From canSAS


Grethe Jensen Leading

We discussed approaches for considering multiple scattering effects. The following points were identified

1. A flag signalling significant multiple scattering would be good for both data reduction software (to allow for immediate action!) and data analysis/modelling software.

  • Requires data on absolute scale, together with values for wavelength and sample path length – or a well-determined measured SAS transmission. Good example of a situation where wavelength and path length would be nice to have accessible in the final reduced data file.
  • Could identify the maximum scattering order that should be included in the data analysis – or suggest a proper sample path length or wavelength that would be required to exclude significant multiple scattering effects.
  • Could suggest measurements for a small series of wavelengths/path lengths/contrasts, to identify features influenced by multiple scattering. These data could/should (?) be published with the data to address/prove the presence/non-presence of these effects.

2. Including multiple scattering effects in the data analysis

  • Relevant for both SAS in transmission geometry and for GISAS.
  • Include calculation of higher order scattering functions in model calculations and parameter optimization loop? By 2D convolution or MC simulations?
  • To speed calculations up, one might consider: Only include the relevant scattering orders, starting out with ‘just’ including second order scattering, only updating higher order scattering functions every N steps in the optimization, ...