Related papers: Machine Learning Approach for Estimating Magnetic …
We present a numerical approach to investigate the relationship between magnetic fields and Faraday rotation effects in clusters of galaxies. We can infer the structure and strength of intra-cluster magnetic fields by comparing our…
We present a series of cosmological magnetohydrodynamic (MHD) simulations that simultaneously follow the formation of a galaxy cluster and evolution of magnetic fields ejected by an Active Galactic Nucleus (AGN). Specifically, we…
Determination of the magnetic field strength in the interstellar medium is one of the most complex tasks of contemporary astrophysics. We can only estimate the order of magnitude of the magnetic field strength by using a few very limited…
The next generation of data-intensive surveys are bound to produce a vast amount of data, which can be dealt with using machine-learning methods to explore possible correlations within the multi-dimensional parameter space. We explore the…
State-of-the-art radio observatories produce large amounts of data which can be used to study the properties of radio galaxies. However, with this rapid increase in data volume, it has become unrealistic to manually process all of the…
Ram pressure (RP) can influence the evolution of cold gas content and star formation rates of galaxies. One of the key parameters for the strength of RP is the density of intra-group medium ($\rho_{\rm igm}$), which is difficult to estimate…
We investigate the possibility for the SKA to detect and study the magnetic fields in galaxy clusters and in the less dense environments surrounding them using Faraday Rotation Measures. To this end, we produce 3-dimensional magnetic field…
Conventional galaxy mass estimation methods suffer from model assumptions and degeneracies. Machine learning, which reduces the reliance on such assumptions, can be used to determine how well present-day observations can yield predictions…
Strong accretion shocks are expected to illuminate the warm-hot inter-galactic medium encompassed by the filaments of the cosmic web, through synchrotron radio emission. Given their high sensitivity, low-frequency large radio facilities may…
We present a new method by which the total masses of galaxies including dark matter can be estimated from the kinematics of their globular cluster systems (GCSs). In the proposed method, we apply the convolutional neural networks (CNNs) to…
Magnetic fields play a fundamental role in protostellar collapse and disk formation, yet direct measurements of magnetic field strength in deeply embedded protostellar envelopes remain difficult. We present a new method to estimate both the…
Magnetic fields have been included in cosmological simulations of galaxy formation only recently, due to advances in numerical techniques and a better understanding of the galaxy formation physics. In this paper, we develop a new subgrid…
Using relativistic mean-field models, the formation of clusterized matter, as the one expected to exist in the inner crust of neutron stars, is determined under the effect of strong magnetic fields. As already predicted from a calculation…
Observations of Faraday rotation and synchrotron emission in galaxy clusters imply large-scale magnetic fields with $\mu\mathrm{G}$ strengths possibly extending back to $z=4$. Non-radiative cosmological simulations of galaxy clusters show a…
We present a convolutional neural network to classify distinct cosmological scenarios based on the statistically similar weak-lensing maps they generate. Modified gravity (MG) models that include massive neutrinos can mimic the standard…
Radio observations of galaxy clusters show that there are $\mu$G magnetic fields permeating the intra-cluster medium (ICM), but it is hard to accurately constrain the strength and structure of the magnetic fields without the help of…
Magnetic fields have been observed in galaxy clusters with strengths of the order of $\sim \mu$G. The non-thermal pressure exerted by magnetic fields also contributes to the total pressure in galaxy clusters and can in turn affect the…
We present an automated approach for identifying magnetospheric regions using supervised machine learning techniques applied to Magnetospheric MultiScale mission data. Our method utilizes ion energy spectra, total magnetic field, total ion…
Galaxy clusters are unique laboratories to investigate turbulent fluid motions and large scale magnetic fields. Synchrotron radio halos at the center of merging galaxy clusters provide the most spectacular and direct evidence of the…
Machine Learning (ML) algorithms are becoming popular in cosmology for extracting valuable information from cosmological data. In this paper, we evaluate the performance of a Convolutional Neural Network (CNN) trained on matter density…