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

Zhuang, Y.; Awel, S.; Barty, A.; Bean, R.; Bielecki, J.; Bergemann, M.; Daurer, B.J.; Ekeberg, T.; Estillore, A.D.; Fangohr, H.; Giewekemeyer, K.; Hunter, M.S.; Karnevskiy, M.; Kirian, R.A.; Kirkwood, H.; Kim, Y.; Koliyadu, J.; Lange, H.; Letrun, R.; Lübke, J.; Mall, A.; Michelat, T.; Morgan, A.J.; Roth, N.; Samanta, A.K.; Sato, T.; Shen, Z.; Sikorski, M.; Schulz, F.; Spence, J.C.H.; Vagovic, P.; Wollweber, T.; Worbs, L.; Xavier, P.L.; Yefanov, O.; Maia, F.R.N.C.; Horke, D.A.; Küpper, J.; Loh, N.D.; Mancuso, A.P.; Chapman, H.N.; Ayyer, K.

doi:10.1107/S2052252521012707

Figure 6
(a) The distribution of the dataset in 2D latent space, colour coded by CLPCA-2. Black crosses in the plot are the 200 selected latent numbers z. (b) The 〈100〉 direction projections of the 200 real-space densities retrieved from the logarithmic intensity volumes generated from the 200 selected latent numbers z [black crosses in panel (a)]. Semi-transparent slices are volumes generated from latent regions with no or very few data points.

IUCrJ

Volume 9| Part 2| March 2022| Pages 204-214

ISSN: 2052-2525

https://doi.org/10.1107/S2052252521012707

PHYSICS | FELS

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