  Regularized Maximum Likelihood (RML) techniques are a class of image
synthesis methods that achieve better angular resolution and image fidelity
than traditional methods like CLEAN for sub-mm interferometric observations. To
identify best practices for RML imaging, we used the GPU-accelerated open
source Python package MPoL, a machine learning-based RML approach, to explore
the influence of common RML regularizers (maximum entropy, sparsity, total
variation, and total squared variation) on images reconstructed from real and
synthetic ALMA continuum observations of protoplanetary disks. We tested two
different cross-validation (CV) procedures to characterize their performance
and determine optimal prior strengths, and found that CV over a coarse grid of
regularization strengths easily identifies a range of models with comparably
strong predictive power. To evaluate the performance of RML techniques against
a ground truth image, we used MPoL on a synthetic protoplanetary disk dataset
and found that RML methods successfully resolve structures at fine spatial
scales present in the original simulation. We used ALMA DSHARP observations of
the protoplanetary disk around HD 143006 to compare the performance of MPoL and
CLEAN, finding that RML imaging improved the spatial resolution of the image by
up to a factor of 3 without sacrificing sensitivity. We provide general
recommendations for building an RML workflow for image synthesis of ALMA
protoplanetary disk observations, including effective use of CV. Using these
techniques to improve the imaging resolution of protoplanetary disk
observations will enable new science, including the detection of protoplanets
embedded in disks.
