FAMA: An automatic code for stellar parameter and abundance determination

Laura Magrini, Sofia Randich, Eileen Friel, Lorenzo Spina, Heather Jacobson, Tristan Cantat-Gaudin, Paolo Donati, Roberto Baglioni, Enrico Maiorca, Angela Bragaglia, Rosanna Sordo, Antonella Vallenari

Research output: Contribution to journalArticleResearchpeer-review

30 Citations (Scopus)


Context. The large amount of spectra obtained during the epoch of extensive spectroscopic surveys of Galactic stars needs the development of automatic procedures to derive their atmospheric parameters and individual element abundances. Aims. Starting from the widely-used code MOOG by C. Sneden, we have developed a new procedure to determine atmospheric parameters and abundances in a fully automatic way. The code FAMA (Fast Automatic MOOG Analysis) is presented describing its approach to derive atmospheric stellar parameters and element abundances. The code, freely distributed, is written in Perl and can be used on different platforms. Methods. The aim of FAMA is to render the computation of the atmospheric parameters and abundances of a large number of stars using measurements of equivalent widths (EWs) as automatic and as independent of any subjective approach as possible. It is based on the simultaneous search for three equilibria: excitation equilibrium, ionization balance, and the relationship between log n(Fe i) and the reduced EWs. FAMA also evaluates the statistical errors on individual element abundances and errors due to the uncertainties in the stellar parameters. The convergence criteria are not fixed "a priori" but are based on the quality of the spectra. Results. In this paper we present tests performed on the solar spectrum EWs that assess the method's dependency on the initial parameters and we analyze a sample of stars observed in Galactic open and globular clusters.

Original languageEnglish
Article numberA38
JournalAstronomy & Astrophysics
Publication statusPublished - 9 Oct 2013
Externally publishedYes


  • Galaxy: abundances
  • Methods: data analysis
  • Open clusters and associations: general
  • Stars: abundances
  • Surveys
  • Techniques: spectroscopic

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