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

Michael Titze; Srikanth Namuduri; Shekhar Bhansali; Hebin Li

doi:10.1364/CLEO_QELS.2020.FF2Q.4

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

Michael Titze
, Srikanth Namuduri
, Shekhar Bhansali
, Hebin Li

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

Although analytical solutions exist, the analysis of two-dimensional spectroscopy (2DCS) data can be tedious. A machine learning approach to analyzing 2DCS spectra is presented. We test the accuracy of the algorithm on simulated and experimental data.

Original language	English
Title of host publication	Optics InfoBase Conference Papers
Volume	Part F182-CLEO-QELS 2020
DOIs	https://doi.org/10.1364/CLEO_QELS.2020.FF2Q.4
State	Published - Jan 1 2020
Externally published	Yes

Access to Document

10.1364/CLEO_QELS.2020.FF2Q.4

Cite this

@inproceedings{de6843937be84f41b33741ca1532cf32,

title = "Machine learning enabled lineshape analysis in optical two-dimensional coherent spectroscopy",

abstract = "Although analytical solutions exist, the analysis of two-dimensional spectroscopy (2DCS) data can be tedious. A machine learning approach to analyzing 2DCS spectra is presented. We test the accuracy of the algorithm on simulated and experimental data.",

author = "Michael Titze and Srikanth Namuduri and Shekhar Bhansali and Hebin Li",

year = "2020",

month = jan,

day = "1",

doi = "10.1364/CLEO\_QELS.2020.FF2Q.4",

language = "English",

isbn = "9781943580767",

volume = "Part F182-CLEO-QELS 2020",

booktitle = "Optics InfoBase Conference Papers",

}

TY - GEN

T1 - Machine learning enabled lineshape analysis in optical two-dimensional coherent spectroscopy

AU - Titze, Michael

AU - Namuduri, Srikanth

AU - Bhansali, Shekhar

AU - Li, Hebin

PY - 2020/1/1

Y1 - 2020/1/1

N2 - Although analytical solutions exist, the analysis of two-dimensional spectroscopy (2DCS) data can be tedious. A machine learning approach to analyzing 2DCS spectra is presented. We test the accuracy of the algorithm on simulated and experimental data.

AB - Although analytical solutions exist, the analysis of two-dimensional spectroscopy (2DCS) data can be tedious. A machine learning approach to analyzing 2DCS spectra is presented. We test the accuracy of the algorithm on simulated and experimental data.

UR - https://www.scopus.com/pages/publications/85095455650

U2 - 10.1364/CLEO_QELS.2020.FF2Q.4

DO - 10.1364/CLEO_QELS.2020.FF2Q.4

M3 - Conference contribution

SN - 9781943580767

VL - Part F182-CLEO-QELS 2020

BT - Optics InfoBase Conference Papers

ER -

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

Abstract

Access to Document

Fingerprint

Cite this