arXiv subjects

Nayat Sánchez-Pi

Publications and source records attributed to Nayat Sánchez-Pi.

3 recordsLinked to original sources

arXiv2026

Comparing phylogenetic trees of stars with spectral graph distances

Phylogenetic trees offer a hierarchical representation of chemical similarities among stars, from which their evolutionary histories can, in principle, be reconstructed. Simulation-based work suggests that phylogenetic trees built under different chemical evolution scenarios carry distinguishable structural imprints, but whether existing methods can detect this signal in practice is unclear. We evaluate distance measures, both classical tree-based and graph-theoretical, for comparing phylogenetic trees, seeking measures robust to observational noise that separate trees built from populations with distinct chemical enrichment histories. We construct Neighbor-Joining trees from chemical abundance vectors of solar twins and of stars associated with the Milky Way (MW) disk, Gaia-Sausage-Enceladus (GSE), and the Sagittarius dwarf spheroidal galaxy (Sgr). Two benchmark tests assess stability under input perturbations within the same evolutionary history and discriminative power between distinct stellar populations used as proxies for different evolutionary histories. Tree metrics rapidly saturate under perturbations of order the typical uncertainty ($\approx2σ$, or $\approx0.05$ dex per element) and fail to distinguish trees when leaf sets differ. In contrast, spectral distances vary smoothly with perturbation amplitude and provide a label-invariant method for comparing trees built from disjoint sets of stars. Among the spectral measures evaluated, the Laplacian spectral distance shows the most consistent separation across population pairs, while the normalized variant is weakest. Applied to MW, GSE, and Sgr stars, spectral measures recover a distance ordering consistent with their known chemical enrichment histories. This framework also provides a route to comparing observed enrichment histories against simulations to identify those that best reproduce the observed tree structure.

astro-ph.GA↗

arXiv2025

Towards model-free stellar chemical abundances. Potential applications in the search for chemically peculiar stars in large spectroscopic surveys

Chemical abundance determinations from stellar spectra are challenged by observational noise, limitations in stellar models, and departures from simplifying assumptions. While traditional and supervised machine learning methods have made remarkable progress in estimating atmospheric parameters and chemical compositions within existing physical models, these factors still constrain our ability to fully exploit the vast data sets provided by modern spectroscopic surveys. We aim to develop a self-supervised, disentangled representation learning framework that extracts chemically meaningful features directly from spectra, without relying on externally imposed label catalogs. We build a variational autoencoder-based representation learning model with physics-inspired structure: multiple decoders each focus on spectral regions dominated by a particular element, enforcing that each latent dimension maps to a single abundance. To evaluate the potential application of our framework, we trained and validated the model on low-resolution, low signal-to-noise synthetic spectra focusing on $\rm [Fe/H]$, $\rm [C/Fe]$, and $\rm [α/Fe]$. We then demonstrate how the trained model can be used to flag stars as chemically enhanced or depleted in these abundances based on their position within the latent distribution. Our model successfully learns a representation of spectra whose axes correlate tightly with the target abundances ($r=0.92\pm0.01$ for $\rm [Fe/H]$, $r=0.92\pm0.01$ for $\rm [C/Fe]$, $r=0.82\pm0.02$ for $\rm [α/Fe]$). The disentangled representations provide a robust means to distinguish stars based on their chemical properties, offering an efficient and scalable solution for large spectroscopic surveys.

astro-ph.SR↗

arXiv2024

A baseline on the relation between chemical patterns and birth stellar cluster

The chemical composition of a star's atmosphere reflects the chemical composition of its birth environment. Therefore, it should be feasible to recognize stars born together that have scattered throughout the galaxy, solely based on their chemistry. This concept, known as "strong chemical tagging", is a major objective of spectroscopic studies, but has yet to yield the anticipated results. We assess the existence and the robustness of the relation between chemical abundances and birth place using known member stars of open clusters. We followed a supervised machine learning approach, using chemical abundances obtained from APOGEE DR17, observed open clusters as labels and different data preprocessing techniques. We found that open clusters can be recovered with any classifier and on data whose features are not carefully selected. In the sample with no field stars, we obtain an average accuracy of $75.2\%$ and we find that the prediction accuracy depends mostly on the uncertainties of the chemical abundances. When field stars outnumber the cluster members, the performance degrades. Our results show the difficulty of recovering birth clusters using chemistry alone, even in a supervised scenario. This clearly challenges the feasibility of strong chemical tagging. Nevertheless, including information about ages could potentially enhance the possibility of recovering birth clusters.

astro-ph.GA↗