35 citations · 109 across the 8 of their papers we have counts for
8 papers
Observational constraints on the origin of the elements. X. Combining NLTE and machine learning for chemical diagnostics of 4 million stars in the 4MIDABLE-HR survey
Nicholas Storm, Maria Bergemann, Tomasz Różański +9
We present the 4MOST-HR resolution Non-Local Thermal Equilibrium (NLTE) Payne artificial neural network (ANN), trained on new FGK spectra with 16 elements computed in NL…
The role of radial migration in open cluster and field star populations with Gaia dr3
Carlos Viscasillas Vázquez, Laura Magrini, Lorenzo Spina +4
The survival time of a star cluster depends on its total mass, density, and thus size, as well as on the environment in which it was born and in which lies. Its dynamical evolution…
Beyond Gaia DR3: Tracing the [/M]-[M/H] bimodality from the inner to the outer Milky Way disc with Gaia-RVS and convolutional neural networks
G. Guiglion, S. Nepal, C. Chiappini +17
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, on…
The Gaia-ESO Survey: Probing the lithium abundances in old metal-rich dwarf stars in the Solar vicinity
M. L. L. Dantas, G. Guiglion, R. Smiljanic +14
We test a scenario in which radial migration could affect the Li abundance pattern of dwarf stars in the solar neighbourhood. This may confirm that the Li abundance in these stars…
The Gaia-ESO Survey: Old super-metal-rich visitors from the inner Galaxy
M. L. L. Dantas, R. Smiljanic, R. Boesso +12
We report the identification of a set of old super metal-rich dwarf stars with orbits of low eccentricity that reach a maximum height from the Galactic plane between ~0.5-1.5 kpc.…
The Gaia-ESO Survey: Chemical evolution of Mg and Al in the Milky Way with Machine-Learning
M. Ambrosch, G. Guiglion, Š. Mikolaitis +14
We aim to prepare the machine-learning ground for the next generation of spectroscopic surveys, such as 4MOST and WEAVE. Our goal is to show that convolutional neural networks can…
The Gaia-ESO Survey: Chemical tagging in the thin disk. Open clusters blindly recovered in the elemental abundance space
L. Spina, L. Magrini, G. G. Sacco +14
The chemical makeup of a star provides the fossil information of the environment where it formed. Under this premise, it should be possible to use chemical abundances to tag stars…
The Gaia-ESO Survey: Galactic evolution of lithium at high metallicity
S. Randich, L. Pasquini, E. Franciosini +20
Reconstructing the Galactic evolution of lithium (Li) is the main tool used to constrain the source(s) of Li enrichment in the Galaxy. Recent results have suggested a decline in Li…