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**: * Diego Bossini * Tiago L. Campante * William J. Chaplin * Hugo R. Coelho * Guy R. Davies * James S. Kuszlewicz * Martin W. Long * Mikkel N. Lund * Andrea Miglio Supplementary material ---------------------- * a more technical :download:`overview <./files/Overview.pdf>` of AIMS * a PDF version of this documentation may be downloaded :download:`here <./files/AIMS.pdf>` 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 :download:`here <../COPYING>` and should also be included in ``AIMS.tgz``