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An attribute for Equivalent Width. It will have the same shape as lam_centers, amp, sigma, and gamma, but it will not be an nn.Parameter. Just a derived parameter.
How to compute it
Two options:
Analytically
This turns out to be impossible for Voigt profiles because the Lorenzian long tails integrate to infinity. However, we could simply provide a number of FWHMs out to which we integrate. This choice has the benefit of operating on a table of derived properties $\sigma$, $A$, $\gamma$.
Numerically
Simply torch.sum() the n_lines axis of flux2D, including the size of the pixels in angstroms. Probably np.trapz or equivalent would work better (or scipy quad).
The text was updated successfully, but these errors were encountered:
What we want
An attribute for Equivalent Width. It will have the same shape as
lam_centers
,amp
,sigma
, andgamma
, but it will not be annn.Parameter
. Just a derived parameter.How to compute it
Two options:
Analytically$\sigma$ , $A$ , $\gamma$ .
This turns out to be impossible for Voigt profiles because the Lorenzian long tails integrate to infinity. However, we could simply provide a number of FWHMs out to which we integrate. This choice has the benefit of operating on a table of derived properties
Numerically
Simply
torch.sum()
the n_lines axis offlux2D
, including the size of the pixels in angstroms. Probably np.trapz or equivalent would work better (or scipy quad).The text was updated successfully, but these errors were encountered: