation Analysis Using Model-based Eigenvalue Decomposition

This paper proposes a new two-stage network mediation method based on the use of a latent network approach -- model-based eigenvalue decomposition -- for analyzing social network data with nodal covariates. In the decomposition stage of the observed network, no assumption on the metric of the latent space structure is required. In the mediation stage, the most important eigenvectors of a network are used as mediators. This method further offers an innovative way for controlling for the conditional covariates and it only considers the information left in the network. We demonstrate this approach in a detailed tutorial R code provided for four separate cases -- unconditional and conditional model-based eigenvalue decompositions for either a continuous outcome or a binary outcome -- to show its applicability to empirical network data.

stat.ME↗

arXiv2019

Machine learning driven synthesis of few-layered WTe2

Reducing the lateral scale of two-dimensional (2D) materials to one-dimensional (1D) has attracted substantial research interest not only to achieve competitive electronic device applications but also for the exploration of fundamental physical properties. Controllable synthesis of high-quality 1D nanoribbons (NRs) is thus highly desirable and essential for the further study. Traditional exploration of the optimal synthesis conditions of novel materials is based on the trial-and-error approach, which is time consuming, costly and laborious. Recently, machine learning (ML) has demonstrated promising capability in guiding material synthesis through effectively learning from the past data and then making recommendations. Here, we report the implementation of supervised ML for the chemical vapor deposition (CVD) synthesis of high-quality 1D few-layered WTe2 nanoribbons (NRs). The synthesis parameters of the WTe2 NRs are optimized by the trained ML model. On top of that, the growth mechanism of as-synthesized 1T' few-layered WTe2 NRs is further proposed, which may inspire the growth strategies for other 1D nanostructures. Our findings suggest that ML is a powerful and efficient approach to aid the synthesis of 1D nanostructures, opening up new opportunities for intelligent material development.

cond-mat.mtrl-sci↗

arXiv2019

Measurements of differential and angle-integrated cross sections for the $^{10}$B($n, α$)$^{7}$Li reaction in the neutron energy range from 1.0 eV to 2.5 MeV

Differential and angle-integrated cross sections for the $^{10}$B($n, α$)$^{7}$Li, $^{10}$B($n, α$$_{0}$)$^{7}$Li and $^{10}$B($n, α$$_{1}$)$^{7}$Li$^{*}$ reactions have been measured at CSNS Back-n white neutron source. Two enriched (90%) $^{10}$B samples 5.0 cm in diameter and ~85.0 $μ$g/cm$^{2}$ in thickness each with an aluminum backing were prepared, and back-to-back mounted at the sample holder. The charged particles were detected using the silicon-detector array of the Light-charged Particle Detector Array (LPDA) system. The neutron energy E$_{n}$ was determined by TOF (time-of-flight) method, and the valid $α$ events were extracted from the E$_{n}$-Amplitude two-dimensional spectrum. With 15 silicon detectors, the differential cross sections of $α$-particles were measured from 19.2° to 160.8°. Fitted with the Legendre polynomial series, the ($n, α$) cross sections were obtained through integration. The absolute cross sections were normalized using the standard cross sections of the $^{10}$B($n, α$)$^{7}$Li reaction in the 0.3 - 0.5 MeV neutron energy region. The measurement neutron energy range for the $^{10}$B($n, α$)$^{7}$Li reaction is 1.0 eV $\le$ En < 2.5 MeV (67 energy points), and for the $^{10}$B($n, α$$_{0}$)$^{7}$Li and $^{10}$B($n, α$$_{1}$)$^{7}$Li$^{*}$ reactions is 1.0 eV $\le$ En < 1.0 MeV (59 energy points). The present results have been analyzed by the resonance reaction mechanism and the level structure of the $^{11}$B compound system, and compared with existing measurements and evaluations.

nucl-ex↗

arXiv2019

Calibration and Status of the 3D Imaging Calorimeter of DAMPE for Cosmic Ray Physics on Orbit

