Related papers: A morphological segmentation approach to determini…
We examine the observable properties of simulated barred galaxies including radial mass profiles, edge-on structure and kinematics, bar lengths and pattern speed evolution for detailed comparison to real systems. We have run several…
We propose a method for extracting very accurate masks of hands in egocentric views. Our method is based on a novel Deep Learning architecture: In contrast with current Deep Learning methods, we do not use upscaling layers applied to a…
Segmentation of distinct bones plays a crucial role in diagnosis, planning, navigation, and the assessment of bone metastasis. It supplies semantic knowledge to visualisation tools for the planning of surgical interventions and the…
The paper is a short review of medical image segmentation using U-Net and its variants. As we understand going through a medical images is not an easy job for any clinician either radiologist or pathologist. Analysing medical images is the…
Motion artifacts caused by prolonged acquisition time are a significant challenge in Magnetic Resonance Imaging (MRI), hindering accurate tissue segmentation. These artifacts appear as blurred images that mimic tissue-like appearances,…
We present a novel unsupervised learning approach to automatically segment and label images in astronomical surveys. Automation of this procedure will be essential as next-generation surveys enter the petabyte scale: data volumes will…
We identify stellar structures in the PHANGS sample of 74 nearby galaxies and construct morphological masks of sub-galactic environments based on Spitzer 3.6 micron images. At the simplest level, we distinguish centres, bars, spiral arms,…
Classification of galaxy morphology is a challenging but meaningful task for the enormous amount of data produced by the next-generation telescope. By introducing the adaptive polar coordinate transformation, we develop a rotationally…
High resolution N-body simulations using different codes and initial condition techniques reveal two different behaviours for the rotation frequency of transient spiral arms like structures. Whereas unbarred disks present spiral arms nearly…
In this study, we investigate the morphology of galaxies in the TNG100 simulation by applying mock observation techniques and compare the results with the observational data from the Sloan Digital Sky Survey (SDSS). By employing a…
Stellar bars are key drivers of secular evolution in galaxies and can be effectively studied using rest-frame near-infrared (NIR) images, which trace the underlying stellar mass and are less impacted by dust and star formation than…
We carry out a comparison study on the bar structure in the Illustris-1 and TNG100 simulations. At $z=0$, 8.9\% of 1232 disc galaxies with stellar mass $>10^{10.5}M_{\odot}$ in Illustris-1 are barred, while the numbers are 55\% of 1269 in…
This study numerically investigates the dynamics of barred spiral galaxies using 3D Ferrers bar response models. A total of 708 models were analyzed, incorporating variations in the axisymmetric potential (nucleus, bulge, disk, halo), bar…
In this article we investigate the morphology and stellar populations of high-redshift galaxies through multi-waveband HST imaging and ground-based spatially-resolved spectroscopy. We study the redshift evolution of galaxy morphology in the…
Stellar disk truncations, also referred to as galaxy edges, are key indicators of galactic size, determined by the radial location of the gas density threshold for star formation. Accurately measuring galaxy sizes for millions of galaxies…
Morphological classification is a key piece of information to define samples of galaxies aiming to study the large-scale structure of the universe. In essence, the challenge is to build up a robust methodology to perform a reliable…
I use volume- and mass-limited subsamples and recently published data from the Spitzer Survey of Stellar Structure in Galaxies (S4G) to investigate how the size of bars depends on galaxy properties. The known correlation between bar…
Astronomers have typically set out to solve supervised machine learning problems by creating their own representations from scratch. We show that deep learning models trained to answer every Galaxy Zoo DECaLS question learn meaningful…
Deep Learning (DL) algorithms for morphological classification of galaxies have proven very successful, mimicking (or even improving) visual classifications. However, these algorithms rely on large training samples of labelled galaxies…
We use 3.6 $\mu$m imaging from the S$^{4}$G survey to characterize the typical stellar density profiles ($\Sigma_{\ast}$) and bars as a function of fundamental galaxy parameters (e.g. the total stellar mass $M_{\ast}$), providing…