15–17 Jul 2026
SLAC
America/Los_Angeles timezone

Toward Autonomous Beamline Tuning and Control at the Argonne Wakefield Accelerator

16 Jul 2026, 10:20
20m
48/1-112A/B/C/D - Redwood A/B/C/D (SLAC)

48/1-112A/B/C/D - Redwood A/B/C/D

SLAC

90
AI/ML Applications: AI/ML and Accelerators AI/ML Applications

Speaker

Anthony Tran (Argonne National Lab)

Description

The Argonne Wakefield Accelerator (AWA) is a flexible user facility and testbed for accelerator physics, radiofrequency technology, and advanced accelerator concepts. Recent efforts include migrating controls to EPICS for greater standardization and portability across facilities, alongside the addition of a new low-level RF system. These hardware upgrades have enabled the deployment of Bayesian optimization for automatic tuning and generative phase-space reconstruction. Further expanding these capabilities to integrate HPC resources for online analysis, deploy online digital twins, and further extend autonomous control to complex tasks is our main goal. This contribution will report on current progress and near-term plans.

Authors

Anthony Tran (Argonne National Lab) Alexander Ody (Argonne National Lab) Liu Wanming (Argonne National Lab) Philippe Piot (Argonne National Laboratory) John Power (Argonne National Laboratory) Tasfia Yeashna (NIU) Ryan Roussel (SLAC)

Presentation materials