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Your 'Combined' SGL wisdom, please...!


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Folks, what is considered the best way of combining data sets from different camera/scope combinations, these days...?

Since changing camera, I've revisited old targets to not only see how the new gear stands up to the old, but also to compare how my guiding and image acquisition has improved over the years.

So, as an example, I have decent (in focus, clean, etc), Ha data sets of the California Nebula from:

FSQ106ED/Focal Reducer/SBIG 8300m - 2.88" arch sec/pixel, 7nm Baader.  23x 600sec

and

FSQ106ED/Atik 16200m - 2.33" arch sec/pixel, 3nm Chroma.  20x 1200sec

Using APP (or Nebulosity 4,) should I (once both data sets have been calibrated with their own respective flat/dark flats/darks), either:

1) combine the 8300m and 16200m light frame sets individually and then stack the master 8300 and 16200 light sets..?

or...

2) stack all the calibrated lights in one go..?

And in the correct, or in each case above, is there a preferred way of combining (the type of sigma rejection, or weighting, etc...)...?

I suppose this is one for Vlaiv (!), but I await your 'combined' thoughts!

Thanks in advance,

Damian

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This is what I would do:

- calibrate each set of lights with their own calibration subs

- choose what resolution you'll work with - either 2.88"/px or 2.33"/px and register all subs against sub from respective set. I'd advise you to go with 2.88"/px rather than 2.33"/px as downsampling will have edge in sharpness and definition over up sampling

- use smart stacking algorithm that is capable of stacking two sets of subs having very different SNR

Problem is of course with point number 3 :D

Most stacking algorithms assign single weight for subs - and that is not good approach because there is no single SNR value in sub. In fact, every pixel will have some signal and some associated noise and ratio of these two quantities will be very different (just think nebulosity and background - one contains signal and other does not, so no way they can have same SNR but they do belong to same sub).

Solution to point three is - use algorithm that I developed that is capable of using subs with very different SNRs across the board :D. Read about it here:

If you are interested, I'll share ImageJ plugins needed to do the stacking and explain how to use them.

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