Markov Chain Monte Carlo Sampling for Target Analysis of Transient Absorption Spectra.

Global and target analysis techniques are ubiquitous tools for interpreting transient absorption (TA) spectra. However, characterizing uncertainty in the kinetic parameters and component spectra derived from these fitting procedures can be challenging. Furthermore, fitting TA spectra of inorganic nanomaterials where the component spectra of different excited states are nearly or completely overlapped is particularly problematic. Here, we present a target analysis model for extracting excited-state spectra and dynamics from TA data using a Markov chain Monte Carlo (MCMC) sampler to visualize and understand uncertainty in the model fits. We demonstrate the utility of this approach by extracting the overlapping component spectra and dynamics of single- and biexciton states in CsPbBr3 nanocrystals. Significantly, refinement of the component spectra is accomplished by fitting the entire fluence-dependent series of ensemble TA data using the Poisson statistics of photon absorption, providing multiple checks for internal consistency. The MCMC method itself is highly general and can be applied to any data set or model framework to accurately characterize uncertainty in the fit and aid model selection when choosing between different models.