Oct 19 – 23, 2020
Europe/Zurich timezone

Selective background MC simulation with graph neural networks at Belle II

Oct 23, 2020, 10:20 AM
Lightning talk 3 ML for simulation and surrogate model : Application of Machine Learning to simulation or other cases where it is deemed to replace an existing complex model Workshop


Nikolai Hartmann (Ludwig Maximilians Universitat (DE))


Searching for rare physics processes requires a good understanding of the
backgrounds involved. This often requires large amounts of simulated data that
are computationally expensive to produce. The Belle II collaboration is planning
to collect 50 times the amount of data of its predecessor Belle. With the
increase in data volume the necessary volume of simulated data increases as
well. Due to aggressive event selections that enrich the signal processes of
interest, much of the simulated data is thrown away.
This talk presents a method for predicting which events will be thrown away
already after the computationally less expensive event generation step. This is
achieved using graph neural networks applied to the simulated event decay tree.
Only events selected by the neural network are passed to the resource intense
detector simulation and the reconstruction step. False negatives from this
selection can lead to biases in the distributions of observables for filtered
events. Possible ways to mitigate this are also discussed.

Primary authors

Nikolai Hartmann (Ludwig Maximilians Universitat (DE)) James Kahn (Karlsruhe Institute of Technology (KIT)) Yannick Bross (Ludwig Maximilians Universitat (DE)) Kilian Lieret (Ludwig Maximilian University Munich) Andreas Maximilian Lindner (University of Munich (LMU)) Thomas Kuhr (Ludwig Maximilians Universitat (DE))

Presentation materials