Related papers: Protostellar classification using supervised machi…

Characterising stellar and circumstellar properties of embedded young stellar objects (YSOs) is mandatory for understanding the early stages of the stellar evolution. This task requires the combination of both spectroscopy and photometry,…

(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…

Context. The classification of young stellar objects (YSOs) is typically done using the infrared spectral slope or bolometric temperature, but either can result in contamination of samples. More accurate methods to determine the…

We explored the AllWISE catalogue of the Wide-field Infrared Survey Explorer mission and identified Young Stellar Object candidates. Reliable 2MASS and WISE photometric data combined with Planck dust opacity values were used to build our…

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…

Research on young stellar populations is essential to understand the properties of embedded clusters and advance theories of their formation. This has driven advancements in methodologies for star detection, leading to the development of…

We present a spectroscopic follow-up of photometrically-selected young stellar object (YSO) candidates in the Central Molecular Zone of the Galactic center. Our goal is to quantify the contamination of this YSO sample by reddened giant…

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…

Identifying stars belonging to different classes is vital in order to build up statistical samples of different phases and pathways of stellar evolution. In the era of surveys covering billions of stars, an automated method of identifying…

A naive Bayes classifier for identifying Class II YSOs has been constructed and applied to a region of the Northern Galactic Plane containing 8 million sources with good quality Gaia EDR3 parallaxes. The classifier uses the five features:…

We performed a large-scale spectral energy distribution (SED) fitting analysis for young stellar objects (YSOs) in the Orion star formation complex (OSFC) to derive key physical parameters; temperature, luminosity, mass, and age, using SED…

Machine learning allows efficient extraction of physical properties from stellar spectra that have been obtained by large surveys. The viability of ML approaches has been demonstrated for spectra covering a variety of wavelengths and…

We present a grid of radiation transfer models of axisymmetric young stellar objects (YSOs), covering a wide range of stellar masses (from 0.1Msun to 50Msun) and evolutionary stages (from the early envelope infall stage to the late…

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 review the use of young low mass stars and protostars, or young stellar objects (YSOs), as tracers of star formation. Observations of molecular clouds at visible, infrared, radio and X-ray wavelengths can identify and characterize the…

We analyze the spatial distribution of dusty young stellar objects (YSOs) identified in the Spitzer Survey of the Orion Molecular clouds, augmenting these data with Chandra X-ray observations to correct for incompleteness in dense clustered…

We apply machine learning techniques in an attempt to predict and classify stellar properties from noisy and sparse time series data. We preprocessed over 94 GB of Kepler light curves from MAST to classify according to ten distinct physical…

We investigate the distribution of different classes of spectroscopically identified sources and theoretical models in the color-color diagrams (CCDs) combining the near-infrared (NIR) and mid-infrared (MIR) data to develop a method to…

We present results from our spectroscopic study, using the Infrared Spectrograph (IRS) onboard the Spitzer Space Telescope, designed to identify massive young stellar objects (YSOs) in the Galactic Center (GC). Our sample of 107 YSO…

The immense amount of time series data produced by astronomical surveys has called for the use of machine learning algorithms to discover and classify several million celestial sources. In the case of variable stars, supervised learning…