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

Optical two-dimensional (2D) coherent spectroscopy excels in studying coupling and dynamics in complex systems. The dynamical information can be learned from lineshape analysis to extract the corresponding linewidth. However, it is usually challenging to fit a 2D spectrum, especially when the homoge...

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Bibliographic Details
Published in:arXiv.org 2019-11
Main Authors: Namuduri, Srikanth, Titze, Michael, Bhansali, Shekhar, Li, Hebin
Format: Article
Language:English
Subjects:
Algorithms
Artificial intelligence
Complex systems
Computer simulation
Machine learning
Spectra
Spectrum analysis
Two dimensional analysis
Online Access:Get full text
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Summary:Optical two-dimensional (2D) coherent spectroscopy excels in studying coupling and dynamics in complex systems. The dynamical information can be learned from lineshape analysis to extract the corresponding linewidth. However, it is usually challenging to fit a 2D spectrum, especially when the homogeneous and inhomogeneous linewidths are comparable. We implemented a machine learning algorithm to analyze 2D spectra to retrieve homogeneous and inhomogeneous linewidths. The algorithm was trained using simulated 2D spectra with known linewidth values. The trained algorithm can analyze both simulated (not used in training) and experimental spectra to extract the homogeneous and inhomogeneous linewidths. This approach can be potentially applied to 2D spectra with more sophisticated spectral features.
ISSN:2331-8422
DOI:10.48550/arxiv.1911.11215