arXiv subjects

Theosamuele Signor

Publications and source records attributed to Theosamuele Signor.

5 recordsLinked to original sources

arXiv2026

Disentangling chemical evolution histories with phylogenetic trees

Chemical abundances encode the fossil record of galaxy evolution in a complex and diverse way that requires innovative approaches to reconstruct galactic histories. We investigate the power of using phylogenetic methods to disentangle different evolutionary pathways in analytical chemical evolution models. We ran 1024 one-zone chemical evolution models using flexCE. The resulting chemical abundances are combined with those of two fiducial models, mw-fid and dw-fid, and then used both to determine which combinations produce two-branched phylogenetic trees, as well as how purely these trees split the two input models. We used random forests and Shapley analysis to predict which model combinations return well-separated trees and explain which input parameters are most important for this. We also studied the abundance patterns, as well as star formation rates, mass accumulation, and branch lengths. We found that {\eta}, the mass-loading outflow parameter in flexCE, had the largest impact in separating models into separate branches, due to its importance in driving the chemical enrichment rates and total abundances. Star formation rates and mass accumulation had some impact on {\eta}, but no direct relation between these quantities and the abundances was found. We also found that branches connected through the most metal rich tips in our trees, which is opposite to how phylogenetic trees connect in biological systems. Phylogenetic trees help to reconstruct histories when there is information that is inherited between generations, which is the case of the chemical elements in galaxy evolution. Branch topologies can provide information about the rates of evolutionary change of the various populations, and the connection between branches also contains information about their shared history. This work brings us a step further understanding galaxy evolution through cross-disciplinary research.

astro-ph.GA↗

arXiv2026

Reconstructing chemical enrichment pathways in disc galaxies: A phylogenetic approach

Phylogenetic methods, traditionally used in biology to trace the evolutionary relationships among species, are emerging as a powerful framework to reconstruct evolutionary processes in galaxies from chemical information. We apply galactic phylogenetics to study the chemical evolution of stellar populations in distinct regions of a simulated disc galaxy, assessing its capability to unveil assembly histories. We used a high-resolution simulation that follows the chemical enrichment of an isolated disc galaxy, by different stellar progenitors. We track gas particles as they turn into stars and inherit their parent gas chemical composition. Target particles are selected to store the chemical history of each chemical element considered in the simulation. Two regions were analysed: an inner ring, influenced by early bar-driven inflows, and an outer ring, shaped by spiral arms. We built phylogenetic trees for stellar populations in each region and quantified their structure using the Corrected Colless index, a standard metric of tree balance used in biology. The inner ring tree reveals a compact clade of old stars enriched by rapid SNII feedback, followed by a hierarchical sequence with increasing SNIa and AGB contributions. In contrast, the outer ring exhibits more symmetric, caterpillar-like trees with smoother abundance gradients, consistent with more prolonged star formation and efficient local mixing. Chemical enrichment rates corroborate these trends, showing fast early enrichment in the inner ring and gradual, spatially extended enrichment in the outer disc. The structural indices differ significantly between the two regions and converge robustly even for modest stellar samples (NSSP = 100). Galactic phylogenetics provides a novel and complementary tool to decode the fossil record of galaxies.

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↗

arXiv2024

Assembling a high-precision abundance catalogue of solar twins in GALAH for phylogenetic studies

Stellar chemical abundances have proved themselves a key source of information for understanding the evolution of the Milky Way, and the scale of major stellar surveys such as GALAH have massively increased the amount of chemical data available. However, progress is hampered by the level of precision in chemical abundance data as well as the visualization methods for comparing the multidimensional outputs of chemical evolution models to stellar abundance data. Machine learning methods have greatly improved the former; while the application of tree-building or phylogenetic methods borrowed from biology are beginning to show promise with the latter. Here we analyse a sample of GALAH solar twins to address these issues. We apply The Cannon algorithm to generate a catalogue of about 40,000 solar twins with 14 high precision abundances which we use to perform a phylogenetic analysis on a selection of stars that have two different ranges of eccentricities. From our analyses we are able to find a group with mostly stars on circular orbits and some old stars with eccentric orbits whose age-[Y/Mg] relation agrees remarkably well with the chemical clocks published by previous high precision abundance studies. Our results show the power of combining survey data with machine learning and phylogenetics to reconstruct the history of the Milky Way.

astro-ph.GA↗