Related papers: Learning novel representations of variable sources…
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 unprecedented volume and quality of data from space- and ground-based telescopes present an opportunity for machine learning to identify new classes of variable stars and peculiar systems that may have been overlooked by traditional…
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…
The combination of machine learning models with physical models is a recent research path to learn robust data representations. In this paper, we introduce p$^3$VAE, a variational autoencoder that integrates prior physical knowledge about…
The recent release of 220+ million BP/RP spectra in $\textit{Gaia}$ DR3 presents an opportunity to apply deep learning models to an unprecedented number of stellar spectra, at extremely low-resolution. The BP/RP dataset is so massive that…
We combine Gaia Data Release 3 and artificial intelligence to enhance the current statistics of substellar companions, particularly within regions of the orbital period vs. mass parameter space that remain poorly constrained by the radial…
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…
One of the novelties of the Gaia-DR3 with respect to the previous data releases is the publication of the multiband light curves of about 1 million AGN. The goal of this work was the creation of a catalogue of variable AGN, whose selection…
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…
At the core of the Gaia mission is a multi-epoch survey consisting of astrometric, photometric, spectrophotometric, and spectroscopic measurements. The astrometric time series provides parallaxes and proper motions, along with information…
High resolution galaxy spectra contain much information about galactic physics, but the high dimensionality of these spectra makes it difficult to fully utilize the information they contain. We apply variational autoencoders (VAEs), a…
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 Early Data Release 3 contains astrometry and photometry results for about 1.8 billion sources based on observations collected by the ESA Gaia satellite during the first 34 months of operations. This paper focuses on the photometric…
The Gaia DR3, released in June 2022, included low-resolution BP/RP (XP) spectra that have been exploited for the classification of various types of emission-line objects using machine-learning techniques. The Gaia Extended Stellar…
Machine learning can play a powerful role in inferring missing line-of-sight velocities from astrometry in surveys such as Gaia. In this paper, we apply a neural network to Gaia Early Data Release 3 (EDR3) and obtain line-of-sight…
Context: Photometric variability is a defining characteristic of young stellar objects (YSO), which can be traced back to a range of physical processes taking place at different stages of young stars' formation and early evolution. Gaia's…
Context. As part of Gaia Data Release 3 (Gaia DR3), epoch photometry has been released for 1.2 million sources centred on M31. This is a taster for Gaia Data Release 4 where all the epoch photometry will be released. Aims. In this paper the…
Variational autoencoders (VAE) represent a popular, flexible form of deep generative model that can be stochastically fit to samples from a given random process using an information-theoretic variational bound on the true underlying…
Representation learning seeks to expose certain aspects of observed data in a learned representation that's amenable to downstream tasks like classification. For instance, a good representation for 2D images might be one that describes only…