  We describe a neural network for predicting the background hit rate in the
Belle II detector produced by the SuperKEKB electron-positron collider. The
neural network, BGNet, learns to predict the individual contributions of
different physical background sources, such as beam-gas scattering or
continuous top-up injections into the collider, to Belle II sub-detector rates.
The samples for learning are archived 1 Hz time series of diagnostic variables
from the SuperKEKB collider subsystems and measured hit rates of Belle II used
as regression targets. We test the learned model by predicting detector hit
rates on archived data from different run periods not used during training. We
show that a feature attribution method can help interpret the source of changes
in the background level over time.
