  An ESO internal ALMA development study, BRAIN, is addressing the ill-posed
inverse problem of synthesis image analysis employing astrostatistics and
astroinformatics. These emerging fields of research offer interdisciplinary
approaches at the intersection of observational astronomy, statistics,
algorithm development, and data science. In this study, we provide evidence of
the benefits of employing these approaches to ALMA imaging for operational and
scientific purposes. We show the potential of two techniques, RESOLVE and
DeepFocus, applied to ALMA calibrated science data. Significant advantages are
provided with the prospect to improve the quality and completeness of the data
products stored in the science archive and overall processing time for
operations. Both approaches evidence the logical pathway to address the
incoming revolution in data rates dictated by the planned electronic upgrades.
Moreover, we bring to the community additional products through a new package,
ALMASim, to promote advancements in these fields, providing a refined ALMA
simulator usable by a large community for training and/or testing new
algorithms.
