I am starting to play with a way of generating photometry for the SSPs. For now I have implemented a simple method that takes as input a list of filters and generates the photometry (stored in the form of flux density). This is currently crucial for the next steps in terms of SED fitting using photometry (+spectra).
This to keep in mind:
When including dust extinction, this should be done prior calling this method.
We can think about ways to extinguish L_lambda.
For now, every time I am changing extinction I create a new copy of the SSP (this is an additional copy method that I've included). For LR spectra this is more or less okey (~ms), but in the case of HR spectra it is quite slow.
Probably you do want to use any of this methods because you create fixed grids outside SSP.
The most important aspects to think about are. Some wild thoughts while attending a talk:
Our models only use L_lambda for computing the final SED. If, for example, we want to generate both spectra and photometric SEDs this needs to be revised.
Alternatively, we could generate two versions of the SSPs, one with the spectra and another one with the photometry and the model is called twice.
Alt-alternative, the model has an intermediate method that returns not the SED, but the weights that are used to generate both the spectra and the photometry.
I am starting to play with a way of generating photometry for the SSPs. For now I have implemented a simple method that takes as input a list of filters and generates the photometry (stored in the form of flux density). This is currently crucial for the next steps in terms of SED fitting using photometry (+spectra).
This to keep in mind:
L_lambda.copymethod that I've included). For LR spectra this is more or less okey (~ms), but in the case of HR spectra it is quite slow.The most important aspects to think about are. Some wild thoughts while attending a talk:
L_lambdafor computing the final SED. If, for example, we want to generate both spectra and photometric SEDs this needs to be revised.