Closed lea-hagen closed 5 years ago
Additional update from this PR: while working on this, we figured out that the normalization of the two lognormals (N1
and N2
) are not actually constrained by the current fitting procedure. (Larger N1
orN2
just increased the likelihood of the fit.) Instead, the MegaBEAST now fits for N12_ratio
, which is the ratio of the two N
s. Sometime in the future, we'll work through the math to also fit for the total predicted number of stars.
I've also added code (parameter_maps.py
) to create maps of the best-fit parameters.
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Now added plotting code (simulate_av_plots.py
) to compare the simulated A_V distributions with their assumed model and the best fit.
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@karllark this is finally ready to merge!
Merging.
This creates a simulated A_V dataset, in which the underlying A_V can be a single or double lognormal. Currently, the simulated A_V values have an error of 0.2 and a completeness of 1, which will need to be generalized in the future.
The inputs are the SED grid file, the parameters of the lognormal(s), and a label for the created files. The dimensions of the image and the number of stars per pixel are also optional inputs. The code creates all necessary data files (
lnp
,nstars
,noisemodel
) and an input file for the megabeast.Documentation is included.
Partially addresses #6.