  When properly tuned, Hamiltonian Monte Carlo scales to some of the most
challenging high-dimensional problems at the frontiers of applied statistics,
but when that tuning is suboptimal the performance leaves much to be desired.
In this paper I show how suboptimal choices of one critical degree of freedom,
the cotangent disintegration, manifest in readily observed diagnostics that
facilitate the robust application of the algorithm.
