Satellite trails not handled by WBPP?

If there are satellite trail artifacts present, usually the rejection statistics in the image integration is the thing you should look for. However, statistics can work if there is enough data to get a reasonable mean, and identify the outliers. So the amount of valid subs is a crucial quantity. If you set the integration minimum weight to zero, it could be that the algorithm rejected too much data, and you’re left with a too small amount of data to reject these trails. The trails are simply not longer identified as outliers, but as signal. Possibly, your sky quality changed during exposure, which can make rejection more unreliable even with proper image calibration.

If you have the possibility to add more data, i would suppose to do that. If not, you can try tweaking the integration / rejection settings. Maybe also trying median instead of mean. In my experience, this can improve things a bit, but you shouldnt expect a wonder. And all the integration settings available today are quite complex, due to the sheer amount of different available rejection algorithms.
If your sky quality changed, you might also try local normalization. In my experience, this is something WBPP didnt do well in the past (so i dont include it in my WBPP workflow anymore, and do it “manually” instead).

HR_Maurer · May 13, 2026 at 08:34 AM

If there are satellite trail artifacts present, usually the rejection statistics in the image integration is the thing you should look for. However, statistics can work if there is enough data to get a reasonable mean, and identify the outliers. So the amount of valid subs is a crucial quantity. If you set the integration minimum weight to zero, it could be that the algorithm rejected too much data, and you’re left with a too small amount of data to reject these trails. The trails are simply not longer identified as outliers, but as signal. Possibly, your sky quality changed during exposure, which can make rejection more unreliable even with proper image calibration.

If you have the possibility to add more data, i would suppose to do that. If not, you can try tweaking the integration / rejection settings. Maybe also trying median instead of mean. In my experience, this can improve things a bit, but you shouldnt expect a wonder. And all the integration settings available today are quite complex, due to the sheer amount of different available rejection algorithms.
If your sky quality changed, you might also try local normalization. In my experience, this is something WBPP didnt do well in the past (so i dont include it in my WBPP workflow anymore, and do it “manually” instead).

I think I have ample subs (5-6 hrs). There was a lot of variance in the sky - 3 different locations, 4 different nights (and one night with shorter exposures).

I haven’t done the WBPP processes manually in a long time. So, the way you do it is to process up to local normalization, then do that, then restart at image integration?

Thanks

Chuck Schreiner · May 13, 2026, 01:44 PM

HR_Maurer · May 13, 2026 at 08:34 AM

If there are satellite trail artifacts present, usually the rejection statistics in the image integration is the thing you should look for. However, statistics can work if there is enough data to get a reasonable mean, and identify the outliers. So the amount of valid subs is a crucial quantity. If you set the integration minimum weight to zero, it could be that the algorithm rejected too much data, and you’re left with a too small amount of data to reject these trails. The trails are simply not longer identified as outliers, but as signal. Possibly, your sky quality changed during exposure, which can make rejection more unreliable even with proper image calibration.

If you have the possibility to add more data, i would suppose to do that. If not, you can try tweaking the integration / rejection settings. Maybe also trying median instead of mean. In my experience, this can improve things a bit, but you shouldnt expect a wonder. And all the integration settings available today are quite complex, due to the sheer amount of different available rejection algorithms.
If your sky quality changed, you might also try local normalization. In my experience, this is something WBPP didnt do well in the past (so i dont include it in my WBPP workflow anymore, and do it “manually” instead).

I think I have ample subs (5-6 hrs). There was a lot of variance in the sky - 3 different locations, 4 different nights (and one night with shorter exposures).

I haven’t done the WBPP processes manually in a long time. So, the way you do it is to process up to local normalization, then do that, then restart at image integration?

Thanks

hi Chuck,
my problem is, in between two deep-sky image session, there is at least one PI update, and a lot of things changed since i used it last time. So i try to set up WBPP, run it, and often end up starting over after image registration, or doing everything “manually”. I’m often not up to date with the newest PI developments in recent years. It is partly because of weather conditions, partly because the sky quality at home got worse, and partially because im growing old.