Optimizing sparse sampling for 2D electronic spectroscopy
Abstract
We present a new data acquisition concept using optimized non-uniform sampling and compressed sensing reconstruction in order to substantially decrease the acquisition times in action-based multidimensional electronic spectroscopy. For this we acquire a regularly sampled reference data set at a fixed population time and use a genetic algorithm to optimize a reduced non-uniform sampling pattern. We then apply the optimal sampling for data acquisition at all other population times. Furthermore, we show how to transform two-dimensional (2D) spectra into a joint 4D time-frequency von Neumann representation. This leads to increased sparsity compared to the Fourier domain and to improved reconstruction. We demonstrate this approach by recovering transient dynamics in the 2D spectrum of a cresyl violet sample using just 25% of the originally sampled data points.
Additional Information
© 2017 Author(s). All article content, except where otherwise noted, is licensed under a Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). (Received 16 December 2016; accepted 27 January 2017; published online 23 February 2017) This work was funded by the European Research Council (ERC) within the Consolidator Grant "MULTISCOPE." We further thank D. Tannor for fruitful discussions on the implementation of the 2DvN basis and S. Draeger for performing the measurements on the laser dye.Attached Files
Published - 1.4976309.pdf
Supplemental Material - .listing
Supplemental Material - SuppInfo.pdf
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Additional details
- Eprint ID
- 75847
- Resolver ID
- CaltechAUTHORS:20170407-123637275
- European Research Council
- Created
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2017-04-07Created from EPrint's datestamp field
- Updated
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2021-11-15Created from EPrint's last_modified field