June 24 2026 APP 2.0.0-beta46 has been released !
Improved internal memory configuration (lower ! memory usage), fixed beta45 startup issue, fixed Set Save Directory & 2-panel mosaics.
May 27 2026 APP 2.0.0-beta45 has been released !
Fully Multi-Threaded LNC, many improvements for the registration engine, platform upgrade, and further tuning of internal memory consumption and memory release back to OS.
Apr 14 2026: Google Pay, Apple Pay & WeChat Pay added as payment options
Update on the 2.0.0 release & the full manual
We are getting close to the 2.0.0 stable release and the full manual. The manual will soon become available on the website and also in PDF format. Both versions will be identical and once released, will start to follow the APP release cycle and thus will stay up-to-date to the latest APP version.
Once 2.0.0 is released, the price for APP will increase. Owner's license holders will not need to pay an upgrade fee to use 2.0.0, neither do Renter's license holders.
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)
