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``