相关论文: The longitudinal neutron resonant spin echo spectr…

A new method for directly sampling the neutron resonance upscattering effect is presented. Alternatives have relied on inefficient rejection sampling techniques or large tabular storage of relative velocities. None of these approaches,…

We describe instrumentation for a high-frequency electron paramagnetic resonance (EPR) and pulsed electron-electron double resonance (PELDOR) spectroscopy. The instrumentation is operated in the frequency range of 107$-$120 GHz and…

Semiconductor spin noise spectroscopy (SNS) has emerged as a unique experimental tool that utilizes spin fluctuations to provide profound insight into undisturbed spin dynamics in doped semiconductors and semiconductor nanostructures. The…

Equations for the neutron reflection amplitude from a magnetic non-collinear wave resonator (NWR) are obtained. It is shown that resonances of the same reflection order of neutrons experiencing spin-flip (spin-flip neutrons) appear in…

The successful deployment of deep learning-based acoustic echo and noise reduction (AENR) methods in consumer devices has spurred interest in developing low-complexity solutions, while emphasizing the need for robust performance in…

Context. Millimetre-wave astronomy is an important tool for both general astrophysics studies and cosmology. A large number of unidentified sources are being detected by the large field-of-view continuum instruments operating on large…

We study the spin response of cold dense neutron matter in the limit of zero momentum transfer, and show that the frequency dependence of the long-wavelength spin response is well constrained by sum-rules and the asymptotic behavior of the…

Recovering a low-CP-rank tensor from noisy linear measurements is a central challenge in high-dimensional data analysis, with applications spanning tensor PCA, tensor regression, and beyond. We exploit the intrinsic geometry of rank-one…

Gravitational-wave parameter estimation for binary neutron star (BNS) systems poses severe computational challenges due to the extended signal duration, which can reach several minutes in current detectors. Neural posterior estimation…

Multi-shot echo planar imaging (msEPI) is a promising approach to achieve high in-plane resolution with high sampling efficiency and low T2* blurring. However, due to the geometric distortion, shot-to-shot phase variations and potential…

Machine learning is becoming widely used in condensed matter physics. Inspired by the concept of image super-resolution, we propose a method to increase the size of lattice spin configurations using deep convolutional neural networks.…

A time of flight MIEZE spectrometer study is presented. The instrument uses solenoid radio frequency (RF) spin flippers with square pole shoes and a magnetic yoke. These flippers can achieve higher static fields than conventional resonant…

Portable, low-field Magnetic Resonance Imaging (MRI) scanners are increasingly being deployed in clinical settings. However, key barriers to their widespread use include low signal-to-noise ratio (SNR), generally low image quality, and long…

Large dynamic index measurement range (n = 1 to n = 1.7) using surface plasmon resonance (SPR) shifts is demonstrated with a ZnSe prism at 632.8 nm, limited by the available high index liquid hosts. In contrast to borosilicate based SPR…

We present an experimental arrangement that permits engineering of cavity back-action on a mesoscopic spin ensemble. By coupling a superconducting thin-film Nb microstrip resonator to a Trityl OX63 electron spin sample, we access different…

This paper investigates the shape reconstructions of sub-wavelength objects from near-field measurements in transverse electromagnetic scattering. This geometric inverse problem is notoriously ill-posed and challenging. We develop a novel…

We demonstrate a deep learning framework capable of recovering physical parameters from the Nonlinear Schrodinger Equation (NLSE) under severe noise conditions. By integrating Physics-Informed Neural Networks (PINNs) with automatic…

Physics-Informed Neural Networks (PINNs) have emerged as a powerful framework for solving partial differential equations (PDEs). However, standard MLP-based PINNs often fail to converge when dealing with complex initial value problems,…

We present a theoretical study of the dynamical spin susceptibility for the intriguing Fe-based superconductor FeSe, based on a tight-binding model developed to account for the temperature-dependent band structure in this system. The model…

We report inelastic neutron scattering (INS) investigations on the bilayer Fe-based superconductor CsCa2Fe4As4F4 above and below its superconducting transition temperature Tc = 28.9 K to investigate the presence of a neutron spin resonance.…