so mainly my main issue resumes in this
-) I have this high resolution spectrum (lets call it Model) and I want to lower it into a lower resolution (lets call it experimental), to compare both and see if the experimental behavior is in the model
-) I already know the experimental resolution I want to smooth into and also I know the Model high resolution Im working with
For that Im using both specutils gaussian smooth and also astropy convolve. And also tried using the FluxConservingResampler from specutils
For what I did, now, both convolution functions (the astropy and specutils one) work with a gaussian kernel
gaussian_smooth(Spectrum, stddev=sigma) for the specutils method
convolve(Spectrum.flux,kernel) for the astropy one
now, when looking the kernel in the astropy method I can also use the function
kernel=Gaussian1Dkernel(sigma) so at the end both of these use standard deviation/sigma.
Here is where my problem exists, for the sigma Ive been using the FWHM relationship
sigma = FWHM / (2 * np.sqrt(2 * np.log(2)))
And this FWHM is calculated using the experimental resolution.
After doing the convolution, yeah I can see the spectrum is indeed smoothed BUT what causes me confusion is
How do I know the spectrum is indeed smoothed to the resolution I want? is there a way to actually apply the experimental resolution into the smoothing? like ''I want to lower to this resolution''
Ive thought in doing a scaling factor, for example
model_resolution/experimental_resolution, but I really dont know how to apply it in code.
If anybody can help me that would be the best of the best Thank you very much
There is an expression for Resolving power R = wavelength/FWHM. In this case, if FWHM is fixed, the resolving power is higher for higher wavelengths.