Unlocking Latent Dimensions: Exploring Representations of Large-Scale X-ray Scattering Data using Variational Autoencoders
This work provides a practical solution for accelerating the analysis of large-scale X-ray scattering data at scientific user facilities, enabling both offline exploration and live on-the-fly analysis.
The authors developed a domain-specific attention-based convolutional variational autoencoder (C-VAE) trained on 1.5 million X-ray scattering images, which learns low-dimensional latent representations that organize experimental data into interpretable clusters and trajectories, and enables controlled synthetic image generation. The model generalizes to time-resolved experiments at two synchrotron facilities without retraining and outperforms a general-purpose vision foundation model (DINOv3) in interpretability.
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.