Geometry correction is traditionally plagued by mis-fitting of correlated parameters, leading to local minima which prevent further improvements. Segmented detectors pose an enhanced risk of mis-fitting: even a minor confusion of detector distance and panel separation can prevent improvement in data quality. The slip-and-slide algorithm breaks down effects of the correlated parameters and their associated target functions in a fundamental shift in the approach to the problem. Parameters are never refined against the components of the data to which they are insensitive, providing a dramatic boost in the exploitation of information from a very small number of diffraction patterns. This algorithm can be applied to exploit the adherence of the spot-finding results prior to indexing to a given lattice using unit-cell dimensions as a restraint. Alternatively, it can be applied to the predicted spot locations and the observed reflection positions after indexing from a smaller number of images. Thus, the indexing rate can be boosted by 5.8% using geometry refinement from only 125 indexed patterns or 500 unindexed patterns. In one example of cypovirus type 17 polyhedrin diffraction at the Linac Coherent Light Source, this geometry refinement reveals a detector tilt of 0.3° (resulting in a maximal Z-axis error of ∼0.5 mm from an average detector distance of ∼90 mm) whilst treating all panels independently. Re-indexing and integrating with updated detector geometry reduces systematic errors providing a boost in anomalous signal of sulfur atoms by 20%. Due to the refinement of decoupled parameters, this geometry method also reaches convergence.
Position reconstruction for segmented detectors
