相关论文: Probing Three-Dimensional Magnetic Fields: III -- …
We propose a new machine learning approach to Stokes inversion based on a convolutional neural network (CNN) and the Milne-Eddington (ME) method. The Stokes measurements used in this study were taken by the Near InfraRed Imaging…
Due to its capability to identify erroneous disparity assignments in dense stereo matching, confidence estimation is beneficial for a wide range of applications, e.g. autonomous driving, which needs a high degree of confidence as mandatory…
3D-MHD numerical simulations of bipolar, hypersonic, weakly magnetized jets and synthetic synchrotron observations are presented to study the structure and evolution of magnetic fields in FR II radio sources. The magnetic field setup in the…
Calculating the effects of Coherent Synchrotron Radiation (CSR) is one of the most computationally expensive tasks in accelerator physics. Here, we use convolutional neural networks (CNN's), along with a latent conditional diffusion (LCD)…
Machine learning techniques are being increasingly applied in medical and physical sciences across a variety of imaging modalities; however, an important issue when developing these tools is the availability of good quality training data.…
Cylindrical magnetic nanowires with large transversal magnetocrystalline anisotropy have been shown to sustain non-trivial magnetic configurations resulting from the interplay of spatial confinement, exchange, and anisotropies. Exploiting…
Magnetic turbulence is anisotropic as the directions of motion are constrained by the magnetic field. Such anisotropy can be observed in velocity centroids obtained from spectroscopic observations. We use magnetohydrodynamics (MHD)…
The increasing need to store large amounts of information with an ultra-dense, reliable, low power and low cost memory device is driving aggressive efforts to improve upon current perpendicular magnetic recording technology. However, the…
We use a set of magnetohydrodynamics (MHD) simulations of fully-developed (driven) turbulence to study the anisotropy in the velocity field that is induced by the presence of the magnetic field. In our models we study turbulence…
The interstellar turbulence is magnetized and thus anisotropic. The anisotropy of turbulent magnetic fields and velocities is imprinted in the related observables, rotation measures (RMs), and velocity centroids (VCs). This anisotropy…
We present for the first time a coherent model of the polarized Galactic synchrotron and thermal dust emissions which are the main diffuse foreground for the measurement of the polarized power spectra of the CMB fluctuations with the Planck…
In this paper we use the diffuse Galactic synchrotron emission to study the properties of the Galactic magnetic field. We combined data from the low-frequency WENSS survey with single-dish observations carried out by Brouw and Spoelstra to…
We present our attempts to detect magnetic fields in filamentary large-scale structure (LSS) by observing polarized synchrotron emission emitted by structure formation shocks. Little is known about the strength and order of magnetic fields…
Probing magnetic fields in the interstellar medium (ISM) is notoriously challenging. Motivated by the modern theories of magnetohydrodynamic (MHD) turbulence and turbulence anisotropy, we introduce the Structure-Function Analysis (SFA) as a…
Synchrotron emission, its polarization and its Faraday rotation at radio frequencies of 0.2-10 GHz are powerful tools to study the strength and structure of cosmic magnetic fields. The observational results are reviewed for spiral, barred…
This paper presents an innovative deep learning pipeline which estimates the relative pose of a spacecraft by incorporating the temporal information from a rendezvous sequence. It leverages the performance of long short-term memory (LSTM)…
Observational studies of magnetic fields are vital as magnetic fields play a crucial role in various astrophysical processes, including star formation, accretion of matter, transport processes (e.g., transport of heat), and cosmic rays. We…
A computational framework that leverages data from self-consistent field theory simulations with deep learning to accelerate the exploration of parameter space for block copolymers is presented. This is a substantial two-dimensional…
Despite recent observational and theoretical advances in mapping the magnetic fields associated with molecular clouds, their three-dimensional (3D) morphology remains unresolved. Multi-wavelength and multi-scale observations will allow us…
We analyse observational signatures of magnetic fields for simulations of a Milky-Way like disc with supernova-driven interstellar turbulence and self-consistent chemical processes. In particular, we post-process two simulations data sets…