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Blind identification of magnetic signals in electron magnetic chiral dichroism using independent component analysis
Uppsala University, Disciplinary Domain of Science and Technology, Physics, Department of Physics and Astronomy, Materials Theory.ORCID iD: 0000-0002-6550-0087
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2018 (English)In: Ultramicroscopy, ISSN 0304-3991, E-ISSN 1879-2723, Vol. 195, p. 129-135Article in journal (Refereed) Published
Abstract [en]

Electron magnetic chiral dichroism (EMCD) is a promising technique to investigate local magnetic structures in the electron microscope. However, recognition of the EMCD signal, or also finding optimal parameter settings for given materials and sample orientations typically requires extensive simulations to aid the experiment. Here, we discuss how modern data processing techniques, in particular independent component analysis, can be used to identify magnetic signals in an unsupervised manner from energy filtered transmission electron microscopy (EFTEM) images. On the background of the recent advent of 4D scanning transmission electron microscopy, we discuss how this data processing may enable simultaneous tracking of all three spatial components of the magnetic momenta for arbitrary materials and several sample orientations without the previous need of complementary simulations.

Place, publisher, year, edition, pages
2018. Vol. 195, p. 129-135
National Category
Physical Sciences
Identifiers
URN: urn:nbn:se:uu:diva-348259DOI: 10.1016/j.ultramic.2018.08.021ISI: 000450060100016OAI: oai:DiVA.org:uu-348259DiVA, id: diva2:1196973
Funder
Swedish Research CouncilEU, FP7, Seventh Framework Programme, 320832Available from: 2018-04-11 Created: 2018-04-11 Last updated: 2018-12-19Bibliographically approved
In thesis
1. Signal Processing Tools for Electron Microscopy
Open this publication in new window or tab >>Signal Processing Tools for Electron Microscopy
2018 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

The detection of weak signals in noisy data is a problem which occurs across various disciplines. Here, the signal of interest is the spectral signature of the electron magnetic chiral dichroism (EMCD) effect. In principle, EMCD allows for the measurement of local magnetic structures in the electron microscope, its spatial resolution, versatility and low hardware requirements giving it an eminent position among competing measurement techniques. However, experimental shortcomings as well as intrinsically low signal to noise ratio render its measurement challenging to the present day.   

This thesis explores how posterior data processing may aid the analysis of various data from the electron microscope. Following a brief introduction to different signals arising in the microscope and a yet briefer survey of the state of the art of EMCD measurements, noise removal strategies are presented. Afterwards, gears are shifted to discuss the separation of mixed signals into their physically meaningful source components based on their assumed mathematical characteristics, so called blind source separation (BSS).    

A data processing workflow for detecting weak signals in noisy spectra is derived from these considerations, ultimately culminating in several demonstrations of the extraction of EMCD signals. While the focus of the thesis does lie on data processing strategies for EMCD detection, the approaches presented here are similarly applicable in other situations. Related topics such as the general analysis of hyperspectral images using BSS methods or the fast analysis of large data sets are also discussed.

Place, publisher, year, edition, pages
Uppsala: Acta Universitatis Upsaliensis, 2018. p. 60
Series
Digital Comprehensive Summaries of Uppsala Dissertations from the Faculty of Science and Technology, ISSN 1651-6214 ; 1672
National Category
Physical Sciences Computer Sciences Other Mathematics
Identifiers
urn:nbn:se:uu:diva-348264 (URN)978-91-513-0345-1 (ISBN)
Public defence
2018-06-12, Å2001, Ångströmlaboratoriet, Lägerhyddsvägen 1, Uppsala, 09:00 (English)
Opponent
Supervisors
Available from: 2018-05-18 Created: 2018-04-11 Last updated: 2018-10-08

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Spiegelberg, JakobRusz, Jan

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