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MARSFT: Efficient fitting of CARS spectra using a library‐based genetic algorithm

Greifenstein, M. ; Dreizler, A. (2024)
MARSFT: Efficient fitting of CARS spectra using a library‐based genetic algorithm.
In: Journal of Raman Spectroscopy, 2021, 52 (3)
doi: 10.26083/tuprints-00017817
Article, Secondary publication, Publisher's Version

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Item Type: Article
Type of entry: Secondary publication
Title: MARSFT: Efficient fitting of CARS spectra using a library‐based genetic algorithm
Language: English
Date: 5 January 2024
Place of Publication: Darmstadt
Year of primary publication: 2021
Place of primary publication: Chichester
Publisher: John Wiley & Sons
Journal or Publication Title: Journal of Raman Spectroscopy
Volume of the journal: 52
Issue Number: 3
DOI: 10.26083/tuprints-00017817
Corresponding Links:
Origin: Secondary publication DeepGreen
Abstract:

A loss‐less compressed library scheme is presented in this publication that allows for computationally efficient fitting of coherent anti‐Stokes Raman spectra with no restriction to the number of degrees of freedom for the spectral fit. The compression is achieved by convolving the squared modulus and the real part of the complex susceptibility with a Gaussian kernel narrower than the experimental apparatus function. This effectively reduces library size while allowing to convolve to the final experimental linewidth during the fit. For the optimization procedure, a gradient‐free mixed‐integer genetic algorithm was implemented due to its ability to extract library spectra without interpolation. We demonstrate the ability of the code by comparing it to CARSFT in terms of dependency on starting solution, computational cost and accuracy using simulated spectra with varying noise contribution.

Uncontrolled Keywords: CARS, genetic algorithm, library, spectral fit
Status: Publisher's Version
URN: urn:nbn:de:tuda-tuprints-178171
Classification DDC: 600 Technology, medicine, applied sciences > 620 Engineering and machine engineering
Divisions: 16 Department of Mechanical Engineering > Institute of Reactive Flows and Diagnostics (RSM)
Date Deposited: 05 Jan 2024 13:43
Last Modified: 14 Mar 2024 10:20
SWORD Depositor: Deep Green
URI: https://tuprints.ulb.tu-darmstadt.de/id/eprint/17817
PPN: 516253581
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