相关论文: Parameter Estimation for Open Clusters using an Ar…
Upcoming large-area narrow band photometric surveys, such as J-PAS, will enable us to observe a large number of galaxies simultaneously and efficiently. However, it will be challenging to analyse the spatially-resolved stellar populations…
We present a statistical and multiwavelength photometric studies of young open cluster IC 1590. We identified 91 cluster members using $Gaia$ DR3 astrometry data using ensemble-based unsupervised machine learning techniques. From $Gaia$…
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…
The large majority of extragalactic star cluster studies done to date have essentially used two or three-passband aperture photometry, combined with theoretical stellar population synthesis models, to obtain age estimates. The accuracy to…
The possibility to constrain cosmological parameters from galaxy surveys using field-level machine learning methods that bypass traditional summary statistics analyses, depends crucially on our ability to generate simulated training sets.…
We present a new technique to fit color-magnitude diagrams of open clusters based on the Cross-Entropy global optimization algorithm. The method uses theoretical isochrones available in the literature and maximizes a weighted likelihood…
Recent studies have identified star clusters with multiple components based on accurate spatial distributions and/or proper motions from Gaia DR3, utilising diverse diagnostics to improve our understanding of subgroup evolution. These…
In general, a similarity threshold (i.e., a vigilance parameter) for a node learning process in Adaptive Resonance Theory (ART)-based algorithms has a significant impact on clustering performance. In addition, an edge deletion threshold in…
We present ASteCA (Automated Stellar Cluster Analysis), a suit of tools designed to fully automatize the standard tests applied on stellar clusters to determine their basic parameters. The set of functions included in the code make use of…
Data from the Gaia satellite are revolutionising our understanding of the Milky Way. With every new data release, there is a need to update the census of open clusters. We aim to conduct a blind, all-sky search for open clusters using 729…
During the last years and decades several individual studies and large-scale spectroscopic surveys significantly improved our knowledge of the Galactic metallicity distribution based on open clusters. The availability of Gaia data provided…
Very precise observational data are needed for studying the stellar cluster parameters (distance, reddening, age, metallicity) and cluster internal kinematics. In turn, these give us an insight into the properties of our Galaxy, for…
Context: Star clusters, composed of stars born from the same molecular cloud, serve as invaluable natural laboratories for understanding the fundamental processes governing stellar formation and evolution. Aims: This study aims to…
The distribution of young stars into OB associations has long been in need of updating. High-precision {\sl Gaia} early Data Release 3 astrometry, coupled with modern machine-learning methods, allows this to be done. We have compiled a…
The ages of star clusters and co-moving stellar groups contain essential information about the Milky Way. Their special properties make them excellent tracers of galactic structure and key components to unlocking its star formation history.…
A new Monte Carlo method has been developed in order to derive ages of young embedded clusters within massive star forming regions where there is strong differential reddening. After foreground and infrared excess source candidates are…
We present a novel approach for identifying members of open star clusters using Gaia DR3 data by combining Minimum Spanning Tree (MST) and Gaussian Mixture Model (GMM) techniques. Our method employs a three-step process: initial filtering…
This work presents GalProTE, a proof-of-concept Machine Learning model utilizing a Transformer Encoder to determine stellar age, metallicity, and dust attenuation from optical spectra. Designed for large astronomical surveys, GalProTE…
Observations of clusters suffer from issues such as completeness, projection effects, resolving individual stars and extinction. As such, how accurate are measurements and conclusions are likely to be? Here, we take cluster simulations…
(ABRIDGED) We assess the systematic uncertainties in (young) cluster age, mass, and - to a lesser extent - extinction and metallicity determinations, based on broad-band imaging observations with the Hubble Space Telescope. Our aim here is…