You can investigate divergences in ShinyStan (even if you didn't fit in RStan). It lets you visualize one, two, or three parameters at a time and highlights the divergent transitions in the scatterplots.
The divergences are because the Hamiltonian simulation fails numerically---it doesn't just affect one parameter, it terminates the iterations and you can be left with a system that both mixes poorly (because you can no longer follow the Hamiltonian, which is where the good mixing comes from) and is biased if the divergences aren't random. Typically we see that in the neck of a funnel in a hierarchical model, for instance, when we can't step the step size low enough to follow the Hamiltonian and still make progress on the the rest of the funnel.
Oh, and if you want to verify you're getting the right results, here's a good way to do it: