Project Summary

Description

Name: “Stellar Parameters INferred Systematically” (SPInS)

History:

• SPInS is a spin-off of the AIMS code (Rendle et al. 2019) in which the seismic part has been removed (thus reducing the computational cost)

Goals:

• estimate stellar parameters and credible intervals/error bars

• chose a representative set or sample of reference models

Inputs:

• classic constraints (Teff, L, …), averaged seismic constraints (\(\Delta \nu\), \(\delta \nu\), \(\nu_{\mathrm{max}}\), averaged ratios, period separations …), and associated probability distributions

Requirements:

• a precalculated grid of stellar models including:

  • parameters for the model (M, R, Teff, age, averaged seismic parameters, …)

Methodology:

• applies an MCMC algorithm based on the python package emcee. Relevant articles include:

  • Bazot et al. (2012, MNRAS 427, 1847)

  • Gruberbauer et al. (2012, ApJ 749, 109)

• interpolates within the grid of models using Delaunay tessellation (from the scipy.spatial package which is based on the Qhull library)

• modular approach: facilitates including contributions from different people

Authors

• Daniel R. Reese

• Yveline Lebreton

Supplementary material

• a more technical overview of SPInS

• a PDF version of this documentation may be downloaded here

Copyright information

• the SPInS project is distributed under the terms of the GNU General Public License, version 3

• a copy of of this license may be downloaded here and should also be included in SPInS.tgz