  Serial crystallography at X-ray free electron laser (XFEL) and synchrotron
facilities has experienced tremendous progress in recent times enabling novel
scientific investigations into macromolecular structures and molecular
processes. However, these experiments generate a significant amount of data
posing computational challenges in data reduction and real-time feedback. Bragg
peak finding algorithm is used to identify useful images and also provide
real-time feedback about hit-rate and resolution. Shot-to-shot intensity
fluctuations and strong background scattering from buffer solution, injection
nozzle and other shielding materials make this a time-consuming optimization
problem. Here, we present PeakNet, an autonomous Bragg peak finder that
utilizes deep neural networks. The development of this system 1) eliminates the
need for manual algorithm parameter tuning, 2) reduces false-positive peaks by
adjusting to shot-to-shot variations in strong background scattering in
real-time, 3) eliminates the laborious task of manually creating bad pixel
masks and the need to store these masks per event since these can be
regenerated on demand. PeakNet also exhibits exceptional runtime efficiency,
processing a 1920-by-1920 pixel image around 90 ms on an NVIDIA 1080 Ti GPU,
with the potential for further enhancements through parallelized analysis or
GPU stream processing. PeakNet is well-suited for expert-level real-time serial
crystallography data analysis at high data rates.
