  Modern electron tomography has progressed to higher resolution at lower doses
by leveraging compressed sensing methods that minimize total variation (TV).
However, these sparsity-emphasized reconstruction algorithms introduce tunable
parameters that greatly influence the reconstruction quality. Here, Pareto
front analysis shows that high-quality tomograms are reproducibly achieved when
TV minimization is heavily weighted. However, in excess, compressed sensing
tomography creates overly smoothed 3D reconstructions. Adding momentum to the
gradient descent during reconstruction reduces the risk of over-smoothing and
better ensures that compressed sensing is well behaved. For simulated data, the
tedious process of tomography parameter selection is efficiently solved using
Bayesian optimization with Gaussian processes. In combination, Bayesian
optimization with momentum-based compressed sensing greatly reduces the
required compute time$-$an 80% reduction was observed for the 3D reconstruction
of SrTiO$_3$ nanocubes. Automated parameter selection is necessary for large
scale tomographic simulations that enable the 3D characterization of a wider
range of inorganic and biological materials.
