MegaBEAST Motivation

The overarching motivation for the MegaBEAST is to fit for the ensemble stellar and dust extinction parameters of observations of a set of stars in a galaxy. One key part of the MegaBEAST ensemble model is to include the effects of completeness from the observations where these effects are calculated from Artificial Star Test (ASTs). Example ensemble stellar parameters are initial mass function (IMF) and star formation history (SFH). Example ensemble dust extinction parameters are maps of dust column A(V) and average grain size R(V). The MegaBEAST is a hierarchical Bayesian model that uses the results of the BEAST Bayesian model.

The BEAST provides fits to photometric SEDs of individual stars giving stellar and dust extinction parameters. The BEAST results include various statistics for the BEAST primary and derived parameters from best fits to the full n-dimensional posterior. The BEAST does not account for completeness as it only fits individual stars.

The MegaBEAST takes advantage of all the BEAST work by using the BEAST results for each star (specifically the n-dimensional posterior). Thus, the MegaBEAST needs to be able to predict the stellar and dust properties of ensembles of extinguished stars in the BEAST parameters only. The MegaBEAST does need to include the observational effect of a finite depth survey of stars, hence the ensemble model includes a model of the completeness.


The goal of the MegaBEAST is to derive maps of stellar and dust extinction parameters from multi-band resolved star surveys of galaxies.

The mapped ensemble parameters include (but are not limited to):

  • star formation history
  • initial mass function
  • mass-metallicity relationship
  • column A(V)
  • average grain size R(V)
  • grain composition measure f_A
  • galaxy distance
  • galaxy depth