相关论文: Application of a Neural Network classifier for the…
(abridged) We present a comprehensive study of stellar stratification in young star clusters in the Large Magellanic Cloud (LMC). We apply our recently developed effective radius method for the assessment of stellar stratification on…
The existing open cluster membership determination algorithms are either prior dependent on some known parameters of clusters or are not automatable to large samples of clusters. In this paper, we present, ML-MOC, a new machine learning…
We present a novel method for automatically detecting and characterising semi-resolved star clusters: clusters where the observational point-spread function (PSF) is smaller than the cluster's radius, but larger than the separations between…
A search for $\gamma$-ray emission from SNRs in the Large Magellanic Cloud (LMC) based on the detection of concentrations in the arrival direction Fermi-LAT images of photons at energies higher than 10 GeV found significant evidence for 9…
Aims: We present a comprehensive study of the supernova remnant (SNR) population of the Small Magellanic Cloud (SMC). We measure multiwavelength properties of the SMC SNRs and compare them to those of the Large Magellanic Cloud (LMC)…
We present identifications and kinematic analysis of 7,426 massive ($\mathrm{\geq}8M_{\odot}$) stars in the Small Magellanic Cloud (SMC), using Gaia DR3 data. We used Gaia ($G_\mathrm{BP}-G_\mathrm{RP}$, $G$) color-magnitude diagram to…
The observation of our home galaxy, the Milky Way (MW), is made difficult by our internal viewpoint. The Gaia survey that contains around 1.6 billion star distances is the new flagship of MW structure and can be combined with other…
Observational studies have identified several sub-structures in different regions of the Magellanic Clouds, the nearest pair of interacting dwarf satellites of the Milky Way. By studying the metallicity of the sources in these…
We present high-resolution maps of the dust reddening in the Magellanic Clouds (MCs). The maps cover the Large and Small Magellanic Cloud (LMC and SMC) area and have a spatial angular resolution between $\sim$ 26 arcsec and 55 arcmin. Based…
We present several machine learning (ML) models developed to efficiently separate stars formed in-situ in Milky Way-type galaxies from those that were formed externally and later accreted. These models, which include examples from…
We present for the first time Washington CT1 photometry for 11 unstudied or poorly studied candidate star clusters. The selected objects are of small angular size, contain a handful of stars, and are projected towards the innermost regions…
The Large Magellanic Cloud (LMC) is the most luminous satellite galaxy of the Milky Way and owing to its companion, the Small Magellanic Cloud (SMC), represents an excellent laboratory to study the interaction of dwarf galaxies. The aim of…
We present a detailed view of cluster formation (CF) to trace the evolution and interaction history of the Magellanic Clouds (MCs) in the last 3.5 Gyr. Using the \textit{Gaia} DR3 data, we parameterized 1710 and 280 star clusters in the…
(abridged) Mass loss is a key parameter in the evolution of massive stars, with discrepancies between theory and observations and with unknown importance of the episodic mass loss. To address this we need increased numbers of classified…
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…
In this contribution I will present the current status of our project of stellar population analyses and spatial information of both Magellanic Clouds (MCs). The Magellanic Clouds - especially the LMC with its large size and small depth…
A detailed study of stellar populations in Milky Way (MW) satellite galaxies remains an observational challenge due to their faintness and fewer spectroscopically confirmed member stars. We use unsupervised machine learning methods to…
The present work explores the origin of the formation of star clusters in our Galaxy and in Small Magellanic Cloud (SMC) through simulated H-R diagrams and compare those with observed star clusters. The simulation study produces synthetic…
Aiming at providing a firm mean distance estimate to the Small Magellanic Cloud (SMC), and thus to place it within the internally consistent Local Group distance framework we recently established, we compiled the current-largest database of…
We present a novel multimodal neural network (MNN) for classifying astronomical sources in multiband ground-based observations, from optical to near infrared, to separate sources in stars, galaxies and quasars. Our approach combines a…