Analysis of Transient Microscopy Data in Reciprocal Space
Abstract
Transient microscopy has evolved to include a family of well-established techniques for studying the spatiotemporal dynamics of excited states in a variety of materials systems. In these methods, a laser pulse is focused to locally excite a material, and the subsequent evolution of the excited species is visualized to extract transport parameters such as diffusivities. Typically, transport coefficients are extracted from experimental datasets using curve-fitting methods to analyze the change in the mean squared displacement (MSD) of the spatial profiles over time. However, this approach can lead to inaccurate or misleading diffusivity values under many common experimental conditions, and is only formally accurate for the case of a single diffusing species. In this work, we introduce a new approach to the analysis of transient microscopy data based on the evolution of profile shapes in reciprocal space. We use simulated datasets to quantitatively analyze the performance of our proposed methods and compare them to current state-of-the-art analysis techniques. We demonstrate that reciprocal-space-based analysis methods have the highest overall performance in terms of accuracy and precision for normal diffusive transport over a range of realistic initial conditions and signal-to-noise ratios. Furthermore, we highlight the limitations of using MSD alone to characterize anomalous transport in materials, and we show how analysis of spatial frequencies can yield a more comprehensive understanding of complex excited-state dynamics and their underlying physical mechanisms. Our results show that reciprocal-space-based analysis methods are powerful tools for characterizing and quantifying many different types of transport behaviors.
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BibTeX
@misc{sheehan2026analysis,
title = {{Analysis of Transient Microscopy Data in Reciprocal Space}},
author = {Thomas J. Sheehan and William A. Tisdale},
howpublished = {arXiv},
year = {2026},
month = oct,
doi = {10.48550/arxiv.2610.23756},
eprint = {2610.23756},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2610.23756},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - Analysis of Transient Microscopy Data in Reciprocal Space
AU - Thomas J. Sheehan
AU - William A. Tisdale
PY - 2026
DA - 2026/10/16
PB - arXiv
DO - 10.48550/arxiv.2610.23756
UR - https://arxiv.org/abs/2610.23756
AB - Transient microscopy has evolved to include a family of well-established techniques for studying the spatiotemporal dynamics of excited states in a variety of materials systems. In these methods, a laser pulse is focused to locally excite a material, and the subsequent evolution of the excited species is visualized to extract transport parameters such as diffusivities. Typically, transport coefficients are extracted from experimental datasets using curve-fitting methods to analyze the change in the mean squared displacement (MSD) of the spatial profiles over time. However, this approach can lead to inaccurate or misleading diffusivity values under many common experimental conditions, and is only formally accurate for the case of a single diffusing species. In this work, we introduce a new approach to the analysis of transient microscopy data based on the evolution of profile shapes in reciprocal space. We use simulated datasets to quantitatively analyze the performance of our proposed methods and compare them to current state-of-the-art analysis techniques. We demonstrate that reciprocal-space-based analysis methods have the highest overall performance in terms of accuracy and precision for normal diffusive transport over a range of realistic initial conditions and signal-to-noise ratios. Furthermore, we highlight the limitations of using MSD alone to characterize anomalous transport in materials, and we show how analysis of spatial frequencies can yield a more comprehensive understanding of complex excited-state dynamics and their underlying physical mechanisms. Our results show that reciprocal-space-based analysis methods are powerful tools for characterizing and quantifying many different types of transport behaviors.
ER -