The DArk Matter Particle Explorer (DAMPE) developed in China was designed to search for evidence of dark matter particles by observing primary cosmic rays and gamma rays in the energy range from 5 GeV to 10 TeV. Since its launch in December 2015, a large quantity of data has been recorded. With the data set acquired during more than a year of operation in space, a precise time-dependent calibration for the energy measured by the BGO ECAL has been developed. In this report, the instrumentation and development of the BGO Electromagnetic Calorimeter (BGO ECAL) are briefly described. The calibration on orbit, including that of the pedestal, attenuation length, minimum ionizing particle peak, and dynode ratio, is discussed, and additional details about the calibration methods and performance in space are presented.

astro-ph.IM↗

arXiv2017

Searching for the Transit of the Earth--mass exoplanet Proxima~Centauri~b in Antarctica: Preliminary Result

Proxima Centauri is known as the closest star from the Sun. Recently, radial velocity observations revealed the existence of an Earth-mass planet around it. With an orbital period of ~11 days, the surface of Proxima Centauri b is temperate and might be habitable. We took a photometric monitoring campaign to search for its transit, using the Bright Star Survey Telescope at the Zhongshan Station in Antarctica. A transit-like signal appearing on 2016 September 8th, is identified tentatively. Its midtime, $T_{C}=2,457,640.1990\pm0.0017$ HJD, is consistent with the predicted ephemeris based on RV orbit in a 1$σ$ confidence interval. Time-correlated noise is pronounced in the light curve of Proxima Centauri, affecting detection of transits. We develop a technique, in a Gaussian process framework, to gauge the statistical significance of potential transit detection. The tentative transit signal reported here, has a confidence level of $2.5σ$. Further detection of its periodic signals is necessary to confirm the planetary transit of Proxima Centauri b. We plan to monitor Proxima Centauri in next Polar night at Dome A in Antarctica, taking the advantage of continuous darkness. \citet{Kipping17} reported two tentative transit-like signals of Proxima Centauri b, observed by the Microvariability and Oscillation of Stars space Telescope in 2014 and 2015, respectively. The midtransit time of our detection is 138 minutes later than that predicted by their transit ephemeris. If all the signals are real transits, the misalignment of the epochs plausibly suggests transit timing variations of Proxima Centauri b induced by an outer planet in this system.

astro-ph.EP↗

arXiv2017

Diffusion MRI measurements in challenging head and brain regions via cross-term spatiotemporally encoding

Cross-term spatiotemporal encoding (xSPEN) is a recently introduced imaging approach delivering single-scan 2D NMR images with unprecedented resilience to field inhomogeneities. The method relies on performing a pre-acquisition encoding and a subsequent image read out while using the disturbing frequency inhomogeneities as part of the image formation processes, rather than as artifacts to be overwhelmed by the application of external gradients. This study introduces the use of this new single-shot MRI technique as a diffusion-monitoring tool, for accessing regions that have hitherto been unapproachable by diffusion-weighted imaging (DWI) methods. In order to achieve this, xSPEN MRIs intrinsic diffusion weighting effects are formulated using a customized, spatially-localized b-matrix analysis; with this, we devise a novel diffusion-weighting scheme that both exploits and overcomes xSPENs strong intrinsic weighting effects. The ability to provide reliable and robust diffusion maps in challenging head and brain regions, including the eyes and the optic nerves, is thus demonstrated in humans at 3T; new avenues for imaging other body regions are also briefly discussed.

physics.med-ph↗

arXiv2017

Offline software for the DAMPE experiment

A software system has been developed for the DArk Matter Particle Explorer (DAMPE) mission, a satellite-based experiment. The DAMPE software is mainly written in C++ and steered using Python script. This article presents an overview of the DAMPE offline software, including the major architecture design and specific implementation for simulation, calibration and reconstruction. The whole system has been successfully applied to DAMPE data analysis, based on which some results from simulation and beam test experiments are obtained and presented.

