  The Global Network of Optical Magnetometers for Exotic physics searches
(GNOME) conducts an experimental search for certain forms of dark matter based
on their spatiotemporal signatures imprinted on a global array of synchronized
atomic magnetometers. The experiment described here looks for a gradient
coupling of axion-like particles (ALPs) with proton spins as a signature of
locally dense dark matter objects such as domain walls. In this work,
stochastic optimization with machine learning is proposed for use in a search
for ALP domain walls based on GNOME data. The validity and reliability of this
method were verified using binary classification. The projected sensitivity of
this new analysis method for ALP domain-wall crossing events is presented.
