That would be most helpful, although the “manual” precedure seems to work surprisingly well, at least for this dataset. The difference is that random phase is introduced only once at the beginning of heterogenous classification (doing it at each cycle requires modification of the code, of course), but it still works. I can share the dataset, if you like. Again, this is anecdotal, but in our case Relion seems to do less well than cryosparc at removing junk particles. (BTW, as in the original post, heterogenous classification against a set of identical random references - as suggested above by olibclarke - also helped, but I did not compare results extensively).