19–24 May 2024
Music City Center
US/Central timezone

Overview of machine learning based beam size control during user operation at the Advanced Light Source

TUPS63
21 May 2024, 16:00
2h
Blues (MCC Exhibit Hall A)

Blues

MCC Exhibit Hall A

Poster Presentation MC6.D13 Machine Learning Tuesday Poster Session

Speaker

Thorsten Hellert (Lawrence Berkeley National Laboratory)

Description

The Advanced Light Source (ALS) storage ring employs various feedback and feedforward systems to stabilize the circulating electron beam thus ensuring delivery of steady synchrotron radiation to the users. In particular, active correction is essential to compensate for the significant perturbations to the transverse beam size induced by user-controlled tuning of the insertion devices, which occurs continuously during normal operation. Past work at the ALS already offered a proof-of-principle demonstration that Machine Learning (ML) methods could be used successfully for this purpose. Recent work has led to the development of a more robust ML-algorithm capable of continuous retraining and its routine deployment into day-to-day machine operation. In this contribution we focus on technical aspects of gathering the training data and model analysis based on archived data from 2 years of user operation as well as on the model implementation including the interface of an EPICS Input/Output Controller (IOC) into a Phoebus Panel, enabling operator-level supervision of the Beam Size Control (BSC) tool during regular user operation.

Region represented North America
Paper preparation format LaTeX

Primary author

Thorsten Hellert (Lawrence Berkeley National Laboratory)

Co-authors

Andrea Pollastro (Lawrence Berkeley National Laboratory) Hiroshi Nishimura (Lawrence Berkeley National Laboratory) Marco Venturini (Lawrence Berkeley National Laboratory) Simon Leemann (Lawrence Berkeley National Laboratory) Tynan Ford (Lawrence Berkeley National Laboratory)

Presentation materials

There are no materials yet.