Rapid Deconvolution of Low-Resolution Time-of-Flight Data using Bayesian Inference

Deconvolution of low-resolution time-of-flight data has numerous advantages including the ability to extract additional information from the experimental data. We augment the well-known Lucy-Richardson deconvolution algorithm by various Bayesian prior distributions and show that a prior of second-differences of the signal outperforms the standard Lucy-Richardson algorithm, accelerating the rate of convergence by more than a factor of four, while preserving the peak amplitude ratios of a similar fraction of the total peaks. A novel stopping criterion and boosting mechanism is implemented to ensure these methods converge to a similar entropy, and that local minima are avoided, respectively. Improvement by a factor of two in mass resolution allows more accurate quantification of the spectra. The general method is demonstrated in this paper by the deconvolution of fragmentation peaks of the DHB matrix, as well as the BTP thermometer ion, following femtosecond ultraviolet laser desorption.