arXiv subjects

Minia Manteiga

Publications and source records attributed to Minia Manteiga.

6 recordsLinked to original sources

arXiv2026

A homogeneous three-dimensional view of Molecular Cloud kinematics out to 2.5 kpc. Using Young Stellar Objects and Open Clusters as complementary tracers

Understanding the large-scale dynamics of molecular clouds (MCs) is crucial for constraining the processes that govern star formation and the structure and evolution of the Galaxy. While gas tracers have traditionally been used to map MC kinematics, stellar tracers such as young stellar objects (YSOs) and open clusters (OCs) provide a complementary approach that enables direct comparisons between the stellar and gaseous components. We aim to validate OCs as complementary tracers by testing whether they retain the same bulk kinematic imprint as YSOs, and to reconstruct the three-dimensional (3D) motions of the main MC complexes within 2.5 kpc of the Sun using YSOs and young OCs as tracers. Using Gaia DR3 astrometry together with complementary spectroscopic surveys for radial velocities, we compiled a unified sample of 24,732 stellar tracers. We applied robust clustering in proper motion space to identify co-moving YSOs and derived cloud-averaged motions via Monte Carlo sampling. These were compared with the kinematics of OCs younger than 30 Myr. Finally, we performed orbital integrations in a realistic Galactic potential to trace the past evolution of the clouds and quantify their expansion and rotation. We derive homogeneous 3D kinematics for 15 MC complexes within 2.5 kpc. YSOs and OCs exhibit strongly consistent kinematics, with a median spatial velocity offset of $\simeq 2$ km s$^{-1}$, confirming that both populations trace the bulk motion of their parent clouds. The resulting cloud kinematics show a median peculiar velocity of $\simeq 8.7$ km s$^{-1}$ with respect to Galactic rotation. We trace back the Solar System's voyage through the Orion cloud and the common origin of Lupus, Ophiuchus, and Corona Australis in Sco-Cen. Internally, we detect significant expansion in Orion and Ophiuchus ($5σ$) and coherent rotation in at least seven complexes.

astro-ph.GA↗

arXiv2025

Finding White Dwarfs' Hidden Companions using an Unsupervised Machine Learning Technique

White dwarfs (WD) with main-sequence (MS) companions are crucial probes of stellar evolution. However, due to the significant difference in their luminosities, the WD is often outshined by the MS star. The aim of this work is to find hidden companions in Gaia's sample of WD candidates. Our methodology involves applying an unsupervised machine learning algorithm for dimensionality reduction and clustering, known as Self-Organizing Map (SOM), to Gaia BP/RP (XP) spectra. This strategy allows us to naturally separate WDMS binaries from single WDs from the detection of subtle red flux excesses in the XP spectra that are indicative of low-mass MS companions. We validate our approach using confirmed WDMS binaries from the SDSS and LAMOST surveys, achieving a precision of $\sim 90\%$. We demonstrated that the luminosity of the faint companions in the missed systems is $\sim 50$ times lower than that of their WD primaries. Applying our SOM to 90,667 sources, we identify 993 WDMS candidates, 506 of which have not been previously reported in the literature. If confirmed, our sample will increase the known WDMS binaries by $20\%$. Additionally, we use the Virtual Observatory Spectral Energy Distribution Analyzer (VOSA) tool to refine and parameterize a ``golden sample'' of 136 WDMS binaries through multi-wavelength photometry and a two-body Spectral Energy Distribution fitting. These high-confidence WDMS binaries are composed by low-mass WDs ($\sim 0.42 M_{\odot}$), with cool MS companions ($\sim 2800$ K). Finally, 13 systems exhibit periodic variability consistent with eclipsing binaries, making them prime targets for further follow-up observations.

astro-ph.SR↗

arXiv2025

Disentangling stellar atmospheric parameters in astronomical spectra using Generative Adversarial Neural Networks

A method based on Generative Adversaria! Networks (GANs) is developed for disentangling the physical (effective temperature and gravity) and chemical (metallicity, overabundance of a-elements with respect to iron) atmospheric properties in astronomical spectra. Using a projection of the stellar spectra, commonly called latent space, in which the contribution dueto one or several main stellar physicochemical properties is minimised while others are enhanced, it was possible to maximise the information related to certain properties, which can then be extracted using artificial neural networks (ANN) as regressors with higher accuracy than a reference method based on the use of ANN trained with the original spectra. Methods. Our model utilises autoencoders, comprising two artificial neural networks: an encoder anda decoder which transform input data into a low-dimensional representation known as latent space. It also uses discriminators, which are additional neural networks aimed at transforming the traditional autoencoder training into an adversaria! approach, to disentangle or reinforce the astrophysical parameters from the latent space. The GANDALF tool is described. It was developed to define, train, and test our GAN model with a web framework to show how the disentangling algorithm works visually. It is open to the community in Github. Results. The performance of our approach for retrieving atmospheric stellar properties from spectra is demonstrated using Gaia Radial Velocity Spectrograph (RVS) data from DR3. We use a data-driven perspective and obtain very competitive values, ali within the literature errors, and with the advantage of an important dimensionality reduction of the data to be processed.

