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. 2010 Apr;81(4):045107.
doi: 10.1063/1.3374111.

Absolute calibration of LIDAR Thomson scattering systems by rotational Raman scattering

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Absolute calibration of LIDAR Thomson scattering systems by rotational Raman scattering

R Scannell et al. Rev Sci Instrum. 2010 Apr.

Abstract

Absolute calibration of LIDAR Thomson scattering systems on large fusion devices may be achieved using rotational Raman scattering. The choice of calibrating gas molecule presents different options and design trade-offs and is likely to be strongly dependent on the laser wavelength selected. Raman scattering of hydrogenic molecules produces a very broad spectrum, however, with far fewer scattered photons than scattering from nitrogen or oxygen at the same gas pressure. Lower laser wavelengths have the advantage that the Raman cross section increases, sigma(Raman) proportional to 1/lambda(0)(4), but the disadvantage that the spectral width of the scattered spectrum decreases, Deltalambda(Raman) proportional to lambda(0)(2). This narrower spectrum makes measurement closer to the laser wavelength necessary. The design of the calibration technique presents a number of challenges. Some of these challenges are generic to all Thomson scattering systems. These include detecting a sufficient number of photoelectrons and designing filters that measure close to the laser wavelength while simultaneously achieving adequate blocking of the laser wavelength. An issue specific to LIDAR systems arises since the collection optics operates over a wide range of depth of field. This wide depth of field has the effect of changing the angle of light incident on the optical interference filter with plasma major radius. The angular distribution then determines the effective spectral transmission function of the interference filter and hence impacts on the accuracy of the absolute calibration. One method that can be used to increase absolute calibration accuracy is collecting both Stokes and anti-Stokes lines with optical filter transmission bands specifically designed to reduce systematic uncertainty.

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