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Automated identification and classification of single particle serial femtosecond X-ray diffraction data.

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Rolles,  D.
Research Group of Structural Dynamics of (Bio)Chemical Systems, MPI for Biophysical Chemistry, Max Planck Society;

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Citation

Andreasson, J., Martin, A. V., Liang, M., Timneanu, N., Aquila, A., Wang, F., et al. (2014). Automated identification and classification of single particle serial femtosecond X-ray diffraction data. Optics Express, 22(3), 2497-2510. doi:10.1364/OE.22.002497.


Cite as: http://hdl.handle.net/11858/00-001M-0000-002C-0E59-9
Abstract
The first hard X-ray laser, the Linac Coherent Light Source (LCLS), produces 120 shots per second. Particles injected into the X-ray beam are hit randomly and in unknown orientations by the extremely intense X-ray pulses, where the femtosecond-duration X-ray pulses diffract from the sample before the particle structure is significantly changed even though the sample is ultimately destroyed by the deposited X-ray energy. Single particle X-ray diffraction experiments generate data at the FEL repetition rate, resulting in more than 400,000 detector readouts in an hour, the data stream during an experiment contains blank frames mixed with hits on single particles, clusters and contaminants. The diffraction signal is generally weak and it is superimposed on a low but continually fluctuating background signal, originating from photon noise in the beam line and electronic noise from the detector. Meanwhile, explosion of the sample creates fragments with a characteristic signature. Here, we describe methods based on rapid image analysis combined with ion Time-of-Flight (ToF) spectroscopy of the fragments to achieve an efficient, automated and unsupervised sorting of diffraction data. The studies described here form a basis for the development of real-time frame rejection methods, e.g. for the European XFEL, which is expected to produce 100 million pulses per hour.