Related papers: Unsupervised learning for variability detection wi…
Hot sub-luminous stars represent a population of stripped and evolved red giants that is located on the extreme horizontal branch. Since they exhibit a wide range of variability due to pulsations or binary interactions, it is crucial to…
Gaia Data Release 3 (DR3) published for the first time epoch photometry, BP/RP (XP) low-resolution mean spectra, and supervised classification results for millions of variable sources. This extensive dataset offers a unique opportunity to…
The immense amount of time series data produced by astronomical surveys has called for the use of machine learning algorithms to discover and classify several million celestial sources. In the case of variable stars, supervised learning…
Context. In the current ever increasing data volumes of astronomical surveys, automated methods are essential. Objects of known classes from the literature are necessary for training supervised machine learning algorithms, as well as for…
We present a comprehensive kinematic analysis of the solar neighborhood (d < 50 pc) using high-precision astrometric data from the third Gaia Data Release (DR3). By leveraging the full six dimensional phase space information (positions and…
The Gaia Data Release 2 (DR2) provided an unprecedented volume of precise astrometric and excellent photometric data. In terms of data mining the Gaia catalogue, machine learning methods have shown to be a powerful tool, for instance in the…
Gaia DR3 contains 1.8 billion sources with G-band photometry, 1.5 billion of which with BP and RP photometry, complemented by positions on the sky, parallax, and proper motion. The median number of field-of-view transits in the three…
Context. Gaia has been in operations since 2014. The third Gaia data release expands from the early data release (EDR3) in 2020 by providing 34 months of multi-epoch observations that allowed us to probe, characterise and classify…
Hot subdwarfs are compact blue evolved objects, burning helium in their cores surrounded by a tiny hydrogen envelope. Most models agree on a common envelope binary evolution scenario in the Red Giant phase. However, the binarity rate for…
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…
In June 2022, Gaia DR3 has provided the astronomy community with about one million spectra from the Radial Velocity Spectrometer (RVS) covering the CaII triplet region. However, one-third of the published spectra have 15<S/N<25 per pixel…
Aims. In this work we use the Gaia DR3 set of ultracool dwarf candidates and complement the Gaia spectrophotometry with additional photometry in order to characterise its global properties. This includes the inference of the distances,…
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…
The Gaia mission's third data release recorded low-resolution spectra for about 100 000 white dwarf candidates. A small subset of these spectra show evidence of characteristic broad Ca II absorption features, implying the accretion of rocky…
During the last ten years, a considerable amount of effort has been made to develop algorithms for automatic classification of variable stars. That has been primarily achieved by applying machine learning methods to photometric datasets…
Identification of specific stellar populations using photometry for spectroscopic follow-up is a first step to confirm and better understand their nature. In this context, we present an unsupervised machine learning approach to identify…
CONTEXT: The unparalleled characteristics of Gaia photometry make it an excellent choice to study stellar variability. AIMS: To measure the phot. dispersion in G+G_BP+G_RP of the 145 677 450 Gaia DR3 5-parameter sources with G <= 17 mag and…
Context. Cepheids are pulsating stars that play a crucial role in several astrophysical contexts. Among the different types, the Classical Cepheids are fundamental tools for the calibration of the extragalactic distance ladder. They are…
Gaia can not individually resolve very close binary systems, however, the collected data can still be used to identify them. A powerful indicator of stellar multiplicity is the sources reported Renormalized Unit Weight Error (ruwe), which…
The goal of this study is to present the development of a machine learning based approach that utilizes phase space alone to separate the Gaia DR2 stars into two categories: those accreted onto the Milky Way from those that are in situ.…