Context. Galactic astrophysics is now in the process of building a multi-dimensional map of the Galaxy. For such a map, stellar ages are an essential ingredient. Ages are measured only indirectly however, by comparing observational data with models. It is often difficult to provide a single age value for a given star, as several non-overlapping solutions are possible.
Aims: We aim at recovering the underlying log(age) distribution from the measured log(age) probability density function for an arbitrary set of stars.
Methods: We build an age inversion method, namely we represent the measured log(age) probability density function as a weighted sum of probability density functions of mono-age populations. Weights in that sum give the underlying log(age) distribution. Mono-age populations are simulated so that the distribution of stars on the log g-[Fe/H] plane is close to that of the observed sample.
Results: We tested the age inversion method on simulated data, demonstrating that it is capable of properly recovering the true log(age) distribution for a large (N > 103) sample of stars. The method was further applied to large public spectroscopic surveys. For RAVE-on, LAMOST and APOGEE we also applied age inversion to mono-metallicity samples, successfully recovering age-metallicity trends present in higher-precision APOGEE data and chemical evolution models.
Conclusions: We conclude that applying an age inversion method as presented in this work is necessary to recover the underlying age distribution of a large (N > 103) set of stars. These age distributions can be used to explore age-metallicity relations, for instance.
Astronomy and Astrophysics
- Pub Date:
- September 2019
- methods: statistical;
- stars: fundamental parameters;
- Galaxy: stellar content;
- Astrophysics - Astrophysics of Galaxies;
- Astrophysics - Solar and Stellar Astrophysics
- 15 pages, 15 figures