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Unsupervised landmarks discovery (ULD) for an object category is a challenging computer vision problem. In pursuit of developing a robust ULD framework, we explore the potential of a recent paradigm of self-supervised learning algorithms,…
The recent discovery of the most extended ultra-diffuse galaxy (UDG), Nube, has raised yet another question about the validity of the cold dark matter (CDM) model. The studies using cosmological and zoom-in simulations, which assume CDM,…
Data mining techniques must be developed and applied to analyse the large public data bases containing hundreds to thousands of millions entries. The aim of this study is to develop methods for locating previously unknown stellar clusters…
Searching for extraterrestrial, transient signals in astronomical data sets is an active area of current research. However, machine learning techniques are lacking in the literature concerning single-pulse detection. This paper presents a…
In this work, we explore the possibility of using probabilistic learning to identify pulsar candidates. We make use of Deep Gaussian Process (DGP) and Deep Kernel Learning (DKL). Trained on a balanced training set in order to avoid the…
Systematic studies have revealed hundreds of ultra-compact dwarf galaxies (UCDs) in the nearby Universe. With half-light radii $r_h$ of approximately 10-100 parsecs and stellar masses $M_*$ $\approx$ $10^6-10^8$ solar masses, UCDs are among…
We present the unexpected discovery of four ultra diffuse galaxies (UDGs) in a group environment. We recently identified seven extremely low surface brightness galaxies in the vicinity of the spiral galaxy M101, using data from the…
In this work, we present a novel approach to system identification for dynamical systems, based on a specific class of Deep Gaussian Processes (Deep GPs). These models are constructed by interconnecting linear dynamic GPs (equivalent to…
We demonstrate the power of Gibbs point process models from the spatial statistics literature when applied to studies of resolved galaxies. We conduct a rigorous analysis of the spatial distributions of objects in the star formation…
Ultra-diffuse galaxies (UDGs) are objects which have very extended morphology and faint central surface brightness. Most UDGs are discovered in galaxy clusters and groups, but also some are found in low density environments. The diffuse…
We present a novel computational approach for extracting weak signals, whose exact location and width may be unknown, from complex background distributions with an arbitrary functional form. We focus on datasets that can be naturally…
Gaussian processes (GPs) are pervasive in functional data analysis, machine learning, and spatial statistics for modeling complex dependencies. Modern scientific data sets are typically heterogeneous and often contain multiple known…
Many catalogues of isolated compact groups of galaxies (CGs) have been extracted using Hickson's criteria to identify isolated, dense systems of galaxies, with at least three or four galaxies concordant in magnitude and redshift. But is not…
We consider the problem of classification of points sampled from an unknown probability measure on a Euclidean space. We study the question of querying the class label at a very small number of judiciously chosen points so as to be able to…
We present a detailed analysis of two-band HST/ACS imaging of 21 ultra-compact dwarf (UCD) galaxies in the Virgo and Fornax Clusters. The aim of this work is to test two formation hypotheses for UCDs--whether they are bright globular…
In order to obtain morphological information of unlabeled galaxies, we present an unsupervised machine-learning (UML) method for morphological classification of galaxies, which can be summarized as two aspects: (1) the methodology of…
Context: The huge and still rapidly growing amount of galaxies in modern sky surveys raises the need of an automated and objective classification method. Unsupervised learning algorithms are of particular interest, since they discover…
We propose an effective and robust algorithm for identifying partial differential equations (PDEs) with space-time varying coefficients from a single trajectory of noisy observations. Identifying unknown differential equations from noisy…
The Euclid Space Telescope will provide deep imaging at optical and near-infrared wavelengths, along with slitless near-infrared spectroscopy, across ~15,000 sq deg of the sky. Euclid is expected to detect ~12 billion astronomical sources,…
The name "Ultra-Compact Dwarf Galaxy" (UCD) was invented for a new type of astronomical object that has been discovered in cores of nearby galaxy clusters a decade ago. UCDs resemble globular clusters, but are up to 100 times more massive…