The hit rate is an important statistic for determining the data acquisition efficiency and planning beamtime utilization in experiments. For example, when the crystal hit rate is low during data collection, researchers can replace the sample with fresh crystals or increase the crystal density, which may increase the hit rate and yield more hit images containing the diffraction pattern for the remaining beamtime. Meanwhile, although obtaining a large number of hit images is important to increase the SX data collection efficiency, when the crystal hit rate is high with intense multiple crystal diffraction patterns during data collection, researchers may decrease the density of the crystal sample. This reduces the hit rate, but it offers the advantage of avoiding the incorrect indexing of the Bragg peaks and the incorrect signal-to-noise ratio (SNR) related to the background noise.

Crystal density is calculated based on the sample delivery method (e.g., sample volume) and X-ray properties (e.g., exposure time, repetition rate, and beam size) to obtain an appropriate hit rate. The crystals are delivered continuously to an X-ray location to collect diffraction data. In an ideal experiment, new crystals (or larger crystals with a new volume) would be delivered continuously at every X-ray exposure point, resulting in a 100% hit rate. However, providing crystals precisely each time both spatially and temporally through which X-rays are transmitted is experimentally impossible. The collected SX data include the diffraction image in which X-rays pass through the crystal and the image information in which the crystal is not hit. In addition, unwanted diffraction from salt crystals and the sample delivery material may occur experimentally during data acquisition. This unwanted diffraction can be sorted as a Bragg peak and processed as a hit image by the filtering program, leading to an increase in the hit rate.

Programs such as Cheetah (Barty et al., 2014), NanoPeakCell (Coquelle et al., 2015), and Psocake (Thayer et al., 2017) can be used to filter hit images from the collected SX data. These programs filter hit images that meet the criteria for selection as hit images, including parameters such as the number of Braggs peaks, minimum SNR, and number of connected pixels above the minimum SNR. These filtering parameters can affect the number of hit images, as researchers can change settings based on data quality (Figure 1B). For example, if researchers lower the criteria for filtering parameters such as the SNR and peak number to include low Bragg peak intensities, the hit rate will increase. Conversely, if the researchers raise the criteria for the filtering parameters to only use data with high Bragg peak intensities, the hit rate will be lower. Therefore, hit rates are variables that can exhibit differences based not only on sample quality but also on the filtering program settings. Hence, a direct correlation between data collection efficiency and hit rate cannot be established. Therefore, hit rates of independent experiments cannot be compared and evaluated.

The hit images including the Bragg peaks are indexed, integrated, and scaled to provide the final three-dimensional structural information. The accurate indexing of crystal diffraction patterns in the first data processing step is essential to provide an accurate structure factor. In general, higher indexing rates provide better data statistics in terms of using more diffraction patterns. Factors affecting the indexing rate include the quality of the acquired image, optimization of the detector geometry, indexing program used, and technical skills. In terms of data quality, the following factors can decrease the indexing rate: 1) several space groups of crystal forms existing in the crystal sample, 2) Bragg peaks with low SNR levels, 3) salt peaks or unwanted intensities, and 4) mis-indexing because of multicrystal diffraction patterns.

Information about the detector geometry, including the X-ray energy, crystal-to-detector distance, and detector specifications is essentially required to index the diffraction patterns from hit images in the SX experiment. The indexing efficiency varies based on the accuracy of the detector geometry information. For example, segmented detectors consist of several small detector modules tiled together, such as Cornell-SLAC Pixel Array Detectors (CSPAD) (Moeller et al., 2012), multi-port charge-coupled devices (MPCCD) (Kameshima et al., 2014), adaptive gain integrating pixel detectors (AGIPD) (Allahgholi et al., 2019), Percival (Marras et al., 2019), and adJUstiNg Gain detector FoR the Aramis User station (JUNGFRAU) (Leonarski et al., 2020) detectors. Geometry optimization may be necessary for each panel during data processing because the pixels in each module may not be perfectly aligned on a regular grid. A previous geometry study showed that the indexing rate of Gd:lysozyme, cathepsin B, DgkA, and rhodopsin-arrestin data sets collected from different SX experiments were improved by 3–60% after geometry refinement (Yefanov et al., 2015). Therefore, geometric optimization is required for efficient indexing of diffraction patterns, and the indexing rate may differ depending on the accuracy of the detector geometry optimization.

Moreover, the indexing rate may vary depending on the indexing programs used for data processing, indexing algorithms, or indexing parameters (Nam, 2022b). Currently, various indexing programs such as CrystFEL (White et al., 2016; White, 2019), dials. index in DIALS (Gildea et al., 2014), Computational Crystallography Toolbox (cctbx) (Brewster et al., 2015), FELIX (Beyerlein et al., 2017b), SPIND (Li et al., 2019), XGANDALF (Gevorkov et al., 2019), Pattern-matching indexing (Dejoie and Tamura, 2020), SPIND-TC (Li et al., 2020) and MCDPS (Zhou et al., 2021) have been developed for SX data analysis, and they analyze diffraction patterns using their unique approaches with various algorithms. Each of these indexing algorisms exhibits different indexing rates and data statistics even when processed using the same indexing parameters, including the detector geometry. Furthermore, the indexing rate can be increased using a combination of several indexing algorithms, which may provide good statistical values with a high indexing rate. However, this does not necessarily result in better structure refinement statistics. In addition, the indexing rate changes during data processing optimization according to the changes in the indexing parameters (e.g., unit cell parameter tolerance, SNR cutoff, and integration radius).

Consequently, the indexing rate varies depending on the quality of the collected data, program used, - technical skills of the individual during processing, and setting of the indexing parameters, even when the procedure for indexing the Bragg peaks in a diffraction pattern is the same. Meanwhile, in general SX data processing, researchers process data by increasing the indexing rate; however, if sufficient diffraction images are collected, increasing the indexing standard and using only excellent data will provide better structural information. On the other hand, since the structure factor is obtained from the correctly indexed images, more important feedbacks than the hit rate during experiments are the accumulated numbers or increasing rate of valid images (indexable patterns).