Ensemble age inversions for large spectroscopic surveys. (arXiv:1908.04548v1 [astro-ph.GA])

<a href="http://arxiv.org/find/astro-ph/1/au:+Mints_A/0/1/0/all/0/1">Alexey Mints</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Hekker_S/0/1/0/all/0/1">Saskia Hekker</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Minchev_I/0/1/0/all/0/1">Ivan Minchev</a>

Galactic astrophysics is now in the process of building a multi-dimensional

map of the Galaxy. For such a map, stellar ages are the essential ingredient.

Ages are however measured only indirectly 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. We aim at recovering the

underlying log(age) distribution from the measured log(age) probability density

function for an arbitrary set of stars. 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. 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. 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 for instance age-metallicity relations.

Galactic astrophysics is now in the process of building a multi-dimensional

map of the Galaxy. For such a map, stellar ages are the essential ingredient.

Ages are however measured only indirectly 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. We aim at recovering the

underlying log(age) distribution from the measured log(age) probability density

function for an arbitrary set of stars. 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. 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. 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 for instance age-metallicity relations.

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