Project Summary

Description

Name: “Asteroseismic Inference on a Massive Scale” (AIMS)

Goals:

• estimate stellar parameters and credible intervals/error bars

• chose a representative set or sample of reference models

• be computationally efficient

Inputs:

• classic constraints and error bars (Teff, L, …)

• seismic constraints and error bars (individual frequencies)

Requirements:

• a precalculated grid of models including:

  • the models themselves

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

  • theoretical frequency spectra for the models

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

Contributors

Author:

• Daniel R. Reese

Comments, corrections, suggestions, and contributions:

• Diego Bossini

• Gael Buldgen

• Tiago L. Campante

• William J. Chaplin

• Hugo R. Coelho

• Guy R. Davies

• Benoît D. C. P. Herbert

• James S. Kuszlewicz

• Yveline Lebreton

• Martin W. Long

• Mikkel N. Lund

• Andrea Miglio

• Ben Rendle

Supplementary material

• a more technical overview of AIMS

• a PDF version of this documentation may be downloaded here

Copyright information

• the AIMS 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 AIMS.tgz