Cumulant mapping as the basis of multi-dimensional spectrometry
arXiv:2204.03709 · doi:10.1039/D2CP02365B
Abstract
Cumulant mapping employs a statistical reconstruction of the whole by sampling its parts. The theory developed in this work formalises and extends ad hoc methods of `multi-fold' or `multi-dimensional' covariance mapping. Explicit formulae have been derived for the expected values of up to the 6th cumulant and the variance has been calculated for up to the 4th cumulant. A method of extending these formulae to higher cumulants has been described. The formulae take into account reduced fragment detection efficiency and a background from uncorrelated events. Number of samples needed for suppressing the statistical noise to a required level can be estimated using Matlab code included in Supplemental Material. The theory can be used to assess the experimental feasibility of studying molecular fragmentations induced by femtosecond or x-ray free-electron lasers. It is also relevant for extending the conventional mass spectrometry of biomolecules to multiple dimensions.
13 pages, 6 figures; inconsequential mistakes in Eq. 8 corrected, a few typos weeded out, irrelevant comments from Matlab code removed