BEAST-Fitting / megabeast

Hierarchical Bayesian Model for Ensembles of Dust Extinguished Stellar Populations
http://megabeast.readthedocs.io/
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Full nD simulations #51

Open karllark opened 4 years ago

karllark commented 4 years ago

(this is an issue to put down ideas - useful before and during the actual coding)

To test the megabeast, need to have simulations that fully test the different parameters of the ensemble model. The simulations are for megabeast ensemble parameters (e.g., IMF slope, Av distribution). There is code currently to simulate A(V) ensemble models and it needs to be updated/generalized.

To start with, the simulations should be done with easy to represent uncertainties on the beast parameters with the beast pPDFs simulated from the physicsgrid. Later, the simulations can be made more realistic by simulating stars with fluxes and uncertainties using the beast code to do this simulation. The latter simulation is already implemented in the beast. Maybe it would be better to just do the more realistic simulations now. That's where we need to be in the end anyway.

One idea it to use the prior model in the beast as this is the ensemble model for the megabeast. The megabeast ensemble model can be specified using the same dictionary structure as the beast prior model.

The megabeast simulation code would need to (for a single pixel):

  1. calculate the beast prior model based on the megabeast ensemble model based on a beast physics modelgrid
  2. create a new physicsgrid with the new weights based on the ensemble model
  3. use the beast simulation observations code to generate a simulated observed catalog
  4. run the beast on the simulated catalog and the original beast physicsgrid model for a specified number of stars
  5. run the megabeast on the beast results and solve for the ensemble model
  6. compare the megabeast solution to the input megabeast ensemble model