Speaker
Description
As AI/ML‑enabled workflows become increasingly central to accelerator and photon‑science experiments, the need for flexible, metadata‑rich, and automation‑friendly control systems has grown substantially. Bluesky provides a modern Python‑based framework for experiment orchestration, data acquisition, and real‑time feedback, making it a strong candidate for integration with next‑generation AI/ML accelerator test facilities. This contribution will outline Bluesky’s architecture and its role within photon‑science operations, including its capabilities for streaming data, coordinated device control, and enabling adaptive experimentation.
The talk will highlight practical integration challenges observed at photon beamlines: managing heterogeneous hardware interfaces, enabling continuous scanning, scaling data workflows for ML‑based analysis, and aligning metadata and data management with facility‑level requirements. We will discuss strategies that have proven effective in real deployments, as well as opportunities for deeper coupling between Bluesky and dedicated AI/ML accelerator infrastructures to support autonomous experimental pipelines.
