Speaker
David Pennicard
(FS-DS (Elektronik und Data))
Description
Increasing data rates in photon science experiments mean that there is demand to perform data reduction close to the detector, to reduce the workload on later stages of data processing, transfer and storage. We have developed machine-learning based methods for reducing data in serial crystallography experiments by rejecting bad images. Additionally, we implemented the algorithm on CPU, GPU and FPGA, with the aim of doing fast hardware-accelerated data reduction in a detector DAQ PC.
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Primary authors
David Pennicard
(FS-DS (Elektronik und Data))
Heinz Graafsma
(FS-DS (Detektorsysteme))
Dr
Vahid Rahmani
(FS-PETRA (PETRA III))
