Tips met settings ivm integratie parameters

[Van den Broek]

Tips ivm gebruik van de integratie parameters zijn meer dan welkom

Bvb default staat alle frames gelijk gewicht, maar is quality van de frame niet belangrijker …

LNC, heeft 2nd order bvb enige meerwaarde,

ik zoek dus eigenlijk real life experiences.

/Yves

[Theunissen]

Ik heb een vergelijkbare vraag ooit op @Bula ‘s site gesteld, het antwoord:

Three rules of thumb to follow would be:

• If you have less then 15-20 frames, use median integration without sigma clipping
• If you more than 15-20 frames and less than 40-50 use average integration and outlier rejection with kappa 2.5 & 2 iterations.
• If you have more then 40-50 frames use average integration and outlier rejection with kappa 3 (or larger) with only 1 iteration.

 
Regarding linear fit clipping:

Linear fit clipping is a solution for a problem, the problem of bad data normalization over the entire field of view.

• so never use it in integration of bias, dark & flat frames because the problem of bad normalization over the entire field of view is non-existent in this case.
  
• And in integration of lights, use LNC instead of linear fit clipping. LNC actually solves this problem, so linear fit clipping is redundant in that case.

 
Then you are left with the choice of sigma clipping or winsorized sigma clipping.

• If you combine data of the same camera and optics, I would advise to always use winsorized sigma clipping. It works a bit better, especially with more frames.
• Otherwise, be careful with winsorized sigma clipping, it is known to introduce stack artefacts, especially at star borders. It’s really bad if you combine data of a refractor and a newton for instance. The diffraction spikes of the newton will show artefacts. These artefacts won’t be there with regular sigma clipping.
• In case of doubt, use sigma clipping instead of winsorized clipping, combined with LNC regular sigma clipping in APP works very good.

 
GENERAL WARNINGs on outlier rejection:

• always try to remove as much artefacts, like bad pixels, in the calibration fase using a Bad Pixel Map
• let integration work for you by dithering your data with plenty of pixels. Dithering with 10-20 pixels is much more efficient to remove calibration problems than 1-2 pixel dithers.
• Outlier rejection must be considered as a filter to be used as a last resort to solve artefacts/problems in your data. The less you need to use it, the better the integration quality will be.
• Outlier rejection removes problems, but also good data. If you set outlier rejection too strong, you will remove a lot of good data as well. So being too aggresively with outlier rejection is very harmfull for the Signal to Noise ratio of your integrated stack. So be carefull with using it.
• If you have more than 50 frames, then 1 iteration is all it should take to remove problems. Only adjust your kappa value.
• Never set the kappa value below 2, this is always really destructive for your data.

 
Bron: https://www.astropixelprocessor.com/community/faq/when-to-use-which-outlier-rejection-filter/

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