arXiv subjects

Leda Berni

Publications and source records attributed to Leda Berni.

5 recordsLinked to original sources

arXiv2026

Tracing the early Milky Way thin disc with the Gaia-ESO Survey

The origin of metal-poor stars on thin-disc-like orbits remains an open question in Galactic archaeology and provides important constraints on the earliest phases of Milky Way disc formation. We aim to identify and characterise metal-poor stars with thin-disc-like kinematics observed in the Gaia-ESO Survey, using Gaia DR3 orbital parameters, spectroscopic information, isochrone ages, and detailed chemical abundances. Out of 1784 turn-off stars, we identify one metal-poor candidate with [Fe/H] = -1.38 on a dynamically cold, prograde orbit, with low eccentricity, high azimuthal velocity ($V_\phi$ approximately 220 km/s), large angular momentum and low vertical action. Chemically, however, it is clearly distinct from the canonical thin disc: it is $\alpha$-enhanced, with [Mg/Fe] = +0.58 and [$\alpha$/Fe] = +0.48, shows a low [Y/Mg] = -0.58 ratio, and lies on the metal-poor sequence in the [Al/Fe]-[Mg/Mn] plane. The star also has an old age estimate, with a most probable value of about 10.2 Gyr. Its properties suggest that it is not a typical member of the canonical thin disc. Instead, it may be associated with the old thin disc, the metal-weak thick disc, or prograde halo-related populations. This object provides a useful benchmark for studying early metal-poor populations on disc-like orbits.

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arXiv2026

HRMOS: A High-Resolution Multi-Object Spectrograph for the VLT

This White Paper presents the scientific rationale and instrument concept for HRMOS (High-Resolution Multi-Object Spectrograph), a next-generation instrument proposed for the ESO Very Large Telescope within the VLT 2030 roadmap. Current and planned facilities offer either multi-object spectroscopy or ultra-high spectral resolution, but not both. HRMOS fills this gap by combining very high spectral resolution, multi-object capability, and radial-velocity stability, enabling transformative studies in Galactic and extragalactic astrophysics. The baseline design provides a resolving power of R = 80000, radial-velocity precision of 10 m s-1 (goal: 5 m s-1), simultaneous observations of 50-60 targets, and broad optical coverage down to 385 nm. These capabilities enable precise measurements of elemental abundances, isotopic ratios, line profiles, and radial velocities for large stellar samples, including crowded fields, star clusters, the Galactic bulge, and nearby dwarf galaxies. HRMOS will address key questions on the age of the oldest stellar populations through nucleocosmochronology, the formation and survival of planetary systems, the assembly history of the Milky Way and satellites, the origin of the heaviest elements, stellar evolution, and the chemical and dynamical properties of the interstellar and circumgalactic medium. It will bridge large spectroscopic surveys and the next generation of extremely large telescopes, with strong synergies with 4MOST, Gaia, TESS, PLATO, the proposed Haydn mission, and future ELT instruments. Building on VLT/FLAMES heritage, HRMOS represents a strategic investment for European astronomy in the 2030s.

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arXiv2025

Deep chemical tagging -- Identifying open clusters and moving groups in chemical space with graph attention networks

Reconstructing the formation history of the Milky Way is hindered by stellar migration, which erases kinematic birth signatures. In contrast, stellar chemical abundances remain stable and can be used to trace stars back to their birth environments through chemical tagging. This study aims to improve chemical tagging by developing a method that leverages kinematic and age information to enhance clustering in chemical space, while remaining grounded in chemistry. We implement a graph attention auto-encoder that encodes stars as nodes with chemical features and connects them via edges based on orbital similarity and age. The network learns an ``informed'' chemical space that accentuates coherent groupings.Applied to $\sim$47,000 APOGEE thin disk stars, the method identifies 282 stellar groups. Among them, five out of six open clusters are successfully recovered. Other groups align with the known moving groups Arch/Hat, Sirius, Hyades, and Hercules. Our approach enables chemically grounded yet kinematically and age informed chemical tagging. It significantly improves the identification of coherent stellar populations, offering a framework for future large-scale stellar archaeology efforts.

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arXiv2024

Searching for chemo-kinematic structures in the Milky Way halo with deep clustering algorithms

According to the lambda CDM scenario, galaxies are formed through the hierarchical accretion of building blocks. Our Galaxy is a privileged place to look for the remnants of accretion events through the study of the chemical and kinematic properties of its halo stellar populations. Due to its low density, the stellar halo holds the most favorable conditions for chemical tagging. However, chemical tagging alone often yields weak results due to both uncertainties in chemical abundances and to overlapping chemical properties among different populations. To overcome this problem, the use of chemical and kinematic properties can be combined. In this Thesis, we developed a machine learning algorithm, named the CREEK, which combines orbital and chemical properties of halo stars observed by two large public spectroscopic surveys, Gaia-ESO and APOGEE. The CREEK operates as follows: 1)Data selection: We selected halo stars from the APOGEE and Gaia-ESO surveys based both on their velocity and metallicity and we computed their orbital parameters. 2)Using kinematics: The selected data were passed to a Siamese Neural Network that established links between stars based on their kinematic similarities. 3)Using chemistry: The graph was passed through a Graph Neural Network (GNN) auto-encoder that took as input the selected abundances. The abundances were chosen to maximize homogeneity within stars from the same cluster while ensuring distinctiveness between stars from different clusters. Additionally, we prioritised elements with smallest errors. The GNN auto-encoder computed a mean of the abundances of all connected stars, weighted on the number of links of each star and mapped the chemical space into a more efficient representation in the latent space. 4)Recovering structures: Finally, OPTICS was applied to the latent space, providing groups based on the chemical similarities of the stars.

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arXiv2024

The Wide-field Spectroscopic Telescope (WST) Science White Paper

The Wide-field Spectroscopic Telescope (WST) is proposed as a new facility dedicated to the efficient delivery of spectroscopic surveys. This white paper summarises the initial concept as well as the corresponding science cases. WST will feature simultaneous operation of a large field-of-view (3 sq. degree), a high multiplex (20,000) multi-object spectrograph (MOS) and a giant 3x3 sq. arcmin integral field spectrograph (IFS). In scientific capability these requirements place WST far ahead of existing and planned facilities. Given the current investment in deep imaging surveys and noting the diagnostic power of spectroscopy, WST will fill a crucial gap in astronomical capability and work synergistically with future ground and space-based facilities. This white paper shows that WST can address outstanding scientific questions in the areas of cosmology; galaxy assembly, evolution, and enrichment, including our own Milky Way; origin of stars and planets; time domain and multi-messenger astrophysics. WST's uniquely rich dataset will deliver unforeseen discoveries in many of these areas. The WST Science Team (already including more than 500 scientists worldwide) is open to the all astronomical community. To register in the WST Science Team please visit https://www.wstelescope.com/for-scientists/participate

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