Paper AS-MoM11
Correction-Free Analysis of SIMS Data at High Mass Resolution in the Presence of Detector Saturation

Monday, October 22, 2018, 11:40 am, Room 204

We present a strategy for analyzing TOF SIMS data sets affected by detector saturation at high mass resolution. The detectors used in many instruments undercount ions due to saturation effects; if two or more ions arrive within a very short interval (the “dead time”) only the first to arrive is recorded. This changes both the total number of ions collected and their statistical distribution. The dead time is typically short enough that only ions of the same nominal mass are affected, but a significant fraction of the total ions reaching the detector may still be missed; at low masses the great majority of ions often go undetected. Methods are available for “correcting” the collected spectra at both unit-mass and high-mass resolutions based on an assumed model for the behavior of the detector, but these are problematic when the number of scans taken is small and/or the saturation is sufficiently high. Calculation of the variance of the corrected data as required in many multivariate analysis techniques is also complex. As a result, performing quantitative analysis of TOF SIMS data while preserving high mass resolution remains problematic.

Rather than attempt to correct the measured data to remove saturation, we incorporate the detector behavior into the statistical distribution used in maximum a posteriori reconstruction, justified on Bayesian grounds. Essentially, this method finds the most probably correct spectra, given the observed data and available prior information. This approach has several advantages over previous techniques: no approximations are involved other than the assumed model of the detector, the method performs well even when applied to highly saturated and/or single-scan data sets, and it is possible to calculate uncertainty estimates for the extracted quantities. Our method is first demonstrated by application to individual spectra. The correctness of the approach and its efficacy are demonstrated on synthetic data sets, and then applied to selected experimental results. Performance on data of different degrees of saturation and/or total number of counts is determined and compared with existing correction-based approaches; the effects of dead-time duration on lineshape and FWHM are also explored. We then demonstrate extension of this approach to imaging data and compare and contrast the results obtained with analyses performed at unit-mass resolution.