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Scalable multicomponent spectral analysis for high-throughput data annotation

MPS-Authors
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Xian,  R. Patrick
Physical Chemistry, Fritz Haber Institute, Max Planck Society;
Department of Neurobiology, Northwestern University;

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Ernstorfer,  Ralph
Physical Chemistry, Fritz Haber Institute, Max Planck Society;

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Fulltext (public)

2102.05604.pdf
(Preprint), 2MB

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Citation

Xian, R. P., Ernstorfer, R., & Pelz, P. M. (in preparation). Scalable multicomponent spectral analysis for high-throughput data annotation.


Cite as: http://hdl.handle.net/21.11116/0000-0007-F406-F
Abstract
Orchestrating parametric fitting of multicomponent spectra at scale is an essential yet underappreciated task in high-throughput quantification of materials and chemical composition. We present a systematic approach compatible with high-performance computing infrastructures using the MapReduce model and task-based parallelization. Our approach is realized in a software, pesfit, to enable efficient generation of high-quality data annotation and online spectral analysis as demonstrated using experimental materials characterization datasets.