Related papers: Protostellar classification using supervised machi…

Understanding the characteristics of young stellar populations is essential for deriving insights into star formation processes within parent molecular clouds and the influence of massive stars. This study focuses on YSOs in the G…

The rate of image acquisition in modern synoptic imaging surveys has already begun to outpace the feasibility of keeping astronomers in the real-time discovery and classification loop. Here we present the inner workings of a framework,…

We present spectral observations of 130 young stellar objects (YSOs) in the Serpens Cloud Core and NGC 1333 embedded clusters. The observations consist of near-IR spectra in the H and K-bands, from SpeX on the IRTF and far-red spectra (6000…

We present the initial results of our investigation of the star-forming complex W49, one of the youngest and most luminous massive star forming regions in our Galaxy. We used Spitzer/Infrared Array Camera (IRAC) data to investigate massive…

Young stellar objects (YSOs) are the gold standard for tracing star formation in galaxies but have been unobservable beyond the Milky Way and Magellanic Clouds. But that all changed when the James Webb Space Telescope was launched, which we…

We use Spitzer Space Telescope observations from the Spitzer Survey of the Small Magellanic Cloud (S3MC) to study the young stellar content of N66, the largest and brightest HII region in the SMC. In addition to large numbers of normal…

In this paper, the fourth version the Sloan Digital Sky Survey (SDSS-4), Data Release 16 dataset was used to classify the SDSS dataset into galaxies, stars, and quasars using machine learning and deep learning architectures. We efficiently…

We investigate star-galaxy classification for astronomical surveys in the context of four methods enabling the interpretation of black-box machine learning systems. The first is outputting and exploring the decision boundaries as given by…

Traditional spectral energy distribution (SED) fitting codes used to derive galaxy physical properties are often uncertain at the factor of a few level owing to uncertainties in galaxy star formation histories and dust attenuation curves.…

Accurately detecting rendezvous and proximity operations (RPO) is crucial for understanding how objects are behaving in the space domain. However, detecting closely-spaced objects (CSO) is challenging for ground-based optical space domain…

We investigate the extent to which supervised machine learning techniques can distinguish between neutron-star matter models using macroscopic and oscillation-related quantities derived from theoretical stellar configurations. Four…

We present an analysis of Young Stellar Object (YSO) candidates towards the LDN 1188 molecular cloud. The YSO candidates were selected from the WISE all-sky catalogue, based on a statistical method. We found 601 candidates in the region,…

We used 3.1 million spectroscopically labelled sources from the Sloan Digital Sky Survey (SDSS) to train an optimised random forest classifier using photometry from the SDSS and the Widefield Infrared Survey Explorer (WISE). We applied this…

We present key results from the Herschel Orion Protostar Survey (HOPS): spectral energy distributions (SEDs) and model fits of 330 young stellar objects, predominantly protostars, in the Orion molecular clouds. This is the largest sample of…

We have used archival HST H$\alpha$ images to study the immediate environments of massive and intermediate-mass young stellar object (YSO) candidates in the Large Magellanic Cloud (LMC). The sample of YSO candidates, taken from Gruendl &…

We employ the XGBoost machine learning (ML) method for the morphological classification of galaxies into two (early-type, late-type) and five (E, S0--S0a, Sa--Sb, Sbc--Scd, Sd--Irr) classes, using a combination of non-parametric…

Context: The huge and still rapidly growing amount of galaxies in modern sky surveys raises the need of an automated and objective classification method. Unsupervised learning algorithms are of particular interest, since they discover…

We present the results of our investigation of the star-forming complexes W51 and W43, two of the brightest in the first Galactic quadrant. In order to determine the young stellar object (YSO) populations in W51 and W43 we used…

Weakly Supervised Object Detection (WSOD), using only image-level annotations to train object detectors, is of growing importance in object recognition. In this paper, we propose a novel deep network for WSOD. Unlike previous networks that…

Embedded Young Stellar Objects (YSO) in dense interstellar clouds is treated self-consistently to understand their spectral energy distributions (SED). Radiative transfer calculations in spherical geometry involving the dust as well as the…