Related papers: Machine Learning Approach for Estimating Magnetic …
This paper studies how to employ synchrotron emission gradient techniques to reveal the properties of the magnetic field within the interstellar media. Based on data cubes of three-dimensional numerical simulations of magnetohydrodynamic…
Complex spin textures in itinerant electron magnets hold promises for next-generation memory and information technology. The long-ranged and often frustrated electron-mediated spin interactions in these materials give rise to intriguing…
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)…
Chiral magnets have attracted a large amount of research interest in recent years because they support a variety of topological defects, such as skyrmions and bimerons, and allow for their observation and manipulation through several…
Cosmological simulations of magnetic fields in galaxy clusters show that remarkable agreement between simulations and observations of Faraday rotation and radio haloes can be achieved assuming that seed fields of 10^{-9} G were present at…
We propose a mechanism for the early generation of the mean Intracluster magnetic field in terms of magnetized galactic winds. These winds are the result of starburst phases of the cluster galaxies, assumed to produce the predominant…
Radio synchrotron emission, its polarization and its Faraday rotation are powerful tools to study the strength and structure of interstellar magnetic fields. The total intensity traces the strength and distribution of total magnetic fields.…
A fully three-dimensional (3D) magnetohydrodynamical (MHD) model is applied to simulate the evolution of the large-scale magnetic field in cluster galaxies interacting with the intra-cluster medium (ICM). As the model input we use a time…
Gravity inversion is the problem of estimating subsurface density distributions from observed gravitational field data. We consider the two-dimensional (2D) case, in which recovering density models from one-dimensional (1D) measurements…
The hot intracluster plasma in clusters of galaxies is weakly magnetized. Mergers between clusters produce gas compression and motions which can increase the magnetic field strength. In this work, we perform high-resolution non-radiative…
We perform cosmological, hydrodynamic simulations of magnetic fields in galaxy clusters. The computational code combines the special-purpose hardware Grape for calculating gravitational interaction, and smooth-particle hydrodynamics for the…
We present the review of various methods of detection of magnetic field strengths in the nearest regions of the active galaxy nuclei (AGN) which are the high energetic machines. Original spectropolarimetric method developed in the Pulkovo…
We perform cosmological, hydrodynamic simulations of magnetic fields in galaxy clusters. The computational code combines the special-purpose hardware Grape for calculating gravitational interaction, and smooth-particle hydrodynamics for the…
Large-scale coherent magnetic fields in the intergalactic medium are presumed to play a key role in the formation and evolution of the cosmic web, and in large scale feedback mechanisms. However, they are theorized to be extremely weak, in…
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…
Magnetic fields, together with cosmic rays (CRs), play an important role in the dynamics and evolution of galaxies, but are difficult to estimate. Energy equipartition between magnetic fields and CRs provides a convenient way to approximate…
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…
Machine learning (ML) is becoming a critical tool for interrogation of large complex data. Labeling, defined as the process of adding meaningful annotations, is a crucial step of supervised ML. However, labeling datasets is time consuming.…
We train deep learning models on thousands of galaxy catalogues from the state-of-the-art hydrodynamic simulations of the CAMELS project to perform regression and inference. We employ Graph Neural Networks (GNNs), architectures designed to…
The detection of Cosmic Microwave Background primordial $B$-mode polarization would constitute a ``smoking gun" signal of primordial gravitational waves. However, this measurement requires accurate removal of polarized Galactic foregrounds…