I was wondering what the best approach is to gradient removal with data from multiple nights.
It seems to me, that as my data grows over multiple nights, the performance of the gradient removal feature deteriorates, resulting in local “hazes”, like so:
This haze in the middle is nearly not as bad as when I stack the data from each night separately.
Now, if I suppose the gradient is linear per night, then the polynomial degree necessary to eliminate this gradient grows per night, meaning if each individual session has a linear gradient, the gradient after, let’s say 6 nights, should be an order 6 polynomial?
Is there any consensus on how to approach this? Would it make sense to add an option in e.g. sirilic to first remove the gradient per session before merging everything and stacking the multi-night data?