  When measuring rare processes at Belle II, a huge luminosity is required,
which means a large number of simulations are necessary to determine signal
efficiencies and background contributions. However, this process demands high
computation costs while most of the simulated data, in particular in case of
background, are discarded by the event selection. Thus, filters using graph
neural networks are introduced at an early stage to save the resources for the
detector simulation and reconstruction of events discarded at analysis level.
In our work, we improved the performance of the filters using graph attention
and investigated statistical methods including sampling and reweighting to deal
with the biases introduced by the filtering.
