Related papers: Estimating the mass of galactic components using m…
Multi-band images of galaxies reveal a huge amount of information about their morphology and structure. However, inferring properties of the underlying stellar populations such as age, metallicity or kinematics from those images is…
Black hole mass is a fundamental property of active galactic nuclei (AGNs). In the distant universe, black hole mass is commonly estimated using the MgII, Hbeta, or Halpha emission line widths and the optical/UV continuum or line…
Weak gravitational lensing depends on the integrated mass along the line of sight. Baryons contribute to the mass distribution of galaxy clusters and the resulting mass estimates from lensing analysis. We use the cosmo-OWLS suite of…
A galaxy's morphological features encode details about its gas content, star formation history, and feedback processes, which play important roles in regulating its growth and evolution. We use deep convolutional neural networks (CNNs) to…
Ongoing and future spectroscopic surveys will measure numerous galaxy redshifts within tens of thousands of galaxy clusters. However, the sampling within these clusters will be low, 15 < N < 50 per cluster. With such data, it will be…
Wide-field cosmological surveys provide hundreds of thousands of spectroscopically confirmed galaxy groups and clusters, valuable for tracing baryonic matter distribution. However, controlling systematics in identifying host dark matter…
We present results exploring the role that probabilistic deep learning models can play in cosmology from large scale astronomical surveys through estimating the distances to galaxies (redshifts) from photometry. Due to the massive scale of…
Aims. We present the application of a fully connected neural network (NN) for galaxy merger identification using exclusively photometric information. Our purpose is not only to test the method's efficiency, but also to understand what…
In this work, we compute rates of merging neutron stars (MNS) in galaxies of different morphological type, as well as the cosmic MNS rate in a unitary volume of the Universe adopting different cosmological scenarios. Our aim is to provide…
We present a novel approach for reconstructing the projected mass distribution of clusters of galaxies from sparse and noisy weak gravitational lensing shear data. The reconstructions are regularised using knowledge gained from numerical…
The details of bulge formation via collapse, mergers, secular processes or their interplay remain unresolved. To start answering this question and quantify the importance of distinct mechanisms, we mapped a sample of three galactic bulges…
We present a novel machine learning method for predicting the baryonic properties of dark matter only subhalos from N-body simulations. Our model is built using the extremely randomized tree (ERT) algorithm and takes subhalo properties over…
We exploit the great potential offered by Bayesian Neural Networks (BNNs) to directly decipher the internal composition of neutron stars (NSs) based on their macroscopic properties. By analyzing a set of simulated observations, namely NS…
AIMS. To derive the mass profiles of the different luminous and dark components in clusters. METHODS. The cluster mass profile is determined by using the Jeans equation applied to the projected phase-space distribution of about 3000…
Joint lensing and dynamical mass profile determinations of galaxy clusters are an excellent tool to constrain modification of gravity at cosmological scales. However, search for tiny departures from General Relativity calls for an accurate…
Understanding the star-formation properties of galaxies as a function of cosmic epoch is a critical exercise in studies of galaxy evolution. Traditionally, stellar population synthesis models have been used to obtain best fit parameters…
Accurate observational data on the rotation curve of the Milky Way galaxy (MW) are very well understood using the gravitational potentials of the baryonic matter (Pouliasis et al., 2017) and the interpolating function presented by McGaugh…
The study of strong-lensing systems conventionally involves constructing a mass distribution that can reproduce the observed multiply-imaging properties. Such mass reconstructions are generically non-unique. Here, we present an alternative…
Here, we present a machine vision approach, combining a VAE framework with PCA, to decipher galaxy images. Using mock gri-band images from the EAGLE simulation, the VAE finds that around 35 features are needed to describe the images. Adding…
We present multi-component decomposition of high-quality $R$-band images of 312 disk galaxies from the Carnegie-Irvine Galaxy Survey. In addition to bulges and disks, we successfully model nuclei, bars, disk breaks, nuclear/inner lenses,…