Related papers: Cluster membership analysis with supervised learni…
We apply four statistical learning methods to a sample of $7941$ galaxies ($z<0.06$) from the Galaxy and Mass Assembly (GAMA) survey to test the feasibility of using automated algorithms to classify galaxies. Using $10$ features measured…
Clustering is a usual unsupervised machine learning technique for grouping the data points into groups based upon similar features. We focus here on unsupervised clustering for contaminated data, i.e in the case where K-medians should be…
With the coming data deluge from synoptic surveys, there is a growing need for frameworks that can quickly and automatically produce calibrated classification probabilities for newly-observed variables based on a small number of time-series…
We present mean proper motions and membership probabilities of individual stars for optically visible open clusters, which have been determined using data from the UCAC5 catalog. This follows our previous studies with the UCAC2 and UCAC4…
The distribution of member stars in the surroundings of an Open Cluster (OC) can shed light on the process of its formation, evolution and dissolution. The analysis of structural parameters of OCs as a function of their age and position in…
We present a machine learning (ML) framework for the detection of wide binary star systems using Gaia DR3 data. By training supervised ML models on established wide binary catalogues, we efficiently classify wide binaries and employ…
Label noise in multi-label learning (MLL) poses significant challenges for model training, particularly in partial multi-label learning (PML) where candidate labels contain both relevant and irrelevant labels. While clustering offers a…
We introduce GalWeight, a new technique for assigning galaxy cluster membership. This technique is specifically designed to simultaneously maximize the number of bona fide cluster members while minimizing the number of contaminating…
We present a Bayesian inference approach to estimating the cumulative mass profile and mean squared velocity profile of a globular cluster given the spatial and kinematic information of its stars. Mock globular clusters with a range of…
Quantifying the contribution of mergers to the stellar mass of galaxies is key for constraining the mechanisms of galaxy assembly across cosmic time. However, the mapping between observable galaxy properties and merger histories is not…
The third Gaia data release (DR3) contains $\sim$170\,000 astrometric orbit solutions of two-body systems located within $\sim$500 pc of the Sun. Determining component masses in these systems, in particular of stars hosting exoplanets,…
Forthcoming large galaxy cluster surveys will yield tight constraints on cosmological models. It has been shown that in an idealized survey, containing > 10,000 clusters, statistical errors on dark energy and other cosmological parameters…
The task of labeling data for training deep neural networks is daunting and tedious, requiring millions of labels to achieve the current state-of-the-art results. Such reliance on large amounts of labeled data can be relaxed by exploiting…
In this paper, we explore the feasibility of using machine learning regression as a method of extracting basic stellar parameters and line-of-sight extinctions from spectro-photometric data. We built a stable gradient-boosted random-forest…
Machine learning has increasingly gained more popularity with its incredibly powerful ability to make predictions or calculated suggestions for large amounts of data. We apply the machine learning classification to 85,613,922 objects in the…
In this study, we develop a membership identification method and apply it for 30 open clusters (OCs) within 200 pc of the Sun using astrometric data of Gaia DR3. By accounting for projection effects that distort apparent stellar motions,…
Robust galaxy cluster mass estimates are fundamental for constraining cosmological parameters from counts. For this reason, it is essential to search for tracers that, independent of the cluster's dynamical state, have a small intrinsic…
Open clusters have long been used to gain insights into the structure, composition, and evolution of the Galaxy. With the large amount of stellar data available for many clusters in the Gaia era, new techniques must be developed for…
In Gaia DR2, the unprecedented high-precision level reached in sub-mas for astrometry and mmag for photometry. Using cluster members identified with these astrometry and photometry in Gaia DR2, we can obtain a reliable determination of…
Heart disease is a serious global health issue that claims millions of lives every year. Early detection and precise prediction are critical to the prevention and successful treatment of heart related issues. A lot of research utilizes…