astro-ph.IM↗

arXiv2017

Partial Fourier techniques in single-shot cross-term spatiotemporal encoded MRI

Purpose: Cross-term spatiotemporal encoding-xSPEN-is a single-shot imaging approach with exceptional resilience to field heterogeneities: its images do not require a priori information nor use post-acquisition corrections, to deliver faithfully the spatial distribution. xSPEN, however, suffers from SNR penalties due to its non-Fourier nature and due to diffusion losses- specially when desiring high resolution. This study explores partial Fourier transform approaches that acting along either the readout or the spatiotemporally-encoded dimensions, reduce these penalties. Methods: xSPEN uses an orthogonal gradient to read, in direct space, the lowbandwidth dimension. This changes substantially the nature of partial Fourier acquisitions vis-a-vis conventional imaging counterparts. A suitable theoretical analysis, however, allows one to implement these procedures along either the low-bandwidth or readout axes. Results: Partial Fourier single-shot xSPEN methods are illustrated on preclinical and human scanners. Owing to their reduction in the experimental acquisition times, these methods provide substantial sensitivity gains vis-a-vis previous implementations for a given targeted in-plane resolution. The magnitude of these gains is as expected. Conclusion: Partial Fourier approaches, particularly when implemented along the xSPEN axis, can provide substantial sensitivity advantages at minimal costs in the operation of the single-shot experiments.

physics.med-ph↗

arXiv2017

A Parameterized Energy Correction Method for Electromagnetic Showers in BGO-ECAL of DAMPE

DAMPE is a space-based mission designed as a high energy particle detector measuring cosmic-rays and $γ-$rays which was successfully launched on Dec.17, 2015. The BGO electromagnetic calorimeter is one of the key sub-detectors of DAMPE for energy measurement of electromagnetic showers produced by $e^{\pm}/γ$. Due to energy loss in dead material and energy leakage outside the calorimeter, the deposited energy in BGO underestimates the primary energy of incident $e^{\pm}/γ$. In this paper, based on detailed MC simulations, a parameterized energy correction method using the lateral and longitudinal information of electromagnetic showers has been studied and verified with data of electron beam test at CERN. The measurements of energy linearity and resolution are significantly improved by applying this correction method for electromagnetic showers.

physics.ins-det↗

arXiv2016

Recurrent Neural Network Training with Dark Knowledge Transfer

Recurrent neural networks (RNNs), particularly long short-term memory (LSTM), have gained much attention in automatic speech recognition (ASR). Although some successful stories have been reported, training RNNs remains highly challenging, especially with limited training data. Recent research found that a well-trained model can be used as a teacher to train other child models, by using the predictions generated by the teacher model as supervision. This knowledge transfer learning has been employed to train simple neural nets with a complex one, so that the final performance can reach a level that is infeasible to obtain by regular training. In this paper, we employ the knowledge transfer learning approach to train RNNs (precisely LSTM) using a deep neural network (DNN) model as the teacher. This is different from most of the existing research on knowledge transfer learning, since the teacher (DNN) is assumed to be weaker than the child (RNN); however, our experiments on an ASR task showed that it works fairly well: without applying any tricks on the learning scheme, this approach can train RNNs successfully even with limited training data.

stat.ML↗

arXiv2016

Temperature Dependence Calibration and Correction of the DAMPE BGO Electromagnetic Calorimeter

A BGO electromagnetic calorimeter (ECAL) is built for the DArk Matter Particle Explorer (DAMPE) mission. The effect of temperature on the BGO ECAL was investigated with a thermal vacuum experiment. The light output of a BGO crystal depends on temperature significantly. The temperature coefficient of each BGO crystal bar has been calibrated, and a correction method is also presented in this paper.

physics.ins-det↗

arXiv2016

The calibration and electron energy reconstruction of the BGO ECAL of the DAMPE detector

The DArk Matter Particle Explorer (DAMPE) is a space experiment designed to search for dark matter indirectly by measuring the spectra of photons, electrons, and positrons up to 10 TeV. The BGO electromagnetic calorimeter (ECAL) is its main sub-detector for energy measurement. In this paper, the instrumentation and development of the BGO ECAL is briefly described. The calibration on the ground, including the pedestal, minimum ionizing particle (MIP) peak, dynode ratio, and attenuation length with the cosmic rays and beam particles is discussed in detail. Also, the energy reconstruction results of the electrons from the beam test are presented.

