  Rare event searches allow us to search for new physics at energy scales
inaccessible with other means by leveraging specialized large-mass detectors.
Machine learning provides a new tool to maximize the information provided by
these detectors. The information is sparse, which forces these algorithms to
start from the lowest level data and exploit all symmetries in the detector to
produce results. In this work we present KamNet which harnesses breakthroughs
in geometric deep learning and spatiotemporal data analysis to maximize the
physics reach of KamLAND-Zen, a kiloton scale spherical liquid scintillator
detector searching for neutrinoless double beta decay ($0\nu\beta\beta$). Using
a simplified background model for KamLAND we show that KamNet outperforms a
conventional CNN on benchmarking MC simulations with an increasing level of
robustness. Using simulated data, we then demonstrate KamNet's ability to
increase KamLAND-Zen's sensitivity to $0\nu\beta\beta$ and $0\nu\beta\beta$ to
excited states. A key component of this work is the addition of an attention
mechanism to elucidate the underlying physics KamNet is using for the
background rejection.
