Implementation and Evaluation of Data Analysis Strategies for Time-Resolved Optical Spectroscopy

TLDR

Time‑resolved optical spectroscopy is essential for probing fundamental processes, but extracting information from its complex data requires advanced analysis techniques. The authors present and evaluate implementation strategies for global lifetime, target, and lifetime distribution analysis, focusing on LDA to explore kinetic information. They establish a regularization procedure for LDA in ultrafast spectroscopy, evaluate factors influencing reliable lifetime reconstruction, and demonstrate strategies to mitigate artifacts inherent in ultrafast optical data. The optimal regularization factor is identified using the L‑curve and generalized cross‑validation, the identity matrix norm proves most efficient, and the described procedures can be readily applied to any time‑resolved data.

Abstract

Time-resolved optical spectroscopy plays a key role in illuminating the mechanisms of many fundamental processes in physics, chemistry, and biology. However, to extract the essential information from the highly complex time-resolved data, advanced data analysis techniques are required. Here we present the implementation strategies and the evaluation of the familiar global lifetime and target analysis as well as the not so widely adopted lifetime distribution analysis (LDA). Furthermore, we demonstrate the implementation of analysis strategies dealing with a number of artifacts inherently present in data from ultrafast optical experiments. The focus of the work is placed on LDA as it allows invaluable exploration depth of the kinetic information contained in the experimental data. We establish a clear regularization procedure for the use of LDA in ultrafast optical spectroscopy and evaluate the performance of a number of factors that play a role in the reliable reconstruction of lifetime distributions. Our results show that the optimal regularization factor can be determined well with the L-curve and the generalized cross-validation techniques. Moreover, the performance evaluations indicate that the most efficient regularization norm is the identity matrix. The analytical procedures described in this work can be readily implemented and used for the analysis of any time-resolved data.