Related papers: Cluster membership analysis with supervised learni…
We explore unsupervised machine learning for galaxy morphology analyses using a combination of feature extraction with a vector-quantised variational autoencoder (VQ-VAE) and hierarchical clustering (HC). We propose a new methodology that…
We present Classification of Cluster GAlaxy MEmbers (C$^2$-GaMe), a classification algorithm based on a suite of machine learning models that differentiates galaxies into orbiting, infalling, and background (interloper) populations, using…
Clustering is an unsupervised machine learning methodology where unlabeled elements/objects are grouped together aiming to the construction of well-established clusters that their elements are classified according to their similarity. The…
The discovery of exoplanets has expanded our understanding of planetary systems and opened new avenues for astronomical research. In this study, we present a machine learning (ML) framework for exoplanet identification using a time-series…
Stellar membership determination of an open cluster is an important process to do before further analysis. Basically, there are two classes of membership determination method: parametric and non-parametric. In this study, an alternative of…
We present a machine learning (ML) pipeline to identify star clusters in the multi{color images of nearby galaxies, from observations obtained with the Hubble Space Telescope as part of the Treasury Project LEGUS (Legacy ExtraGalactic…
Motivated by previous findings that the magnitude gap between certain satellite galaxy and the central galaxy can be used to improve the estimation of halo mass, we carry out a systematic study of the information content of different member…
This work presents the first large-scale photometric variability survey of the intermediate age (~200 Myr) open cluster M11. Thirteen nights of data over two observing seasons were analyzed (using crowded field and ensemble photometry…
This chapter reviews the application of Artificial Intelligence (AI) techniques to the study of galaxy clusters, covering both theoretical developments and their use as tools to infer cluster properties from a variety of observational…
We present a study of six open clusters (Berkeley 67, King 2, NGC 2420, NGC 2477, NGC 2682 and NGC 6940) using the Ultra Violet Imaging Telescope (UVIT) aboard \textit{ASTROSAT} and \textit{Gaia} EDR3. We used combinations of astrometric,…
Student's mental health problems have been explored previously in higher education literature in various contexts including empirical work involving quantitative and qualitative methods. Nevertheless, comparatively few research could be…
This research investigates how Machine Learning (ML) algorithms can assist in workload allocation strategies by detecting tasks with node affinity operators (referred to as constraint operators), which constrain their execution to a limited…
The primary difficulty in measuring dynamical masses of galaxy clusters from galaxy data lies in the separation between true cluster members from interloping galaxies along the line of sight. We study the impact of membership contamination…
We propose a simple mixed membership model for social network clustering in this paper. A flexible function is adopted to measure affinities among a set of entities in a social network. The model not only allows each entity in the network…
We present a homogeneous catalog of 1,232 open clusters with precisely determined ages, metallicities, distances, extinctions, and stellar mass function (MF) slopes, derived from Gaia DR3 data. The parameters are inferred using the Mixture…
This work considers clustering nodes of a largely incomplete graph. Under the problem setting, only a small amount of queries about the edges can be made, but the entire graph is not observable. This problem finds applications in…
Machine learning (ML) has become a key tool in astronomy, driving advancements in the analysis and interpretation of complex datasets from observations. This article reviews the application of ML techniques in the identification and…
We provide classifications for all 143 million non-repeat photometric objects in the Third Data Release of the Sloan Digital Sky Survey (SDSS) using decision trees trained on 477,068 objects with SDSS spectroscopic data. We demonstrate that…
We present a study of 7 southern open clusters based on UBVRI CCD photomety (Johnsons-Cousins system) and Gaia DR2 data. Dias 4, Dias 6 and four other clusters had UBVRI photometric observations determined for the first time. From the…
We use machine learning to develop a framework for classifying meteoroids based on 13 directly observed parameters from the Global Meteor Network. This method adds depth to the $K_{b}$ parameter, which uses only three parameters. We employ…