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  Scalable multicomponent spectral analysis for high-throughput data annotation

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

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Item Permalink: https://hdl.handle.net/21.11116/0000-0007-F406-F Version Permalink: https://hdl.handle.net/21.11116/0000-000F-4D2F-8
Genre: Paper

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2102.05604.pdf (Preprint), 2MB
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 Creators:
Xian, R. Patrick1, 2, Author           
Ernstorfer, Ralph1, Author           
Pelz, Philipp Michael3, 4, Author
Affiliations:
1Physical Chemistry, Fritz Haber Institute, Max Planck Society, ou_634546              
2Department of Neurobiology, Northwestern University, Evanston 60208, IL, USA, ou_persistent22              
3National Center for Electron Microscopy Facility, Molecular Foundry, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA, ou_persistent22              
4Department of Materials Science and Engineering, University of California, Berkeley, CA 94720, USA, ou_persistent22              

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Free keywords: Condensed Matter, Materials Science, cond-mat.mtrl-sci, Physics, Data Analysis, Statistics and Probability, physics.data-an
 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.

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Language(s): eng - English
 Dates: 2021-02-10
 Publication Status: Not specified
 Pages: 17
 Publishing info: -
 Table of Contents: -
 Rev. Type: -
 Identifiers: arXiv: 2102.05604
URI: https://arxiv.org/abs/2102.05604
 Degree: -

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