Related papers: Dusty stellar sources classification by implementi…

The Magellanic Clouds (MCs) are excellent locations to study stellar dust emission and its contribution to galaxy evolution. Through spectral and photometric classification, MCs can serve as a unique environment for studying stellar…

We proposed a machine learning approach to identify and distinguish dusty stellar sources employing supervised and unsupervised methods and categorizing point sources, mainly evolved stars, using photometric and spectroscopic data collected…

Dusty stellar sources, including young stellar objects (YSOs) and evolved stars such as oxygen- and carbon-rich AGBs (OAGBs, CAGBs), red supergiants (RSGs), and post-AGB stars (PAGBs), play a key role in the chemical enrichment of galaxies.…

The Magellanic clouds are uniquely placed to study the stellar contribution to dust emission. Individual stars can be resolved in these systems even in the mid-infrared, and they are close enough to allow detection of infrared excess caused…

The Infrared Spectrograph (IRS) on the {\em Spitzer Space Telescope} observed nearly 800 point sources in the Large Magellanic Cloud (LMC), taking over 1,000 spectra. 197 of these targets were observed as part of the Sage-Spec Spitzer…

Differences in metallicity between the Large Magellanic Cloud (LMC) and the Small Magellanic Cloud (SMC) offer an opportunity to examine whether environmental metallicity affects the performance of machine learning models in classifying…

We present the classification of 197 point sources observed with the Infrared Spectrograph in the SAGE-Spec Legacy program on the Spitzer Space Telescope. We introduce a decision-tree method of object classification based on infrared…

Context: We present a newly discovered class of low-luminosity, dusty, evolved objects in the Magellanic Clouds. These objects have dust excesses, stellar parameters, and spectral energy distributions similar to those of dusty…

Classification of young stellar objects (YSOs) into different evolutionary stages helps us to understand the formation process of new stars and planetary systems. Such classification has traditionally been based on spectral energy…

Using observations from the {\em Herschel} Inventory of The Agents of Galaxy Evolution (HERITAGE) survey of the Magellanic Clouds, we have found thirty five evolved stars and stellar end products that are bright in the far-infrared. These…

Mass loss is a key aspect of stellar evolution, particularly in evolved massive stars, yet episodic mass loss remains poorly understood. To investigate this, we need evolved massive stellar populations across various galactic environments.…

Mass loss is a key property to understand stellar evolution and in particular for low-metallicity environments. Our knowledge has improved dramatically over the last decades both for single and binary evolutionary models. However, episodic…

We are performing a uniform and unbiased, ~7x7 degrees imaging survey of the Large Magellanic Cloud (LMC), using the IRAC and MIPS instruments on board the Spitzer Space Telescope in order to survey the agents of a galaxy's evolution…

This paper explores the application of machine learning methods for classifying astronomical sources using photometric data, including normal and emission line galaxies (ELGs; starforming, starburst, AGN, broad line), quasars, and stars. We…

We present initial results and source lists of variable sources in the Large Magellanic Cloud (LMC) for which we detect thermal infrared variability from the SAGE (Surveying the Agents of a Galaxy's Evolution) survey, which had 2 epochs of…

The ASSESS project aims to determine the role of episodic mass-loss in the evolution of massive stars. As a first step, we construct a catalog of spectroscopically identified dusty, evolved massive stars in ten southern galaxies for which…

Increasing the statistics of spectroscopically confirmed evolved massive stars in the Local Group enables the investigation of the mass loss phenomena that occur in these stars in the late stages of their evolution. We aim to complete the…

We present a supervised machine learning classification of stellar populations in the Local Group spiral galaxy M\,33. The Probabilistic Random Forest (PRF) methodology, previously applied to populations in NGC\,6822, utilises both near and…

We used a supervised machine learning algorithm (probabilistic random forest) to classify ~130 million sources in the VISTA Survey of the Magellanic Clouds (VMC). We used multi-wavelength photometry from optical to far-infrared as features…

(Abridged) Photometry of archival Spitzer observations of the Large Magellanic Cloud (LMC) are used to search for young stellar objects (YSOs). Simple mid-infrared selection criteria were used to exclude most normal and evolved stars and…