Related papers: Identifying Evolutionary Stages of Molecular Clump…
In the Milky Way there are thousands of stellar clusters each harboring from a hundred to a million stars. Although clusters are common, the initial conditions of cluster formation are still not well understood. To determine the processes…
Membership analysis is an important tool for studying star clusters. There are various approaches to membership determination, including supervised and unsupervised machine learning (ML) methods. We perform membership analysis using the…
Massive clumps tend to fragment into clusters of cores and condensations, some of which form high-mass stars. In this work, we study the structure of massive clumps at different scales, analyze the fragmentation process, and investigate the…
Massive stars (M $\gsim 10$ \msun) form from collapse of parsec-scale molecular clumps. How molecular clumps fragment to give rise to massive stars in a cluster with a distribution of masses is unclear. We search for cold cores that may…
We have developed a procedure for the classification of eclipsing binaries from their light-curve parameters and spectral type. The procedure was tested on more than 1000 systems with known classification, and its efficiency was estimated…
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…
Automated characterization of galactic substructure is an essential step in understanding the transformative physical processes driving galaxy evolution. In this study, we investigate the application of deep learning (DL) frameworks to…
The intracluster light (ICL) permeating galaxy clusters is a tracer of the cluster's assembly history, and potentially a tracer of their dark matter structure. In this work we explore the capability of the Euclid Wide Survey to detect ICL…
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 study the formation and evolution of several molecules in a collapsing interstellar cloud using a reasonably large reaction network containing more then four hundred atomic and molecular species. We employ a time dependent, spherically…
We present results from the first robust investigation of galaxy morphology as a function of environment at z>1.5. Our study is motivated by the fact that star-forming galaxies contained within a protocluster at z=2.3 in the HS1700+64 field…
We have carried out a survey of the dense clumps associated with 14 embedded clusters in the C^18O (J=1-0) line emission with the Nobeyama 45m telescope in order to understand the formation and evolution of stellar clusters in dense clumps…
Nowadays, Machine Learning techniques offer fast and efficient solutions for classification problems that would require intensive computational resources via traditional methods. We examine the use of a supervised Random Forest to classify…
Locating the center of convex objects is important in both image processing and unsupervised machine learning/data clustering fields. The automated analysis of biological images uses both of these fields for locating cell nuclei and for…
Molecular clouds are the principle stellar nurseries of our universe, keeping them in the focus of both observational and theoretical studies. From observations, some of the key properties of molecular clouds are well known but many…
Using the ISOPHOT Serendipity Survey (ISOSS) at 170um a sample of galactic star-forming regions exhibiting very cold dust temperatures (< 20 K) and high masses (> 100 M_sun) has been established. We characterise the star-forming content of…
The ATLASGAL survey provides an ideal basis for detailed studies of large numbers of massive star forming clumps covering the whole range of evolutionary stages. The ATLASGAL Top100 is a sample of clumps selected from their infrared and…
Supervised Learning has been successfully used to produce phase diagrams and identify phase boundaries when local order parameters are unavailable. Here, we apply unsupervised learning to this task. By using readily available clustering…
We use a multi million particle N-body + SPH simulation to follow the formation of a rich galaxy cluster in a Lambda+CDM cosmology, with the goal of understanding the origin and properties of intracluster stars. The simulation includes gas…
We extend a machine learning (ML) framework presented previously to model galaxy formation and evolution in a hierarchical universe using N-body + hydrodynamical simulations. In this work, we show that ML is a promising technique to study…