Unlocking Latent Dimensions: Exploring Representations of Large-Scale X-ray Scattering Data using Variational Autoencoders
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Computer Science > Machine Learning
arXiv:2606.14999 (cs)
[Submitted on 12 Jun 2026]
Title:Unlocking Latent Dimensions: Exploring Representations of Large-Scale X-ray Scattering Data using Variational Autoencoders
Authors:Monika Choudhary, Xiaoya Chong, Runbo Jiang, Wiebke Koepp, Petrus H. Zwart, Damon English, Gregory M. Su, Eric Schaible, Chenhui Zhu, Mostafa Nassr, Noah P. Wamble, Kelvin Kam-Yun Li, Jonathan M. Chan, Jose Carlos Diaz, Cameron McKay, Lynn Katz, Benny Freeman, Guillaume Freychet, Yevgen Matviychuk, Eliot Gann, Daniel B. Allan, Benedikt Sochor, Frank Schluenzen, Stephan V. Roth, Ethan Crumlin, Dylan McReynolds, Tanny Chavez, Alexander Hexemer
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Abstract:Scientific user facilities generate X-ray scattering data faster than traditional workflows can process them. We address this challenge across two settings, offline dataset exploration and live on-the-fly analysis. We train a domain-specific attention-based Convolutional Variational Autoencoder (C-VAE) on 1.5 million X-ray scattering images to learn low-dimensional representations capturing structural variation across diverse experimental conditions. The learned latent space reveals well-organized clusters and smooth trajectories reflecting experimental progression. It further supports controlled synthetic scattering image generation across diverse structural states. When deployed without retraining, the model organizes time-resolved film formation experiments at two synchrotron facilities into interpretable latent structures. Benchmarking against DINOv3 (ViT-7B), a general-purpose vision foundation model, demonstrates that domain-specific training yields more interpretable latent organization for scattering data. Both workflows are integrated within Latent Space Explorer, a component of the MLExchange platform, supporting interactive structural exploration across archived datasets and live experiments.
| Subjects: | Machine Learning (cs.LG) |
| Cite as: | arXiv:2606.14999 [cs.LG] |
| (or arXiv:2606.14999v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2606.14999
arXiv-issued DOI via DataCite (pending registration)
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