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Proceedings Paper

Automatic alignment of a synchrotron radiation source beamline using intelligent systems
Author(s): S. Olof Svensson; Roberto Pugliese
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Paper Abstract

Synchrotron Radiation (SR) sources in general, and the new third generation SR sources in particular, deliver very intense x-ray beams with very low divergence. However, due to small shifts of the sorted electron beam position caused by re-optimization of the closed orbit after shutdowns, the beamlines must be regularly re-aligned in order to deliver optimum performance. Since the beamlines generally contain complicated optical elements, such as x- ray mirrors and monochromators, the alignment procedure is difficult and time-consuming. Automatic beamline alignment has been envisaged in order to more constantly keep optimal performance of the beamline. An Intelligent System approach has been chosen to face the complexity of x-ray beamline alignment. A knowledge-based system has been chosen for the development of the automatic alignment tools. The developed tools have been applied to the multi-wavelength anomalous dispersion (MAD) beamline of the European Synchrotron Radiation (ESRF). The intensity and the spot shape at the sample position, obtained by using a small 2D CCD detector, were optimized by automatically aligning the main optical element, a bent cylindrical mirror that focuses the beam in both horizontal and vertical directions. The developed automatic techniques have been shown to robustly optimize the intensity and the focal spot shape on the ESRF MAD beamline. A series of images of the beam shape showing the optimization will be presented.

Paper Details

Date Published: 13 October 1998
PDF: 8 pages
Proc. SPIE 3455, Applications and Science of Neural Networks, Fuzzy Systems, and Evolutionary Computation, (13 October 1998); doi: 10.1117/12.326729
Show Author Affiliations
S. Olof Svensson, European Synchrotron Radiation Facility (France)
Roberto Pugliese, Sincrotrone Trieste SCpA (Italy)


Published in SPIE Proceedings Vol. 3455:
Applications and Science of Neural Networks, Fuzzy Systems, and Evolutionary Computation
Bruno Bosacchi; David B. Fogel; James C. Bezdek, Editor(s)

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