Ok I see. Then I guess one can try a Half-Cauchy/Half-Normal prior distributions and, if divergences happen because of the posterior having a mode at the boundary, one can start looking into boundary-avoiding kind of priors. Thanks to your comment, I also noticed that actually both in the Stan and Pymc documentation about the non-centered formulations they keep getting “false positive” divergences. I guess something else I could do is to check whether the same applies in my case.
Let me know if I am overthinking/overcomplicating this, but it starts to make sense. Thanks again for your comments!