2026-10-09 APP 2.0.0-beta48 has been released !
This is mainly a bug fix release for the reported bugs in beta47. In addition it has a significant User Interface performance improvement noticable when you need to process 1000s of images in APP.
2026-10-03 APP 2.0.0-beta47 has been released !
This is a very big release... many code optimizations/rewrites, many parts of APP are much faster and more precise, especially star analysis and registration are much faster ! Big mosaics will go much faster 🙂
And we introduce a new preview filter, which includes the new Adaptive Deep Sky Stretch and a General Levels Stretch, this has been long over due. The old preview filter (the panel on the right side to stretch and preview your data) has not been updated for a couple of years and it is a critical part of any APP session.
Adaptive Deep Sky Stretch or ADSS is a much more intuitive stretch algorithm. It is also color preserving (which is a great new addition in itself ! ) which means that the highlights are no longer bleached, color is maintained from the linear data while stretching. You can simply stretch your data using the sky background level and signal width sliders which work much easier and more intuitive. You no longer need to fiddle with the old complicated sliders with zoom buttons. All the zoom buttons for the sliders are made redundant. And you can enter slider values in the slider value fields yourself. I will make a youtube video in the coming days to explain and fully introduce ADSS. Once the video is ready, it will be highlighted on the forum.
RGB Combine upgrade !. The RGB Combine Tool has been rewritten mostly. The tool is faster and easier to use and most importantly, the Luminance slider to inject a luminance channel or channels into the RGB composite is now working correctly. It no longer bleaches the RGB colors. Combined with the new ADSS preview, you will get much better and easier great composites with great color! I will also explain this in a new youtube video.
I'm curious, is there an official formula published for how the Quality Score is calculated, similar to the "acceptance criteria" often used in PI? Â I assume it includes FHWM, Star Count, Eccentricity, PSF in the Star Analysis phase, but is there any weighting for each of those? Â Are there any others, like perhaps contrast detection to identify subs that might have been taken with dew on the mirror/objective? Â My apologies if I've missed this documented somewhere.
Also, I know that the calculated quality score changes with the Registration and Normalization phases. Â The general way that I use this score is to sort my Star Analysis results, exclude the poor-quality outliers, then Register, sort on Quality again and repeat, then Normalize, sort on Quality again and repeat (though I often don't have any to throw away at this stage).
I tend to vacillate on what my acceptance threshold is (for only the Star Analysis phase). Â I would normally shoot for no less than 1 standard deviation lower than the mean... but APP doesn't calculate S.D. Â If I'm quick and lazy, I'll de-select the bottom third of the quality scores for each filter. Â If I'm really being picky but still lazy I'll kill the bottom half, but that's throwing away a lot of data and my pickiness usually doesn't produce a noticeably better image. Â Much of the time I will just drop the obvious outliers.
It would be awesome if the Star Analysis tab allowed me to define my acceptance criteria, then auto-select only those subs that meet it after Star Analysis completes. Â Choose .5, 1 or 2 Standard Deviations from a dropdown menu and Star Analysis would select only the subs that meet that threshold. Â (I'm not sure I'd do the same for Registration or Normalization, as it may result in a more data loss than needed, but it'd be interesting to try it).
Hi @mountainair,
The quality score formula changes at each procesing step in APP as you have epxerienced, collecting more and more analytical results. The final formula after 5) Normalize combines all the reported metrics, FWHM size and shape (eccentricity), star count, noise. But there are other methrics like background and dispersion and SNR. You can simply make graphs of these metrics, sort on them and then reject clear outliers. The actual formula will be release in the manual with 2.0 stable. And please know that we will add more options in this regard. Currently the quality score is our fixed formula, we will make it possible to use many different formula going forward.
Finally, in our experience, throwing away so much data like you indicate is usually counter productive in terms of getting good signal to noise ratios in the final result unless you suffer from many frame with bad stars due to guiding issues with your setup, is this the case?
Please be carefull and only throw away clear outliers in the graphs, and you will be fine and get the most out of your data 😉
Mabula
