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 |
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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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