Related papers: Unsupervised learning for variability detection wi…
White dwarf stars are ubiquitous in the Galaxy, and are essential to understanding stellar evolution. While most white dwarfs are photometrically stable and reliable flux standards, some can be highly variable, which can reveal unique…
We present a new clustering method, Significance Mode Analysis (SigMA), to extract co-spatial and co-moving stellar populations from large-scale surveys such as ESA Gaia. The method studies the topological properties of the density field in…
The spatial distribution, Galactic model parameters and luminosity function of cataclysmic variables (CVs) are established using re-estimated trigonometric parallaxes of {\it Gaia} DR3. The data sample of 1,587 CVs in this study is claimed…
The success of automatic classification of variable stars strongly depends on the lightcurve representation. Usually, lightcurves are represented as a vector of many statistical descriptors designed by astronomers called features. These…
State-of-the-art techniques to identify H\alpha emission line sources in narrow-band photometric surveys consist of searching for H\alpha excess with reference to nearby objects in the sky (position-based selection). However, while this…
Although previous searches for star clusters have been very successful, many clusters are likely still omitted, especially at high Galactic latitude regions. In this work, based on the astrometry of Gaia EDR3, we searched nearby (parallax >…
We here apply a novel technique selecting quasar candidates purely as sources with zero proper motions in the Gaia data release 2 (DR2). We demonstrate that this approach is highly efficient toward high Galactic latitudes with < 25%…
This paper presents a novel approach to learn and detect distinctive regions on 3D shapes. Unlike previous works, which require labeled data, our method is unsupervised. We conduct the analysis on point sets sampled from 3D shapes, then…
Motivated by the vast gap between photometric and spectroscopic data volumes, there is great potential in using 5D kinematic information to identify and study substructures of the Milky Way. We identify substructures in the Galactic halo…
Open clusters are among the most useful and widespread tracers of Galactic structure. The completeness of the Galactic open cluster census, however, remains poorly understood. For the first time ever, we establish the selection function of…
The debris from past merger events is expected and, to some extent, known to populate the stellar halo near the Sun. We aim to identify and characterise such merger debris using Gaia DR3 data supplemented by metallicity and chemical…
Large surveys are providing a diversity of spectroscopic observations with Gaia alone set to deliver millions of Ca-triplet-region spectra across the Galaxy. We aim to understand the dimensionality of the chemical abundance information in…
We report 541 new open cluster candidates in Gaia EDR3 through revisiting the cluster results from an earlier analysis of the Gaia DR2, which revealed nearly a thousand open cluster candidates in the solar neighborhood (mostly d < 3 kpc)…
Since its launch in 2013, the Gaia space telescope has provided precise measurements of the positions and magnitudes of over 1 billion stars. This has enabled extensive searches for stellar and sub-stellar companions through astrometric and…
The Gaia mission has observed over 2 billion stars repeatedly across the entire sky over 10 years, revealing the many astronomical objects that vary on human timescales from seconds to years. Its repeated astrometric, photometric,…
We propose an unsupervised learning methodology with descriptors based on Topological Data Analysis (TDA) concepts to describe the local structural properties of materials at the atomic scale. Based only on atomic positions and without a…
Machine learning can provide powerful tools to detect patterns in multi-dimensional parameter space. We use K-means -a simple yet powerful unsupervised clustering algorithm which picks out structure in unlabeled data- to study a sample of…
The 3rd data release of the Gaia mission includes orbital solutions for $> 10^5$ single-lined spectroscopic binaries, representing more than an order of magnitude increase in sample size over all previous studies. This dataset is a treasure…
The Hunting Outbursting Young Stars (HOYS) project performs long-term, optical, multi-filter, high cadence monitoring of 25 nearby young clusters and star forming regions. Utilising Gaia DR3 data we have identified about 17000 potential…
In the era of big astronomical surveys, our ability to leverage artificial intelligence algorithms simultaneously for multiple datasets will open new avenues for scientific discovery. Unfortunately, simply training a deep neural network on…