Related papers: Star Cluster Classification using Deep Transfer Le…
(Abbreviated) We present an imaging survey of 37 nearby galaxies observed with HST/WFPC2 in the mid-UV F300W filter and in F814W. 11 galaxies were also imaged in F255W. These galaxies were selected to be detectable with WFPC2 in one orbit,…
The robust and automated determination of crystal symmetry is of utmost importance in material characterization and analysis. Recent studies have shown that deep learning (DL) methods can effectively reveal the correlations between X-ray or…
We present a catalogue of photometric and structural properties of 228 nuclear star clusters (NSCs) in nearby late-type disk galaxies. These new measurements are derived from a homogeneous analysis of all suitable WFPC2 images in the $HST$…
Galaxy clusters are the most massive gravitationally bound structures in the Universe and key probes of cosmic evolution. The large data volume expected from upcoming surveys requires efficient automated analysis methods for tens of…
Background: Convolutional Neural Networks can be effectively used only when data are endowed with an intrinsic concept of neighbourhood in the input space, as is the case of pixels in images. We introduce here Ph-CNN, a novel deep learning…
We present 279 galaxy cluster candidates at $z > 1.3$ selected from the 94 deg$^{2}$ Spitzer South Pole Telescope Deep Field (SSDF) survey. We use a simple algorithm to select candidate high-redshift clusters of galaxies based on…
High redshift (z >~ 1) clusters are ideal probes to study the formation and evolution of large scale structures and galaxies in the universe. A 10-m class ground based telescope, X-ray observatories (Chandra, XMM-Newton) and HST/ACS are…
The analysis of star cluster ages in tandem with the morphology of their HII regions can provide insight into the processes that clear a cluster's natal gas, as well as the accuracy of cluster ages and dust reddening derived from Spectral…
Transfer learning for deep neural networks is the process of first training a base network on a source dataset, and then transferring the learned features (the network's weights) to a second network to be trained on a target dataset. This…
The studies of the evolution of galaxies in Galaxy Clusters have as a traditional complication the difficulty in establishing cluster membership of those sources detected in the field of view. The determination of spectroscopic redshifts…
Upcoming Fast Radio Burst (FRB) surveys will search $\sim$10\,$^3$ beams on sky with very high duty cycle, generating large numbers of single-pulse candidates. The abundance of false positives presents an intractable problem if candidates…
The taxonomy of galaxy morphology is critical in astrophysics as the morphological properties are powerful tracers of galaxy evolution. With the upcoming Large-scale Imaging Surveys, billions of galaxy images challenge astronomers to…
An important part of breast cancer staging is the assessment of the sentinel axillary node for early signs of tumor spreading. However, this assessment by pathologists is not always easy and retrospective surveys often requalify the status…
We present the first study of high-precision internal proper motions (PMs) in a large sample of globular clusters, based on Hubble Space Telescope (HST) data obtained over the past decade with the ACS/WFC, ACS/HRC, and WFC3/UVIS…
In this study, it is aimed to develop a deep learning application which detects types of garbage into trash in order to provide recyclability with vision system. Training and testing will be performed with image data consisting of several…
We present a new machine learning model for estimating photometric redshifts with improved accuracy for galaxies in Pan-STARRS1 data release 1. Depending on the estimation range of redshifts, this model based on neural networks can handle…
In image classification, visual separability between different object categories is highly uneven, and some categories are more difficult to distinguish than others. Such difficult categories demand more dedicated classifiers. However,…
Deep clustering which adopts deep neural networks to obtain optimal representations for clustering has been widely studied recently. In this paper, we propose a novel deep image clustering framework to learn a category-style latent…
Distinguishing active galaxies from star-forming galaxies is essential for understanding galaxy evolution. Diagnostic methods like the BPT (Baldwin, Phillips, and Terlevich) diagram use optical emission-line ratios to separate galaxies.…
Protoclusters are high-$z$ overdense regions that will evolve into clusters of galaxies by $z=0$, making them ideal for studying galaxy evolution expected to be accelerated by environmental effects. However, it has been challenging to…