2026 workshop program
Hyrax - A low-code solution for rapid experimentation with machine learning and unsupervised discovery in astronomy.
The work
Abstract
With current and upcoming large astronomical surveys producing data at unprecedented scale, the limiting factor for ML-driven discovery is increasingly not the data itself, but the infrastructure required to work with it. Astronomers routinely spend a significant amount of their time on data wrangling, configuration management, and bespoke pipeline engineering — effort that comes directly at the expense of science; and is often not reusable by other research groups/teams resulting in duplicated effort.
Hyrax is an extensible GPU-enabled framework that provides infrastructure for the full ML lifecycle in astronomy: from data acquisition and training to inference and experiment comparison, with capabilities including multimodal dataset support, integrated vector databases for similarity search, and interactive 2D/3D latent-space exploration for unsupervised discovery.