When scientists use ultrafast spectroscopy to observe molecular processes that occur in trillionths of a second or less, they rely on computational methods to interpret complex datasets. A new study from the laboratory of chemist Chong Fang provides a rigorous way to determine how much confidence researchers can place in those analyses.
Published as the cover article in the latest issue of Precision Chemistry, the study introduces a statistical framework for characterizing uncertainty in global analysis, a widely used method for analyzing spectroscopy data. The researchers found that, for typical spectral datasets with reasonable signal-to-noise ratios across a broad wavelength region, uncertainty in fitted patterns is generally well below 10%, supporting the robustness of conclusions drawn from global analysis.
The interdisciplinary project brought together expertise in mathematics, statistics, physics, chemistry and biology. The research team used Markov Chain Monte Carlo sampling, a Bayesian statistical approach that repeatedly explores possible solutions to map parameter uncertainty and probability distributions. Unlike conventional fitting approaches that focus on identifying a single best-fit solution, MCMC sampling provides a more complete picture of the range of plausible parameter values.
