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