Related papers: Protostellar classification using supervised machi…
In order to analyse the large numbers of Seyfert galaxy spectra available at present, we are testing new techniques to derive their physical parameters fastly and accurately. We present an experiment on such a new technique to segregate old…
Infrared Dark Clouds are ideal laboratories to study the initial processes of high-mass star and star cluster formation. We investigated star formation activity of an unexplored filamentary dark cloud (~5.7pc x 1.9pc), which itself is part…
We present fundamental parameters for 110 canonical K- & M-type (1.3$-$0.13$M_\odot$) Taurus-Auriga young stellar objects (YSOs). The analysis produces a simultaneous determination of effective temperature ($T_{\rm eff}$), surface gravity…
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…
We have undertaken a dedicated program of automatic source classification in the WISE database merged with SuperCOSMOS scans, comprehensively identifying galaxies, quasars and stars on most of the unconfused sky. We use the Support Vector…
We present a novel approach for classifying stars as binary or exoplanet using deep learning techniques. Our method utilizes feature extraction, wavelet transformation, and a neural network on the light curves of stars to achieve…
In this second paper in a series of papers based on the most-up-to-date catalogue of symbiotic stars (SySts), we present a new approach for identifying and distinguishing SySts from other Halpha emitters in photometric surveys using machine…
Nowadays, Machine Learning techniques offer fast and efficient solutions for classification problems that would require intensive computational resources via traditional methods. We examine the use of a supervised Random Forest to classify…
Information on the spectral types of stars is of great interest in view of the exploitation of space-based imaging surveys. In this article, we investigate the classification of stars into spectral types using only the shape of their…
In this work we focus on the determination of the relative distributions of young, intermediate-age and old populations of stars in galaxies. Starting from a grid of theoretical population synthesis models we constructed a set of model…
We report on a multi-wavelength (IR to cm) and multi-resolution (1 mas to 20 arcsec) exploration of high-mass star formation regions in the Galactic plane, at longitudes observable from the Southern Hemisphere. Our source sample was…
Young stellar objects (YSOs) may undergo periods of active accretion (outbursts), during which the protostellar accretion rate is temporarily enhanced by a few orders of magnitude. Whether or not these accretion outburst YSOs possess…
Machine Learning is an efficient method for analyzing and interpreting the increasing amount of astronomical data that is available. In this study, we show, a pedagogical approach that should benefit anyone willing to experiment with Deep…
The properties of unresolved protostars and their local environment are frequently inferred from spectral energy distributions (SEDs) using radiative transfer modeling. We perform synthetic observations of realistic star formation…
Edge-on galaxies have many important applications in galactic astrophysics, but they can be difficult to identify in vast amounts of astronomical data. To facilitate the search for them, we have developed a deep learning algorithm designed…
The increased computerization in recent years has resulted in the production of a variety of different software, however measures need to be taken to ensure that the produced software isn't defective. Many researchers have worked in this…
Deep learning, particularly Convolutional Neural Networks (CNNs), has gained significant attention for its effectiveness in computer vision, especially in agricultural tasks. Recent advancements in instance segmentation have improved image…
This thesis presents an innovative framework for the automated detection and characterization of galactic bars, pivotal structures in spiral galaxies, using the YOLO-OBB (You Only Look Once with Oriented Bounding Boxes) model. Traditional…
We apply Support Vector Machines -- a machine learning algorithm -- to the task of classifying structures in the Interstellar Medium. As a case study, we present a position-position velocity data cube of 12 CO J=3--2 emission towards…
We present the results of a proof-of-concept experiment which demonstrates that deep learning can successfully be used for production-scale classification of compact star clusters detected in HST UV-optical imaging of nearby spiral galaxies…