Notes on Fitting and Analysis Frameworks for QENS Spectra of (Soft) Colloid Suspensions

¹ Institut für Angewandte Physik, Universität Tübingen, Auf der Morgenstelle 10, 72076 Tübingen, Germany.
² Institut Max von Laue Paul Langevin (ILL), CS 20156, F-38042 Grenoble Cedex 9, France.
³ Jülich Centre for Neutron Science (JCNS), Forschungszentrum Jülich GmbH, D-52425 Jülich, Germany
⁴ Chemical and Engineering Materials Division, Neutron Sciences Directorate, and JCNS Outstation at the Spallation Neutron Source (SNS), Oak Ridge National Laboratory, P.O. Box 2008, Oak Ridge, Tennessee 37831, USA
⁵ Department of Biomedical Sciences and Biofilms-Research Center for Biointerfaces (BRCB), Malmö University, 20506 Malmö, Sweden.

^* e-mail: christian.beck@uni-tuebingen.de
^** current address: European Synchrotron Radiation Facility 71, avenue des Martyrs, CS 40220, 38043 Grenoble Cedex 9, France.
^*** e-mail: seydel@ill.eu

Published online: 17 November 2022

Abstract

With continuously improving signal-to-noise ratios, a statistically sound analysis of quasi-elastic neutron scattering (QENS) spectra requires to fit increasingly complex models which poses several challenges. Simultaneous fits of the spectra for all recorded values of the momentum transfer become a standard approach. Spectrometers at spallation sources can have a complicated non-Gaussian resolution function which has to be described most accurately. At the same time, to speed up the fitting, an analytical convolution with this resolution function is of interest. Here, we discuss basic concepts to efficient approaches for fits of QENS spectra based on standard MATLAB and Python fit algorithms. We illustrate the fits with example data from IN16B, BASIS, and BATS.