Related papers: Probing Three-Dimensional Magnetic Fields: III -- …
Machine learning promises to deliver powerful new approaches to neutron scattering from magnetic materials. Large scale simulations provide the means to realise this with approaches including spin-wave, Landau Lifshitz, and Monte Carlo…
Understanding the role of magnetic fields in star-forming regions is of fundamental importance. In the near future, the exceptional sensitivity of SKA will offer a unique opportunity to evaluate the magnetic field strength in molecular…
LOFAR observations of diffuse interstellar polarization at meter wavelengths reveal intricate polarized intensity structures with an unexpected correlation with neutral HI filaments that could not be reproduced in simulations with low cold…
We consider turbulent synchrotron emitting media that also exhibits Faraday rotation and provide a statistical description of synchrotron polarization fluctuations. In particular, we consider these fluctuations as a function of the spatial…
Based on the modern understanding of MHD turbulence theory, we propose a new method for measuring the spectral properties of magnetic turbulence by synchrotron polarization gradient analysis. Using synthetic polarization observational data,…
Modern cosmological research in large scale structure has witnessed an increasing number of applications of machine learning methods. Among them, Convolutional Neural Networks (CNNs) have received substantial attention due to their…
Geomagnetic disturbance forecasting is based on the identification of solar wind structures and accurate determination of their magnetic field orientation. For nowcasting activities, this is currently a tedious and manual process. Focusing…
Magnetic fields are crucial in numerous astrophysical processes within the interstellar medium. However, the detailed determination of magnetic field geometry is notoriously challenging. Based on the modern magnetohydrodynamic (MHD)…
Context. The polarized thermal emission from interstellar dust offers a valuable tool for probing both the dust and the magnetic field in the interstellar medium (ISM). However, existing observations only yield the total amount of dust…
Absolute knowledge about the magnetic field orientation plays a crucial role in single spin-based quantum magnetometry and the application toward spin-based quantum computation. In this paper, we reconstruct the 3D orientation of an…
This paper proposes to learn analysis transform network for dynamic magnetic resonance imaging (LANTERN) with small dataset. Integrating the strength of CS-MRI and deep learning, the proposed framework is highlighted in three components:…
With the increasing popularity of deep learning, Convolutional Neural Networks (CNNs) have been widely applied in various domains, such as image classification and object detection, and achieve stunning success in terms of their high…
Deep learning models have provided huge interpretation power for image-like data. Specifically, convolutional neural networks (CNNs) have demonstrated incredible acuity for tasks such as feature extraction or parameter estimation. Here we…
The self-consistent field (SCF) generation of the three-dimensional (3D) electron density distribution ($\rho$) represents a fundamental aspect of density functional theory (DFT) and related first-principles calculations, and how one can…
Recently, many deep-learning-based pan-sharpening methods have been proposed for generating high-quality pan-sharpened (PS) satellite images. These methods focused on various types of convolutional neural network (CNN) structures, which…
While deep convolutional neural networks (CNN) have been successfully applied for 2D image analysis, it is still challenging to apply them to 3D anisotropic volumes, especially when the within-slice resolution is much higher than the…
I investigate the effect of non-uniform magnetic fields in the extended structures of radio galaxies on the observed synchrotron and inverse-Compton emission. On the assumption of an isotropic field, with a given power spectrum and a…
There is an increasing interest in applying deep learning to 3D mesh segmentation. We observe that 1) existing feature-based techniques are often slow or sensitive to feature resizing, 2) there are minimal comparative studies and 3)…
We derive expressions for the total and linearly-polarized synchrotron emissivity of an element of plasma containing relativistic particles and disordered magnetic field which has been sheared or compressed along three independent…
Object viewpoint estimation from 2D images is an essential task in computer vision. However, two issues hinder its progress: scarcity of training data with viewpoint annotations, and a lack of powerful features. Inspired by the growing…