Tables for
Volume F
Crystallography of biological macromolecules
Edited by M. G. Rossmann and E. Arnold

International Tables for Crystallography (2006). Vol. F, ch. 11.4, p. 233   | 1 | 2 |

Section 11.4.7. Detector diagnostics

Z. Otwinowskia* and W. Minorb

aUT Southwestern Medical Center at Dallas, 5323 Harry Hines Boulevard, Dallas, TX 75390-9038, USA, and bDepartment of Molecular Physiology and Biological Physics, University of Virginia, 1300 Jefferson Park Avenue, Charlottesville, VA 22908, USA
Correspondence e-mail:

11.4.7. Detector diagnostics

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The HKL package has a number of tools that can detect possible detector or experimental setup problems (Minor & Otwinowski, 1997[link]). Visual inspection of the image may provide only a very rough estimate of data quality. A check of the analogue-to-digital converter can provide rough diagnostics of detector electronics. Examination of the background can provide information about detector noise, especially when uncorrected images can be examined in the areas exposed to X-rays and areas where pure read-out noise can be observed. DENZO provides several diagnostic tools during the integration stage, as the crystallographer may observe crystal slippage, a change of unit-cell parameters or a change of the values of positional and angular [\chi^{2}] during the refinement. Even more tools are provided at the data-scaling stage. By observing scale factors, poor crystal alignment can be detected. Other tools may help diagnose X-ray shutter malfunction, spindle axis alignment and internal detector alignment problems. The final inspection of outliers may again provide valuable information about detector quality. The clustering of outliers in one area of the detector may indicate a damaged surface; if most outliers are partials, it may indicate a problem with spindle backlash or shutter control. The zoom mode may be used to display the area around the outliers to identify the source of a problem: for example, the existence of a satellite crystal or single pixel spikes due to electronic failure. Sometimes, even for very strong data, a histogram of the pixel intensities may stop below the maximum valid pixel value, indicating saturation of the data-acquisition hardware or software.


Minor, W. & Otwinowski, Z. (1997). Advances in accuracy and automation of data collection and processing. In Proceedings of the IUCr computing school, Bellingham, 1996.

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