physics.ins-det↗

arXiv2016

Enhanced bifunctional oxygen catalysis in strained LaNiO3 perovskites

Strain is known to greatly influence low temperature oxygen electrocatalysis on noble metal films, leading to significant enhancements in bifunctional activity essential for fuel cells and metal-air batteries. However, its catalytic impact on transition metal oxide thin films, such as perovskites, is not widely understood. Here, we epitaxially strain the conducting perovskite LaNiO3 to systematically determine its influence on both the oxygen reduction and oxygen evolution reaction. Uniquely, we found that compressive strain could significantly enhance both reactions, yielding a bifunctional catalyst that surpasses the performance of noble metals such as Pt. We attribute the improved bifunctionality to strain-induced splitting of the eg orbitals, which can customize orbital asymmetry at the surface. Analogous to strain-induced shifts in the d-band center of noble metals relative to Fermi level, such splitting can dramatically affect catalytic activity in this perovskite and other potentially more active oxides.

cond-mat.mtrl-sci↗

arXiv2015

A study of energy correction for the electron beam data in the BGO ECAL of the DAMPE

The DArk Matter Particle Explorer (DAMPE) is an orbital experiment aiming at searching for dark matter indirectly by measuring the spectra of photons, electrons and positrons originating from deep space. The BGO electromagnetic calorimeter is one of the key sub-detectors of the DAMPE, which is designed for high energy measurement with a large dynamic range from 5 GeV to 10 TeV. In this paper, some methods for energy correction are discussed and tried, in order to reconstruct the primary energy of the incident electrons. Different methods are chosen for the appropriate energy ranges. The results of Geant4 simulation and beam test data (at CERN) are presented.

physics.ins-det↗

arXiv2015

Probing the anisotropic behaviors of black phosphorus by transmission electron microscope, angular-dependent Raman spectra and electronic transports measurements

In this work, we correlated the angular dependence of the Raman response of black phosphorus to its crystallographic orientation by using transmission electron microscopy and Raman spectroscopy. It was found that the intensity of the A2g mode reached a maximum when the polarization direction of the incident light was parallel to the zigzag crystallographic orientation. Notably, it was further confirmed that the zigzag crystallographic direction exhibited superior conductance and carrier mobility. Because of the lattice extension along the armchair direction, an intensification of the anisotropic Raman response was observed. This work provides direct evidence of the correlation between anisotropic properties and crystallographic direction and represents a turning point in the discussion of the angular-dependent electronic properties of black phosphorus.

cond-mat.mtrl-sci↗

arXiv2015

Improved Deep Speaker Feature Learning for Text-Dependent Speaker Recognition

A deep learning approach has been proposed recently to derive speaker identifies (d-vector) by a deep neural network (DNN). This approach has been applied to text-dependent speaker recognition tasks and shows reasonable performance gains when combined with the conventional i-vector approach. Although promising, the existing d-vector implementation still can not compete with the i-vector baseline. This paper presents two improvements for the deep learning approach: a phonedependent DNN structure to normalize phone variation, and a new scoring approach based on dynamic time warping (DTW). Experiments on a text-dependent speaker recognition task demonstrated that the proposed methods can provide considerable performance improvement over the existing d-vector implementation.

cs.CL↗

arXiv2015

Recognize Foreign Low-Frequency Words with Similar Pairs

Low-frequency words place a major challenge for automatic speech recognition (ASR). The probabilities of these words, which are often important name entities, are generally under-estimated by the language model (LM) due to their limited occurrences in the training data. Recently, we proposed a word-pair approach to deal with the problem, which borrows information of frequent words to enhance the probabilities of low-frequency words. This paper presents an extension to the word-pair method by involving multiple `predicting words' to produce better estimation for low-frequency words. We also employ this approach to deal with out-of-language words in the task of multi-lingual speech recognition.

cs.CL↗