Related papers: Cosmic Web Classification through Stochastic Topol…
We present a new non-parametric method to quantify morphologies of galaxies based on a particular family of learning machines called support vector machines. The method, that can be seen as a generalization of the classical CAS…
Context: We present COSTA (COld STream finder Algorithm), a novel algorithm to search for cold kinematical substructures in the phase space of planetary nebulae (PNe) and globular clusters (GCs) in the halo of massive galaxies and…
We introduce a new method to determine galaxy cluster membership based solely on photometric properties. We adopt a machine learning approach to recover a cluster membership probability from galaxy photometric parameters and finally derive…
Hierarchical clustering is a common algorithm in data analysis. It is unique among many clustering algorithms in that it draws dendrograms based on the distance of data under a certain metric, and group them. It is widely used in all areas…
Yearslong time series of high-precision brightness measurements have been assembled for thousands of stars with telescopes operating in space. Such data have allowed astronomers to measure the physics of stellar interiors via nonradial…
We apply the Alcock-Paczynski (AP) test to the stacked voids identified using the large-scale structure galaxy catalog from the Baryon Oscillation Spectroscopic Survey (BOSS). This galaxy catalog is part of the Sloan Digital Sky Survey…
Context: We present the first Cosmological Parameter inferences from eROSITA X-ray observations of galaxy clusters using a Machine Learning algorithm. Methods: We train a Random Forest using mock catalogs of clusters from Magneticum…
Filaments are crucial components of the cosmic web, representing the extensive and aligned distributions of galaxies and gas. Using the Five-hundred-meter Aperture Spherical radio Telescope (FAST), we report the detection of a filament in…
We look for correlated changes in stellar mass and star formation rate along filaments in the cosmic web by examining the stellar masses and UV-derived star formation rates (SFR) of 1,799 ungrouped and unpaired spiral galaxies that reside…
Galaxies and their dark matter halos populate a complicated filamentary network around large, nearly empty regions known as cosmic voids. Cosmic voids are usually identified in spectroscopic galaxy surveys, where 3D information about the…
Galaxies are not distributed randomly throughout space but are instead arranged in an intricate "cosmic web" of filaments and walls surrounding bubble-like voids. There is still no compelling observational evidence of a link between the…
Most existing star-galaxy classifiers depend on the reduced information from catalogs, necessitating careful data processing and feature extraction. In this study, we employ a supervised machine learning method (GoogLeNet) to automatically…
We apply and compare various Artificial Neural Network (ANN) and other algorithms for automatic morphological classification of galaxies. The ANNs are presented here mathematically, as non-linear extensions of conventional statistical…
While cosmic voids are now recognized as a valuable cosmological probe, identifying them in a galaxy catalog is challenging for multiple reasons: observational effects such as holes in the mask or magnitude selection hinder the detection…
Galaxy filaments are a peculiar environment, and their impact on the galaxy properties is still controversial. Exploiting the data from the GAs Stripping Phenomena in galaxies with MUSE (GASP), we provide the first characterisation of the…
Large-scale gaseous filaments with length up to the order of 100 pc are on the upper end of the filamentary hierarchy of the Galactic interstellar medium. Their association with respect to the Galactic structure and their role in Galactic…
Components of large-scale structure (LSS) of Universe includes galaxy clusters, walls, filaments, groups and field galaxies. The question of spatial organization of all these components remains open despite of a number of recently developed…
The stochastic order redshift technique (SORT) is a simple, efficient, and robust method to improve cosmological redshift measurements. The method relies upon having a small ($\sim$10 per cent) reference sample of high-quality redshifts.…
We review the analysis of the Cosmic Web by means of an extensive toolset based on the use of Delaunay and Voronoi tessellations. The Cosmic Web is the salient and pervasive foamlike pattern in which matter has organized itself on scales of…
I present here a review of past and present multi-disciplinary research of the Pittsburgh Computational AstroStatistics (PiCA) group. This group is dedicated to developing fast and efficient statistical algorithms for analysing huge…