A Neural Network Based Pre-Selection of Big Data in Photon Science

One of the challenges of scientific data collection on a big data scale is the problem of storing all data. An example of this is femtosecond crystallography. Here, small crystals are illuminated by a pulsed X-ray laser beam and the resulting diffraction patterns are recorded by a detector device. However, only a minor portion of the diffraction data can be used in subsequent analyses because the crystal hit rate is small. In this paper, neural networks are explored for identifying useful diffraction data automatically. The technique is applied to data from the Linac Coherent Light Source (LCLS) and the resulting neural network design is shown.