English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Galactic astrophysics is now in the process of building a multi-dimensionalmap of the Galaxy. For such a map, stellar ages are the essential ingredient.Ages are however measured only indirectly by comparing observational data withmodels. It is often difficult to provide a single age value for a given star,as several non-overlapping solutions are possible. We aim at recovering theunderlying log(age) distribution from the measured log(age) probability densityfunction for an arbitrary set of stars. We build an age inversion method,namely, we represent the measured log(age) probability density function as aweighted sum of probability density functions of mono-age populations. Weightsin that sum give the underlying log(age) distribution. Mono-age populations aresimulated so that the distribution of stars on the log g-[Fe/H] plane is closeto that of the observed sample. We tested the age inversion method on simulateddata, demonstrating that it is capable of properly recovering the true log(age)distribution for a large (N > 103) sample of stars. The method was furtherapplied to large public spectroscopic surveys. For RAVE-on, LAMOST and APOGEEwe also applied age inversion to mono-metallicity samples, successfullyrecovering age-metallicity trends present in higher-precision APOGEE data andchemical evolution models. We conclude that applying an age inversion method aspresented in this work is necessary to recover the underlying age distributionof a large (N > 103 ) set of stars. These age distributions can be used toexplore for instance age-metallicity relations.

Ensemble age inversions for large spectroscopic surveys