SPInS, a pipeline for massive stellar parameter inference: A public
  Python tool to age-date, weigh, size up stars, and more
  Stellar parameters are required in a variety of contexts, ranging from the
characterisation of exoplanets to Galactic archaeology. Among them, the age of
stars cannot be directly measured, while the mass and radius can be measured in
some particular cases (binary systems, interferometry). Stellar ages, masses,
and radii have to be inferred from stellar evolution models by appropriate
techniques. We have designed a Python tool named SPInS. It takes a set of
photometric, spectroscopic, interferometric, and/or asteroseismic observational
constraints and, relying on a stellar model grid, provides the age, mass, and
radius of a star, among others, as well as error bars and correlations. We make
the tool available to the community via a dedicated website. SPInS uses a
Bayesian approach to find the PDF of stellar parameters from a set of classical
constraints. At the heart of the code is a MCMC solver coupled with
interpolation within a pre-computed stellar model grid. Priors can be
considered, such as the IMF or SFR. SPInS can characterise single stars or
coeval stars, such as members of binary systems or of stellar clusters. We
illustrate the capabilities of SPInS by studying stars that are spread over the
Hertzsprung-Russell diagram. We then validate the tool by inferring the ages
and masses of stars in several catalogues and by comparing them with literature
results. We show that in addition to the age and mass, SPInS can efficiently
provide derived quantities, such as the radius, surface gravity, and seismic
indices. We demonstrate that SPInS can age-date and characterise coeval stars
that share a common age and chemical composition. The SPInS tool will be very
helpful in preparing and interpreting the results of large-scale surveys, such
as the wealth of data expected or already provided by space missions, such as
Gaia, Kepler, TESS, and PLATO.
