I have successfully managed to get some spectra with the SA200. Although I have been able to do the basic processing (i.e. dark subtracion and wavelength calibration by applying the dispersion that is 9.78 for my setup) I am encountering difficulties when I have to divide the spectrum by the instrumental repsonse curve possibly because I have not created it properly. Can you let me know the right steps to create a solid respone curve with R Spec?
Can I submit the spectrum of a standard star to the AAVSO database without the flux calibration? By the way I am unable to submit the .fit file as I am getting an error message from the AAVSO website.
Cheers
Gianluca (RGN)
I cant help with RSpec as I don't use it but if you are having problems following the procedures shown in the videos on the RSpec website, perhaps the presentation
"Low Resolution Slitless Spectroscopy (Star Analyser)- Observing a fast transient of a T Tauri star. Producing spectra using a Star Analyer grating"
on my website here might help.
http://www.threehillsobservatory.co.uk/astro/spectroscopy_10.htm
(slides 5-33 missing out 14,24-26)
It shows the steps from raw images to wavelength and flux calibrated spectra using ISIS and Visual Spec but the procedure is the same for other software
Cheers
Robin
For best accuracy the reference star should be a hot star with a known spectrum, non variable (preferably main sequence) with low interstellar extinction at similar altitude to the target star. To help find suitable reference stars you might find Francois Teyssier's spreadsheet called "Reference Star Finder" downloadable from his website here
http://www.astronomie-amateur.fr/ProjetsSpectro0.html
This spreadsheet is also useful for other low resolution spectrographs eg ALPY,LISA etc
As far as I can tell, unlike other databases AAVSO do not test for flux calibration when validating spectra for their database (see the conversation here)
https://www.aavso.org/alf-ari-spectrum
but I would encourage observers to do this. It is not difficult to master and adds value to the data.
Cheers
Robin