Related papers: Cluster membership analysis with supervised learni…

Many clustering methods, including k-means, require the user to specify the number of clusters as an input parameter. A variety of methods have been devised to choose the number of clusters automatically, but they often rely on strong…

This study presents a novel integration of unsupervised learning and decision-making strategies for the advanced analysis of 4D-STEM datasets, with a focus on non-negative matrix factorization (NMF) as the primary clustering method. Our…

There exist a variety of star-galaxy classification techniques, each with their own strengths and weaknesses. In this paper, we present a novel meta-classification framework that combines and fully exploits different techniques to produce a…

We present a two-component Machine Learning (ML) based approach for classifying astronomical images by data-quality via an examination of sources detected in the images and image pixel values from representative sources within those images.…

In this work we present a method to identify possible members of globular clusters using data from Gaia DR2. The method consists of two stages: the first one based on a clustering algorithm, and the second one based on the analysis of the…

The period versus mass diagrams (i.e., rotational sequences) of open clusters provide crucial constraints for angular momentum evolution studies. However, their memberships are often heavily contaminated by field stars, which could…

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…

We present the results of various automated classification methods, based on machine learning (ML), of objects from data releases 6 and 7 (DR6 and DR7) of the Sloan Digital Sky Survey (SDSS), primarily distinguishing stars from quasars. We…

Stellar clusters are regularly used to study the evolution of their host galaxy. Except for a few nearby galaxies, these studies rely on the interpretation of integrated cluster properties, especially integrated photometry observed using…

The bulk motion of star clusters can be determined after careful membership analysis using parametric or non-parametric approaches. This study aims to implement non-parametric membership analysis based on Binned Kernel Density Estimator…

The Gaia Data Release 3 (DR3), published in June 2022, delivers a diverse set of astrometric, photometric, and spectroscopic measurements for more than a billion stars. The wealth and complexity of the data makes traditional approaches for…

Determining whether a dataset was part of a machine learning model's training data pool can reveal privacy vulnerabilities, a challenge often addressed through membership inference attacks (MIAs). Traditional MIAs typically require access…

In recent years, large scale data intensive astronomical surveys have resulted in more detailed images being produced than scientists can manually classify. Even attempts to crowd-source this work will soon be outpaced by the large amount…

Within scientific and real life problems, classification is a typical case of extremely complex tasks in data-driven scenarios, especially if approached with traditional techniques. Machine Learning supervised and unsupervised paradigms,…

Context. Since the first publication of the Gaia catalogue a new view of our Galaxy has arrived. Its astrometric and photometric information has improved the precision of the physical parameters of open star clusters obtained from them.…

We consider an extension of model-based clustering to the semi-supervised case, where some of the data are pre-labeled. We provide a derivation of the Bayesian Information Criterion (BIC) approximation to the Bayes factor in this setting.…

Classifying stars, galaxies, and quasars is essential for understanding cosmic structure and evolution; however, the vast data from modern surveys make manual classification impractical, while supervised learning methods remain constrained…

Making mock simulated catalogs is an important component of astrophysical data analysis. Selection criteria for observed astronomical objects are often too complicated to be derived from first principles. However the existence of an…

This paper explores the application of machine learning methods for classifying astronomical sources using photometric data, including normal and emission line galaxies (ELGs; starforming, starburst, AGN, broad line), quasars, and stars. We…

Studying star clusters offers significant advances in stellar astrophysics due to the combined power of having many stars with essentially the same distance, age, and initial composition. This makes clusters excellent test benches for…