A Python Algorithm to Analyze Inelastic Neutron Scattering Spectra Based on the y -Scale Formalism

This paper presents a Python-based algorithm, named INSCorNorm, to correct the inelastic neutron scattering (INS) spectra for both sample and container self-shielding and to normalize the experimental spectral intensity to an absolute physical scale (barn/energy unit) facilitating the comparison wit...

Full description

Saved in:
Bibliographic Details
Published in:Journal of chemical theory and computation Vol. 16; no. 12; p. 7671
Main Authors: Scatigno, Claudia, Romanelli, Giovanni, Preziosi, Enrico, Zanetti, Matteo, Parker, Stewart F, Rudić, Svemir, Andreani, Carla, Senesi, Roberto
Format: Journal Article
Language:English
Published: United States 08.12.2020
ISSN:1549-9626, 1549-9626
Online Access:Get more information
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:This paper presents a Python-based algorithm, named INSCorNorm, to correct the inelastic neutron scattering (INS) spectra for both sample and container self-shielding and to normalize the experimental spectral intensity to an absolute physical scale (barn/energy unit) facilitating the comparison with computer simulations and interpretation. The algorithm is benchmarked against INS measurements of ZrH performed on the TOSCA spectrometer at the ISIS Facility. We also apply the algorithm to the INS spectra from l-lysine, a system of broad interest in biology and medicine, and we discuss how corrected INS data provide an experimental benchmark for theoretical calculations of nuclear anisotropic displacement parameters in molecular systems. The total neutron sample cross section to use for the self-shielding corrections is discussed, as well as the best approach to derive experimentally the cross section at the VESUVIO spectrometer, together with the experimental value of the hydrogen nuclear mean kinetic energy, ⟨ ⟩. The algorithm is made available to the neutron user community within the MANTID software.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 23
ISSN:1549-9626
1549-9626
DOI:10.1021/acs.jctc.0c00790