Related papers: Protostellar classification using supervised machi…

In contrast to most other galaxies, star-formation rates in the Milky Way can be estimated directly from Young Stellar Objects (YSOs). In the Central Molecular Zone (CMZ) the star-formation rate calculated from the number of YSOs with 24…

Machine Learning algorithms are good tools for both classification and prediction purposes. These algorithms can further be used for scientific discoveries from the enormous data being collected in our era. We present ways of discovering…

We present a census of the population of deeply embedded young stellar objects (YSOs) in the Perseus molecular cloud complex based on a combination of Spitzer Space Telescope mid-IR data from the c2d legacy team and JCMT/SCUBA submillimeter…

Wide-field searches for young stellar objects (YSOs) can place useful constraints on the prevalence of clustered versus distributed star formation. The Spitzer/IRAC Candidate YSO (SPICY) catalog is one of the largest compilations of such…

Traditionally source identification is solved using threshold based energy detection algorithms. These algorithms frequently sum up the activity in regions, and consider regions above a specific activity threshold to be sources. While these…

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 demonstrate the use of the AKARI survey photometric data in the study of galactic star formation. Our aim was to select young stellar objects (YSOs) in the AKARI FIS catalogue. We used AKARI Far-Infrared Surveyor and Wide-field Infrared…

We have extended and refined the existing young stellar object (YSO) catalogs for the Orion A molecular cloud, the closest massive star-forming region to Earth. This updated catalog is driven by the large spatial coverage (18.3 deg$^2$,…

We use various analytical techniques to study Young Stellar Objects (YSOs) in an area of approximately $10' \times 10'$ in the IRAS 18456-0223 star-forming region. We use archival optical (Gaia DR3) and infrared (2MASS, UKIDSS, Spitzer,…

In the preparation for ESA's Euclid mission and the large amount of data it will produce, we train deep convolutional neural networks on Euclid simulations classify solar system objects from other astronomical sources. Using transfer…

We demonstrate the unique capabilities of Herschel to study very young luminous extragalactic young stellar objects (YSOs) by analyzing a central strip of the Large Magellanic Cloud obtained through the HERITAGE Science Demonstration…

The 7 and 15 um observations of selected fields in the Galactic Plane obtained with ISOCAM during the ISOGAL program offer an unique possibility to search for previously unknown YSOs, undetected by IRAS because of lower sensitivity or…

Unveiling the evolutionary history of galaxies necessitates a precise understanding of their physical properties. Traditionally, astronomers achieve this through spectral energy distribution (SED) fitting. However, this approach can be…

We are searching for Young Stellar Objects (YSOs) near the boundary between protostars and pre-main sequence objects, what we have termed transitional YSOs. We have identified a sample of 125 objects as candidate transitional YSOs on the…

With the advent of digital astronomy, new benefits and new problems have been presented to the modern day astronomer. While data can be captured in a more efficient and accurate manor using digital means, the efficiency of data retrieval…

The very inner structure of massive young stellar objects (YSOs) is difficult to trace. With conventional observational methods we identify structures still several hundreds of AU in size. However, the (proto-)stellar growth takes place at…

We present an HCO$^{+}$ J=3-2 survey of Class 0+I and Flat SED young stellar objects (YSOs) found in the Gould Belt clouds by surveys with Spitzer. Our goal is to provide a uniform Stage 0+I source indicator for these embedded protostar…

We present an application of unsupervised Machine Learning Clustering to the PAU Survey of galaxy spectral energy distribution (SED) within the COSMOS field. The clustering algorithm is implemented and optimized to get the relevant groups…

The spectral energy distribution (SED) of observed stars in wide-field images is crucial for chromatic point spread function (PSF) modelling methods, which use unresolved stars as integrated spectral samples of the PSF across the field of…

(abridged) We develop a tool for the automated spectral classification of OB stars according to their sub-types. We use the regular Random Forest (RF) algorithm, the Probabilistic RF (PRF), and we introduce the KDE-RF method which is a…