Combining data from two nights - shorter subs and warmer camera sensor, any point?

[Lehtojj]

Hi all,

Apologies for the long and confusing title, I'm a bit confused with my narrowband data and need a little help.

I have collected data of the Bubble Nebula in two different nights. From the second night I have 15 x 300s Ha, 15 x 300s Oiii and 15 x 300s Sii subs taken with Atik One 6.0 cooled down to -20C. 

The first night was a bit more cloudy and I was testing my setup and guiding for the first time properly, but I managed to gather about 10 x 240s Ha and 8 x 240s Oiii, camera cooled down to -10C.

So, is it worth combining the data? Do I gain anything from it since the subs are shorter and the camera sensor is warmer? More data is more data, but are the subs that much more noisy, or does it make any significant difference really? And if so, how should I combine the data? I'm using DSS for stacking and Photoshop for processing Thanks in advance.

Cheers,

J

 

Edited January 26, 2020 by Lehtojj
Added another question

[vlaiv]

It is certainly worth combining the data in a proper way.

Unfortunately it is a bit complicated topic and DSS does not have option to combine data in such way.

Just combining them as if they were same SNR subs can result in improvement but can also result in worse data. Actual result depends on how different SNR is between the subs, and to make matters worse - there is no single SNR value per sub - each pixel in fact has its own SNR value.

There are couple of things that you can do to address this issue:

1. Stack better subs to one stack and then stack all subs to other stack - inspect stacks to determine which one is better looking - and use that one.

2. If you have license of PixInsight - use that as it has weighted average stacking (not ideal, but better than equal weights stacking)

3. Make two stacks - one of good subs and one of poor subs and then try different weighted combining of those two stacks (simple image arithmetic - 0.8 * good + 0.2 * bad - or other weights, and select combination of weights that gives you best result).

4. Use algorithm specifically designed to handle such cases. Have a look at this thread first:

And if you are up to it - I can make you a small tutorial on how to use ImageJ and plugin that I wrote to stack your data with that algorithm.

 

[Lehtojj]

Thanks Vlaiv. I need some time to progress all this information, meanwhile I'll be practicing my processing with the subs I have available. Still learning this all.

J

[dph1nm]

On 26/01/2020 at 13:21, vlaiv said:

Unfortunately it is a bit complicated topic and DSS does not have option to combine data in such way.

What does the entropy weighted stacking do? It seems this is designed to combine data taken with different exposure times or on different nights.

NIgelM

 

[vlaiv]

40 minutes ago, dph1nm said:

What does the entropy weighted stacking do? It seems this is designed to combine data taken with different exposure times or on different nights.

NIgelM

 

Good point - I have no idea what it does.

There is a mention of this algorithm being implemented per some academic paper - but I did not read the paper although I wanted to (I think I could not find it).

I was under impression that it does some sort of entropy weighting based on single pixel values in stack and does not take into account other pixels in the image, or their distributions. Not sure why I got this idea - maybe because of the fact that if you take bunch of pixel values - all clipped because of saturation it will have very low entropy - such pixels get discarded and value of clipped region is calculated from bunch of pixels that have higher entropy (like short exposure subs that are not clipping).

But that was just a guess based on name of algorithm, like I said I have not read the paper on it.

Do you know how it works?

[ollypenrice]

Being a pragmatist, I would do the following:

Make two different stacks from each night.  Align them in whatever software you use for this. I use Registar.  Open one in Photoshop (or other program with Layers) and give it a basic stretch, far enough for the noise to be creeping into vsibility but not much beyond.  Open the second stack and stretch that in the same way, getting the background to the same values as the first.  Paste the second onto the first and then use the opacity slider to find out the weighting which gives you the lowest noise. (Just zoom in close and use your eyes.)  Flatten and continue to stretch.

This approach is often called 'stacking the stacks' and is, theoretically, less effective than stacking all the separately and previously calibrated subs. However, in many practical tests I've found precious little difference between the real results.

My own stacking program, AstroArt, would allow me to calibrate but not combine all my subs so I'd have a complete set of subs, calibrated with their own darks/flats etc, which I could then combine as usual.  In situations where the Sigma clipping of outliers were important (eg where many satellite trails were present in the subs) this method would have a significant advantage.

Olly