  Magnetic flux emergence from the convection zone into the photosphere and
beyond is a critical component of the behaviour of large-scale solar magnetism.
Flux rarely emerges amid field-free areas at the surface, but when it does, the
interaction between magnetism and plasma flows can be reliably explored. Prior
ensemble studies identified weak flows forming near emergence locations, but
the low signal-to-noise required averaging over the entire dataset, erasing
information about variation across the sample. Here, we apply deep learning to
achieve improved signal-to-noise, enabling a case-by-case study. We find that
these associated flows are dissimilar across instances of emergence and also
occur frequently in the quiet convective background. Our analysis suggests
diminished influence of supergranular-scale convective flows and magnetic
buoyancy on flux rise. Consistent with numerical evidence, we speculate that
small-scale surface turbulence and / or deep-convective processes play an
outsize role in driving flux emergence.
