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 thought this topic might be helpful for a number of people in my situation: learned some but need more understanding to get better results.
I’ve gotten to the point that when I look at my data I get to the normalize step and stop. Then I do an analytical graph for star shape, quality, and RMS. I throw out some data based on some arbitrary threshold. It definitely helps but not enough.
When evaluating the subs, what should I really be looking for? Like, what star shape is generally considered good? What is a bad RMS? What rules of thumb should I use to throw out data?
I know the obvious ones like when tracking is bad and other obvious anomalies. I’m wanting to understand what I’m looking at better, while not being a data scientist, so I can more readily throw out bad data that may be giving me undesired results.
Thanks!
It may be a good idea to load say 5 images with very different quality, then do a bit of processing and check the numbers. This may give you a good idea already, what is good or bad is a bit of a setup specific thing in astrophotography so there is no hard boundary there. A registration RMS closer to 0 is better, FWHM can differ for different setups. The quality score is based on the analysis you do at each step of the process and it accounts for things like star shape. Maybe focussing on that is good enough already?
In general, never throw away data with a satellite trail, clouds and stuff I would throw away manually.
In my case I have hundreds and hundreds of subs so looking through a handful won’t help too much. I do go through them all to throw out obvious bad ones. I was thinking more along the lines of what star roundness is considered acceptable by people, or other measures that people look for.
I do look at the FWHM but there’s no way to sort on that measure so it’s more challenging to throw out bad ones when you’re dealing with several or more hundred.
I have seen in other tools that people have come up with various formulas to help them automatically select the good frames. I don’t understand the formulas entirely (I’m rather new at this plus I’m not a mathematician) but I was curious what people who use APP look at to weed out bad frames that could scale.
It's an interesting topic - this is one area where I feel APP could do with some work - I've found the stats often bear little relationship to the actual sub (especially where it's cloudy, they often have better scores than clear skies!) - so I have found the only way to do it reliably is to go through each by hand - which can be slow and somewhat tedious.
It's not so bad that I would start using an alternative tool, so it cant be THAT bad! (but could be improved)
