Speaker
Description
By striving for ever-higher luminosities, the Belle II detector is set to observe rare decay signals. However, these high luminosities correspond to an increased demand for MC-simulated events. Therefore, an efficient algorithm to produce and process simulated events in large quantities is vital for the successful interpretation of Belle II’s data. While generating events in the MC simulation chain is quick and easy to compute, detector simulation and particle reconstruction are slow tasks. A fine-tuned version of the Particle Transformer (ParT) is introduced into the chain to classify passing events during analysis pre-selection and to discard those that fail the pre-filter before the detector response simulation. With considerable speedups in the case of static ParT training, this method has been shown to reduce overall time consumption, thereby successfully saving valuable computing resources. This work explores a parallelised approach to the ParT training process in order to match the parallel workflow of data production. It involves an on-the-fly training pipeline across multiple machines, retrieving updated ParT models, and redistributing them back for the next iteration.