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  Unsupervised learning approaches to characterize heterogeneous samples using X-ray single particle imaging

Zhuang, Y., Awel, S., Barty, A., Bean, R., Bielecki, J., Bergemann, M., et al. (2021). Unsupervised learning approaches to characterize heterogeneous samples using X-ray single particle imaging.

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2109.06179.pdf (Preprint), 7MB
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https://arxiv.org/abs/2109.06179 (Preprint)
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 Creators:
Zhuang, Y.1, 2, Author              
Awel, S.3, Author
Barty, A.3, Author
Bean, R.3, Author
Bielecki, J.3, Author
Bergemann, M.3, Author
Daurer, B. J.3, Author
Ekeberg, T.3, Author
Estillore, A. D.3, Author
Fangohr, H.2, 4, 5, 6, Author              
Giewekemeyer, K.3, Author
Hunter, M. S.3, Author
Karnevskiy, M.3, Author
Kirian, R. A.3, Author
Kirkwood, H.3, Author
Kim, Y.3, Author
Koliyadu, J.3, Author
Lange, H.3, Author
Letrun, R.3, Author
Lübke, J.3, Author
Mall, A.1, 2, Author              Michelat, T.3, AuthorMorgan, A. J.3, AuthorRoth, N.3, AuthorSamanta, A. K.3, AuthorSato, T.3, AuthorShen, Z.3, AuthorSikorski, M.3, AuthorSchulz, F.3, AuthorSpence, J. C. H.3, AuthorVagovic, P.3, AuthorWollweber, T.1, 2, 7, Author              Worbs, L.3, AuthorXavier, P. L.2, 7, AuthorYefanov, O.3, AuthorMaia, F. R. N. C.3, AuthorHorke, D. A.3, AuthorKüpper, J.3, AuthorLoh, N. D.3, AuthorMancuso, A. P.3, AuthorChapman, H. N.3, AuthorAyyer, K.1, 2, 7, Author               more..
Affiliations:
1Computational Nanoscale Imaging, Condensed Matter Dynamics Department, Max Planck Institute for the Structure and Dynamics of Matter, Max Planck Society, ou_3012829              
2Center for Free-Electron Laser Science, ou_persistent22              
3external, ou_persistent22              
4Computational Science, Scientific Service Units, Max Planck Institute for the Structure and Dynamics of Matter, Max Planck Society, ou_3267028              
5European XFEL, ou_persistent22              
6University of Southampton, ou_persistent22              
7The Hamburg Center for Ultrafast Imaging, Universität Hamburg, ou_persistent22              

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Free keywords: eess.IV, Condensed Matter, Mesoscale and Nanoscale Physics, cond-mat.mes-hall, Physics, Data Analysis, Statistics and Probability, physics.data-an
 Abstract: One of the outstanding analytical problems in X-ray single particle imaging (SPI) is the classification of structural heterogeneity, which is especially difficult given the low signal-to-noise ratios of individual patterns and that even identical objects can yield patterns that vary greatly when orientation is taken into consideration. We propose two methods which explicitly account for this orientation-induced variation and can robustly determine the structural landscape of a sample ensemble. The first, termed common-line principal component analysis (PCA) provides a rough classification which is essentially parameter-free and can be run automatically on any SPI dataset. The second method, utilizing variation auto-encoders (VAEs) can generate 3D structures of the objects at any point in the structural landscape. We implement both these methods in combination with the noise-tolerant expand-maximize-compress (EMC) algorithm and demonstrate its utility by applying it to an experimental dataset from gold nanoparticles with only a few thousand photons per pattern and recover both discrete structural classes as well as continuous deformations. These developments diverge from previous approaches of extracting reproducible subsets of patterns from a dataset and open up the possibility to move beyond studying homogeneous sample sets and study open questions on topics such as nanocrystal growth and dynamics as well as phase transitions which have not been externally triggered.

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Language(s): eng - English
 Dates: 2021-09-13
 Publication Status: Published online
 Pages: 29
 Publishing info: -
 Table of Contents: -
 Rev. Type: No review
 Identifiers: arXiv: 2109.06179
 Degree: -

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