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Title: Machine learning enabled lineshape analysis in optical two-dimensional coherent spectroscopy

Abstract

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. Finally, this approach can be potentially applied to 2D spectra with more sophisticated spectral features.

Authors:
 [1]; ORCiD logo [2];  [1]; ORCiD logo [1]
  1. Florida International Univ., Miami, FL (United States)
  2. Florida International Univ., Miami, FL (United States); Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
Publication Date:
Research Org.:
Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
Sponsoring Org.:
USDOE National Nuclear Security Administration (NNSA)
OSTI Identifier:
1617317
Alternate Identifier(s):
OSTI ID: 1617107
Report Number(s):
SAND-2020-4169J
Journal ID: ISSN 0740-3224; JOBPDE; 685393; TRN: US2106742
Grant/Contract Number:  
AC04-94AL85000; NA0003525
Resource Type:
Accepted Manuscript
Journal Name:
Journal of the Optical Society of America. Part B, Optical Physics
Additional Journal Information:
Journal Volume: 37; Journal Issue: 6; Journal ID: ISSN 0740-3224
Publisher:
Optical Society of America (OSA)
Country of Publication:
United States
Language:
English
Subject:
71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSICS

Citation Formats

Namuduri, Srikanth, Titze, Michael, Bhansali, Shekhar, and Li, Hebin. Machine learning enabled lineshape analysis in optical two-dimensional coherent spectroscopy. United States: N. p., 2020. Web. doi:10.1364/JOSAB.385195.
Namuduri, Srikanth, Titze, Michael, Bhansali, Shekhar, & Li, Hebin. Machine learning enabled lineshape analysis in optical two-dimensional coherent spectroscopy. United States. https://doi.org/10.1364/JOSAB.385195
Namuduri, Srikanth, Titze, Michael, Bhansali, Shekhar, and Li, Hebin. Fri . "Machine learning enabled lineshape analysis in optical two-dimensional coherent spectroscopy". United States. https://doi.org/10.1364/JOSAB.385195. https://www.osti.gov/servlets/purl/1617317.
@article{osti_1617317,
title = {Machine learning enabled lineshape analysis in optical two-dimensional coherent spectroscopy},
author = {Namuduri, Srikanth and Titze, Michael and Bhansali, Shekhar and Li, Hebin},
abstractNote = {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. Finally, this approach can be potentially applied to 2D spectra with more sophisticated spectral features.},
doi = {10.1364/JOSAB.385195},
journal = {Journal of the Optical Society of America. Part B, Optical Physics},
number = 6,
volume = 37,
place = {United States},
year = {Fri Mar 06 00:00:00 EST 2020},
month = {Fri Mar 06 00:00:00 EST 2020}
}

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