astro-ph.IM↗

arXiv2024

A method based on Generative Adversarial Networks for disentangling physical and chemical properties of stars in astronomical spectra

Data compression techniques focused on information preservation have become essential in the modern era of big data. In this work, an encoder-decoder architecture has been designed, where adversarial training, a modification of the traditional autoencoder, is used in the context of astrophysical spectral analysis. The goal of this proposal is to obtain an intermediate representation of the astronomical stellar spectra, in which the contribution to the flux of a star due to the most influential physical properties (its surface temperature and gravity) disappears and the variance reflects only the effect of the chemical composition over the spectrum. A scheme of deep learning is used with the aim of unraveling in the latent space the desired parameters of the rest of the information contained in the data. This work proposes a version of adversarial training that makes use of a discriminator per parameter to be disentangled, thus avoiding the exponential combination that occurs in the use of a single discriminator, as a result of the discretization of the values to be untangled. To test the effectiveness of the method, synthetic astronomical data are used from the APOGEE and Gaia surveys. In conjunction with the work presented, we also provide a disentangling framework (GANDALF) available to the community, which allows the replication, visualization, and extension of the method to domains of any nature.

astro-ph.IM↗

arXiv2024

Identifying new high-confidence polluted white dwarf candidates using Gaia XP spectra and Self-Organizing Maps

The identification of new white dwarfs (WDs) polluted with heavy elements is important since they provide a valuable tool for inferring chemical properties of putative planetary systems accreting material on their surfaces. The Gaia space mission has provided us with an unprecedented amount of astrometric, photometric, and low resolution (XP) spectroscopic data for millions of newly discovered stellar sources, among them thousands of WDs. In order to find WDs among this data and to identify which ones have metals in their atmospheres, we propose a methodology based on an unsupervised artificial intelligence technique called Self-Organizing Maps (SOM). In our approach a nonlinear high-dimensional dataset is projected on a 2D grid map where similar elements fall into the same neuron. By applying this method, we obtained a clean sample of 66,337 WDs. We performed an automatic spectral classification analysis to them, obtaining 143 bona fide polluted WD candidates not previously classified in the literature. The majority of them are cool WDs and we identify in their XP spectra several metallic lines such as Ca, Mg, Na, Li, and K. The fact that we obtain similar precision metrics than those achieved with recent supervised techniques highlights the power of our unsupervised approach to mine the Gaia archives for hidden treasures to follow-up spectroscopically with higher resolution.

astro-ph.SR↗

arXiv2024

A search for Galactic post-asymptotic giant branch stars in Gaia DR3

Context. When low and intermediate-mass stars leave the Asymptotic Giant Branch (AGB) phase, and before they reach the Planetary Nebulae stage, they enter a very brief and rather puzzling stellar evolutionary stage named post-AGB. Aims. To provide a reliable catalogue of galactic post-AGB stars together with their physical and evolutionary properties obtained through Gaia DR3 astrometry and photometry. Methods. We started by identifying post-AGB stars or possible candidates from the bibliography with their Gaia DR3 counterpart sources. Using the available photometry, interstellar extinction, literature spectroscopically derived temperatures or spectral types and parallax-derived distances from Gaia DR3, we fitted their Spectral Energy Distributions and we estimated their luminosities and circumstellar extinctions. When compared to models, luminosity values allowed us to disclose objects that are likely post-AGB stars from other target types. Their position on the HR diagram allows direct comparison with updated post-AGB evolutionary tracks and an estimation of their masses and evolutionary ages. Results. We obtained a sample of 69 reliable post-AGB candidates that meet our classification criteria, providing their coordinates, distances, effective temperature, total extinction, luminosity, mass, and evolutionary age. In addition, similar data for other stellar objects in our initial compilation, such as supergiant stars or young stellar objects, is provided. Conclusions. We have filtered out the data that have the best precision in parallaxes and distances to obtain more accurate luminosities, which allows us to classify with confidence the objects of the sample among different stellar phases. This allows us to provide a small but reliable sample of post-AGB objects. Derived mean evolutionary time and average mass values are in agreement with theoretical expectations.

astro-ph.SR↗