# Ensemble age inversions for large spectroscopic surveys

## TL;DR

This paper introduces an age inversion method that reconstructs the true stellar age distribution from probability density functions, enabling better understanding of galactic evolution from large spectroscopic surveys.

## Contribution

The paper presents a novel age inversion technique that models the log(age) distribution as a weighted sum of mono-age populations, validated on simulated and real survey data.

## Key findings

- Successfully recovers true age distributions from simulated data.
- Effectively reproduces age-metallicity trends in large surveys.
- Applicable to samples with over 1,000 stars.

## Abstract

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.