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Optical two-dimensional (2D) coherent spectroscopy excels in studyingcoupling and dynamics in complex systems. The dynamical information can belearned from lineshape analysis to extract the corresponding linewidth.However, it is usually challenging to fit a 2D spectrum, especially when thehomogeneous and inhomogeneous linewidths are comparable. We implemented amachine learning algorithm to analyze 2D spectra to retrieve homogeneous andinhomogeneous linewidths. The algorithm was trained using simulated 2D spectrawith known linewidth values. The trained algorithm can analyze both simulated(not used in training) and experimental spectra to extract the homogeneous andinhomogeneous linewidths. This approach can be potentially applied to 2Dspectra with more sophisticated spectral features.

Machine Learning Enabled Lineshape Analysis in Optical Two-Dimensional Coherent Spectroscopy