activity
20032025
most citedThe surprising magnetic topology of tauSco: fossil remnant or dynamo output?

496 citations · 3.7k across the 54 of their papers we have counts for

collaborators

54 papers

astro-ph.SR2025

Stable magnetic fields and changing starspots on Vega -- An ultra-deep decadal survey at Pic du Midi and OHP

T. Böhm, M. Holschneider, P. Petit +4

Monitoring magnetic and activity variations in A- and B-type stars with ultra-weak magnetic fields is essential to understand the origin and evolution of these fields in this regio…

astro-ph.IM2023★ 4 cited

Full non-LTE spectral line formation II. Two-distribution radiation transfer with coherent scattering in the atom's frame

F. Paletou, M. Sampoorna, C. Peymirat

In the present article, we discuss a numerical method of solution for the so-called "full non-LTE" radiation transfer problem, basic formalism of which was revisited by Paletou & P…

astro-ph.SR2022

Deep Learning application for stellar parameters determination: II- Application to observed spectra of AFGK stars

Marwan Gebran, Frédéric Paletou, Ian Bentley +2

In this follow-up paper, we investigate the use of Convolutional Neural Network for deriving stellar parameters from observed spectra. Using hyperparameters determined previously,…

astro-ph.SR2022★ 133 cited

The Gaia-ESO Public Spectroscopic Survey: Motivation, implementation, GIRAFFE data processing, analysis, and final data products

G. Gilmore, S. Randich, C. C. Worley +165

The Gaia-ESO Public Spectroscopic Survey is an ambitious project designed to obtain astrophysical parameters and elemental abundances for 100,000 stars, including large representat…

astro-ph.GA2022★ 188 cited

The Gaia-ESO Public Spectroscopic Survey: Implementation, data products, open cluster survey, science, and legacy

S. Randich, G. Gilmore, L. Magrini +161

In the last 15 years different ground-based spectroscopic surveys have been started (and completed) with the general aim of delivering stellar parameters and elemental abundances f…

astro-ph.IM2022

Deep Learning application for stellar parameters determination: I- Constraining the hyperparameters

Marwan Gebran, Kathleen Connick, Hikmat Farhat +2

Machine Learning is an efficient method for analyzing and interpreting the increasing amount of astronomical data that is available. In this study, we show, a pedagogical approach…