arXiv subjects

H. T. Wang

Publications and source records attributed to H. T. Wang.

6 recordsLinked to original sources

arXiv2026

Revealing Higher-Order Topological Bulk-boundary Correspondence in Bismuth Crystal with Spin-helical Hinge State Loop and Proximity Superconductivity

Topological materials are typically characterized by gapless boundary states originated from nontrivial bulk band topology, known as topological bulk-boundary correspondence. Recently, this fundamental concept has been generalized in higher-order topological insulators (HOTIs). E.g., a second-order three-dimensional (3D) TI hosts one-dimensional (1D) topological hinge states winding around the crystal. However, a complete verification of higher-order topology is still lacking as it requires probing all the crystal boundaries. Here we studied a promising candidate of second-order TI, bismuth (Bi), in the form of mesoscopic crystals grown on superconducting V3Si. Using low-temperature scanning tunneling microscopy, we directly observed dispersive 1D states on various hinges of the crystal. Upon introducing magnetic scatterers, new scattering channels emerged selectively on certain hinges, revealing their spin-helical nature. Combining first-principle calculation and global symmetry analysis, we find these hinge states are topological and formed a closed loop encircling the crystal. This provides direct evidence on the higher-order topology in Bi. Moreover, proximity superconductivity is observed in the topological hinge states, enabling HOTI as a promising platform for realizing topological superconductivity and Majorana quasiparticles.

cond-mat.mes-hall↗

arXiv2025

Correlation-driven 3d Heavy Fermion behavior in LiV2O4

LiV2O4 is a spinel-structured compound that stands out as the first known 3d-electron system exhibiting typical heavy fermion behavior. A central question is how such strong mass renormalization emerges in the absence of f-electrons. In this work, we investigate the three-dimensional electronic structure of LiV2O4 thin films using angle-resolved photoemission spectroscopy (ARPES). We identify that an electron-like flat band is derived from a1g orbitals, along with a highly dispersive e'g band strongly coupled with phonons. The overall agreement with dynamical mean-field theory (DMFT) calculations highlights the essential role of inter-orbital Hund's coupling in reducing the a1g bandwidth to 25 meV, approaching a Mott state. Notably, we find that heavy-fermion behavior arises from additional renormalization at the a1g band near the Fermi level, likely driven by many-body interactions at energy scales down to a few meV and potentially linked to geometric frustration inherent to the spinel lattice. These results provide crucial insights into the origin of the heavy fermion behavior in 3d-electron systems.

cond-mat.str-el↗

arXiv2023

Automatic Stack Velocity Picking Using an Unsupervised Ensemble Learning Method

Seismic velocity picking algorithms that are both accurate and efficient can greatly speed up seismic data processing, with the primary approach being the use of velocity spectra. Despite the development of some supervised deep learning-based approaches to automatically pick the velocity, they often come with costly manual labeling expenses or lack interpretability. In comparison, using physical knowledge to drive unsupervised learning techniques has the potential to solve this problem in an efficient manner. We suggest an Unsupervised Ensemble Learning (UEL) approach to achieving a balance between reliance on labeled data and picking accuracy, with the aim of determining the stack velocity. UEL makes use of the data from nearby velocity spectra and other known sources to help pick efficient and reasonable velocity points, which are acquired through a clustering technique. Testing on both the synthetic and field data sets shows that UEL is more reliable and precise in auto-picking than traditional clustering-based techniques and the widely used Convolutional Neural Network (CNN) method.

cs.LG↗

arXiv2022

Automatic Velocity Picking Using a Multi-Information Fusion Deep Semantic Segmentation Network

Velocity picking, a critical step in seismic data processing, has been studied for decades. Although manual picking can produce accurate normal moveout (NMO) velocities from the velocity spectra of prestack gathers, it is time-consuming and becomes infeasible with the emergence of large amount of seismic data. Numerous automatic velocity picking methods have thus been developed. In recent years, deep learning (DL) methods have produced good results on the seismic data with medium and high signal-to-noise ratios (SNR). Unfortunately, it still lacks a picking method to automatically generate accurate velocities in the situations of low SNR. In this paper, we propose a multi-information fusion network (MIFN) to estimate stacking velocity from the fusion information of velocity spectra and stack gather segments (SGS). In particular, we transform the velocity picking problem into a semantic segmentation problem based on the velocity spectrum images. Meanwhile, the information provided by SGS is used as a prior in the network to assist segmentation. The experimental results on two field datasets show that the picking results of MIFN are stable and accurate for the scenarios with medium and high SNR, and it also performs well in low SNR scenarios.

cs.CV↗

arXiv2017

Anisotropy in the thermal hysteresis of resistivity and charge density wave nature of single crystal SrFeO3-delta: X-ray absorption and photoemission studies

The local electronic and atomic structures of the high-quality single crystal of SrFeO3-{delta} ({delta}~0.19) were studied using temperature-dependent x-ray absorption and valence-band photoemission spectroscopy (VB-PES) to investigate the origin of anisotropic resistivity in the ab-plane and along the c-axis close to the region of thermal hysteresis (near temperature for susceptibility maximum, Tm~78 K). All experiments herein were conducted during warming and cooling processes. The Fe L3,2-edge X-ray linear dichrois results show that during cooling from room temperature to below the transition temperature, the unoccupied Fe 3d eg states remain in persistently out-of-plane 3d3z2-r2 orbitals. In contrast, in the warming process below the transition temperature, they change from 3d3z2-r2 to in-plane 3dx2-y2 orbitals. The nearest-neighbor (NN) Fe-O bond lengths also exhibit anisotropic behavior in the ab-plane and along the c-axis below Tm. The anisotropic NN Fe-O bond lengths and Debye-Waller factors stabilize the in-plane Fe 3dx2-y2 and out-of-plane 3d3z2-r2 orbitals during warming and cooling, respectively. Additionally, a VB-PES study further confirms that a relative band gap opens at low temperature in both the ab-plane and along the c-axis, providing the clear evidence of the charge-density-wave nature of SrFeO3-{delta} ({delta}~ 0.19) single crystal.

cond-mat.str-el↗

arXiv2017

The Variability and Period Analysis for the BL Lac AO 0235+164

Variability is one of the extreme observational properties of BL Lacertae objects. AO 0235+164 is a well studied BL Lac through the whole electro-magnetic wavebands. In the present work, we show its optical R band photometric observations carried out during the period of Nov, 2006 to Dec. 2012 using the Ap6E CCD camera attached to the primary focus of the $\rm 70-cm$ meniscus telescope at Abastumani Observatory, Georgia. It shows a large variation of $ΔR$ = 4.88 mag (14.19 - 19.07 mag) and a short time scale of $ΔT_v$ = 73.5 min during our monitoring period. During the period of Dec. 2006 to Nov. 2009, we made radio observations of the source using the 25-m radio telescope at Xinjiang Astronomical Observatory. When a discrete correlation function (DCF) is adopted to the optical and radio observations, we found that the optical variation leads the radio variation by 23.2$\pm$12.9 days.

astro-ph.HE↗