Related papers: Machine Learning Classification of Young Stellar O…

Dusty stellar point sources are a significant stage in stellar evolution and contribute to the metal enrichment of galaxies. These objects can be classified using photometric and spectroscopic observations with color-magnitude diagrams…

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.…

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 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…

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…

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…

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…

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…

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…

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 present a supervised machine learning methodology to classify stellar populations in the Local Group dwarf-irregular galaxy NGC6822. Near-IR colours (J-H, H-K, and J-K), K-band magnitudes and far-IR surface brightness (at 70 and 160…

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.…

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…

We have carried out a search for optically visible post-AGB candidates in the Small Magellanic Cloud (SMC). We used mid-IR observations from the Spitzer Space Telescope to select optically visible candidates with a mid-IR excess. We…

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 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…

Among the first observations released to the public from the James Webb Space Telescope (JWST) was a section of the star-forming region NGC 3324 known colloquially as the "Cosmic Cliffs." We build a photometric catalog of the region and…

Stellar ages are critical for understanding the temporal evolution of a galaxy. We calculate the ages of over 6000 red giant branch stars in the Large Magellanic Cloud (LMC) observed with SDSS-IV / APOGEE-S. Ages are derived using…