Unlocking Latent Dimensions: Exploring Representations of Large-Scale X-ray Scattering Data using Variational Autoencoders

2026-06-12

AI Summary

X-ray scattering data are generated at rates far exceeding the capacity of conventional analysis pipelines, creating an urgent need for efficient computational methods. This work proposes the first attention-based convolutional variational autoencoder (C-VAE) tailored to this domain, which learns interpretable low-dimensional latent representations from 1.5 million scattering images. Without requiring fine-tuning, the model successfully captures structural evolution dynamics across time-resolved experiments conducted at different synchrotron facilities. It substantially outperforms general-purpose vision foundation models, revealing dynamic structural changes in two thin-film formation studies while enabling high-fidelity synthetic image generation and real-time data organization. Integration with the MLExchange platform and the Latent Space Explorer tool further supports interactive offline exploration and online analysis.

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.

Problem

• X-ray scattering
• large-scale data
• latent representation
• scientific user facilities
• data exploration

Innovation

• Variational Autoencoder
• X-ray Scattering
• Latent Space
• Domain-specific Model
• Attention-based CNN