Related papers: Identifying Evolutionary Stages of Molecular Clump…
Structural properties posses valuable information about the formation and evolution of galaxies, and are important for understanding the past, present, and future universe. Here we use unsupervised machine learning methodology to analyze a…
The research of infall motion is a common means to study molecular cloud dynamics and the early process of star formation. Many works had been done in-depth research on infall. We searched the literature related to infall study of molecular…
The task of labeling data for training deep neural networks is daunting and tedious, requiring millions of labels to achieve the current state-of-the-art results. Such reliance on large amounts of labeled data can be relaxed by exploiting…
We study the mid-infrared (MIR) properties of the galaxies in the A2199 supercluster at z = 0.03 to understand the star formation activity of galaxy groups and clusters in the supercluster environment. Using the Wide-field Infrared Survey…
The efficient classification of different types of supernova is one of the most important problems for observational cosmology. However, spectroscopic confirmation of most objects in upcoming photometric surveys, such as the The Rubin…
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…
Currently available star cluster catalogues from HST imaging of nearby galaxies heavily rely on visual inspection and classification of candidate clusters. The time-consuming nature of this process has limited the production of reliable…
Machine learning (ML) is a standard approach for estimating the redshifts of galaxies when only photometric information is available. ML photo-z solutions have traditionally ignored the morphological information available in galaxy images…
Ground-based optical surveys such as PanSTARRS, DES, and LSST, will produce large catalogs to limiting magnitudes of r > 24. Star-galaxy separation poses a major challenge to such surveys because galaxies---even very compact…
Due to the ever-expanding volume of observed spectroscopic data from surveys such as SDSS and LAMOST, it has become important to apply artificial intelligence (AI) techniques for analysing stellar spectra to solve spectral classification…
Unsupervised machine learning offers significant opportunities for extracting knowledge from unlabeled data sets and for achieving maximum machine learning performance. This paper demonstrates how to construct, use, and evaluate a high…
Sufficiently massive clumps of molecular gas collapse under self-gravity and fragment to spawn a cluster of stars that have a range of masses. We investigate observationally the early stages of formation of a stellar cluster in a massive…
We introduce a new method to determine galaxy cluster membership based solely on photometric properties. We adopt a machine learning approach to recover a cluster membership probability from galaxy photometric parameters and finally derive…
Unsupervised machine learning is widely used to mine large, unlabeled datasets to make data-driven discoveries in critical domains such as climate science, biomedicine, astronomy, chemistry, and more. However, despite its widespread…
By matching infrared-selected, massive young stellar objects (MYSOs) and compact HII regions in the RMS survey to massive clumps found in the submillimetre ATLASGAL survey, we have identified ~1000 embedded young massive stars between…
Traditional studies of stellar clusters in external galaxies use surface photometry and therefore focus on systems that are still bright and compact enough to be separated from the stellar background. Consequently, the latter stages of…
Understanding how galaxies trace the underlying matter density field is essential for characterizing the influence of the large-scale structure on galaxy formation, being therefore a key ingredient in observational cosmology. This…
Definitive cancer diagnosis and management depend upon the extraction of information from microscopy images by pathologists. These images contain complex information requiring time-consuming expert human interpretation that is prone to…
(Abridged) The Census of High- and Medium-mass Protostars (CHaMP) is the first large-scale (280 degree<l<300 degree, -4 degree<b<2 degree), unbiased, sub-parsec resolution survey of Galactic molecular clumps and their embedded stars. Barnes…
We present a catalogue of starless and protostellar clumps associated with infrared dark clouds (IRDCs) in a 40 degrees wide region of the inner Galactic Plane (b<1). We have extracted the far-infrared (FIR) counterparts of 3493 IRDCs with…