SPInS, a pipeline for massive stellar parameter inference

Abstract

Context. 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 (e.g. binary systems, interferometry). More generally, stellar ages, masses, and radii have to be inferred from stellar evolution models by appropriate techniques. Aims. 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. Methods. SPInS uses a Bayesian approach to find the probability distribution function of stellar parameters from a set of classical constraints. At the heart of the code is a Markov chain Monte Carlo solver coupled with interpolation within a pre-computed stellar model grid. Priors can be considered, such as the initial mass function or stellar formation rate. SPInS can characterise single stars or coeval stars, such as members of binary systems or of stellar clusters. Results. We first 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. Conclusions. 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.

Gaia Data Release 3: Apsis II -- Stellar Parameters

“…One can trace most differences compared with the literature to the different input T eff and L estimates. To support this statement, we compared FLAME's ages to the ones we obtained with the SPinS public code (Lebreton & Reese 2020). We generated random sets of 600 stars with the SPinS code using the same Gaia DR3 APs that FLAME uses and compared the output ages in four different magnitude intervals.…”

CORALIE radial-velocity search for companions around evolved stars (CASCADES)

“…A color cut-off bias could also be part of the explanation for this effect, excluding the low-log-g stars with high metallicity and the high-log-g stars with low metallicity, as discussed in Mortier et al (2013); Adibekyan (2019). We also obtained stellar masses for the sample with global parameters fitting, using evolutionary tracks from Pietrinferni et al (2004), using the SPInS software (Lebreton & Reese 2020). The distribution ranges approximately from 0.75 to 4 M , with a maximum around 2 M .…”

CORALIE radial-velocity search for companions around evolved stars (CASCADES). I. Sample definition and first results: Three new planets orbiting giant stars

“…A color cut-off bias could also be part of the explanation for this effect, excluding the low-logg stars with high metallicity and the high-log-g stars with low metallicity, as discussed in Mortier et al (2013); Adibekyan (2019). We also obtained stellar masses for the sample with global parameters fitting, using evolutionary tracks from Pietrinferni et al ( 2004), using the SPInS software (Lebreton & Reese 2020).…”

“…7, together with the stellar evolutionary tracks at solar metallicity of Pietrinferni et al ( 2004) 6 . Those were used to estimate the masses of our stars using the SPInS software (Lebreton & Reese 2020) 7 . The approach compares the luminosity, effective temperature, logarithm of surface gravity, and [Fe/H] of individual objects to theoretical evolutionary tracks and accounts for the observational errors in these four quantities.…”