Serial crystallography at X-ray free electron laser (XFEL) sources has
experienced tremendous progress in achieving high data rate in recent times.
While this development offers potential to enable novel scientific
investigations, such as imaging molecular events at logarithmic timescales, it
also poses challenges in regards to real-time data analysis, which involves
some degree of data reduction to only save those features or images pertaining
to the science on disks. If data reduction is not effective, it could directly
result in a substantial increase in facility budgetary requirements, or even
hinder the utilization of ultra-high repetition imaging techniques making data
analysis unwieldy. Furthermore, an additional challenge involves providing
real-time feedback to users derived from real-time data analysis. In the
context of serial crystallography, the initial and critical step in real-time
data analysis is finding X-ray Bragg peaks from diffraction images. To tackle
this challenge, we present PeakNet, a Bragg peak finder that utilizes neural
networks and runs about four times faster than Psocake peak finder, while
delivering significantly better indexing rates and comparable number of indexed
events. We formulated the task of peak finding into a semantic segmentation
problem, which is implemented as a classical U-Net architecture. A key
advantage of PeakNet is its ability to scale linearly with respect to data
volume, making it well-suited for real-time serial crystallography data
analysis at high data rates