Quick question (I hope) - stacking multiple exposures in DSS

[BrendanC]

I've been thinking a lot about stacking in DSS recently.

Here's one of those thoughts.

Say I have 100 exposures of a galaxy at 60s, and another 100 at 240s.

I know that I could stack them all in one go in DSS, assuming I correctly allocate the calibration frames etc.

I also know that this would improve the SNR.

But, in doing so, how would the maths work for the fainter stuff in the 240s exposures? I have a sneaking feeling it would average it out from what it can see in the 60s exposures. I tend use to use Media Kappa Sigma for my lights, and I know that there's an algorithm that's supposed to be ideal for this, called Entropy Weighted Average, but I've tried that without much success.

The alternative approach is to stack them separately then combine in Photoshop using masks and layers. But then, I wouldn't benefit from the improvement in SNR that way. The noise would just be greater in each stack.

So what's the best way to do this? Is it, as is always the case, 'it depends'?

I've been through lots of posts about this, both here and elsewhere, and I don't see anyone addressing the issue of potentially losing data from the 240s exposures by stacking them with the 60s exposures.

Thanks, Brendan

Edited May 9, 2021 by BrendanC

[BrendanC]

I know this question has been asked quite a lot, but as I say, I haven't seen anyone cover the issue of whether data would be LOST using this method!

[JSeaman]

I stack individually and combine but you should have a try with the weighted entropy algorithm which is made for this (a bit like HDR on an SLR)

[BrendanC]

Thanks, I'll give it another go if you think it'll work, but I've never been able to get anything decent out of it! 

I'm just wondering whether I'm correct about losing data if I use the Kappa Sigma stacking method. I think the maths is such that I would lose data, and I've seen people use layers and masks instead. If I'm wrong about losing data then I'll carry on as before. 

Thanks for responding.

Edited May 10, 2021 by BrendanC