Dear ATSAS (cryson) developers,
most SANS beamlines nowadays provide the resolution effect via an uncertainty of q, in the fourth column of data. However, as of now it is not possible to use this information to take resolution effects into account in Cryson. I know, that one can provide a resolution file. However, using the fourth column is easier and is in many cases also more precise.
will you provide this option? coding-wise it should be relatively straight-forward.
the option is provided in Pepsi-SANS (https://team.inria.fr/nano-d/software/pepsi-sans/), but Pepsi-SANS lacks an option to perdeuterate separate chains.
the option is also provided in CaPP (https://github.com/Niels-Bohr-Institute ... ophys/CaPP), but fitting in CaPP is not well-suited for batch-mode, as it runs in a python GUI.
Best regards,
Andreas
resolution effects in Cryson via the fourth column
fourth column in SANS data
Could you please point us to some documentation on how the fourth column should be interpreted? So far we've seen SANS data in four-column format only from KWS1 (FRM2, Munich).
Re: resolution effects in Cryson via the fourth column
Sure I can.
Several SANS beamlines provide a fourth column in data, e.g. these:
QUAKKA@ANSTO
D22@ILL
... and probable many more. The fourth column provides an uncertainty on the estimated q-value (1 sigma, assuming normal distributions).
Documented e.g. here:
https://www.ncnr.nist.gov/staff/hammoud ... ter_15.pdf
that is, the value of I at a given q_i is calculated as the integral of I(q) over a range of q values, weighted with the normal distribution with mean q_i, and standard deviation s_i. Here s_i denotes the spread of the i'th q (sigma of q). s_i is (often) given in the fourth column of data.
Several SANS beamlines provide a fourth column in data, e.g. these:
QUAKKA@ANSTO
D22@ILL
... and probable many more. The fourth column provides an uncertainty on the estimated q-value (1 sigma, assuming normal distributions).
Documented e.g. here:
https://www.ncnr.nist.gov/staff/hammoud ... ter_15.pdf
that is, the value of I at a given q_i is calculated as the integral of I(q) over a range of q values, weighted with the normal distribution with mean q_i, and standard deviation s_i. Here s_i denotes the spread of the i'th q (sigma of q). s_i is (often) given in the fourth column of data.