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

Optimization of AGS bunch merging with reinforcement learning

TUPS53
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

Yuan Gao (Brookhaven National Laboratory)

Description

The RHIC heavy ion program relies on a series of RF bunch merge gymnastics to combine individual source pulses into bunches of suitable intensity. Intensity and emittance preservation during these gymnastics require careful setup of the voltages and phases of RF cavities operating at several different harmonic numbers. The optimum setting tends to drift over time, degrading performance and requiring operator attention to correct. We describe a reinforcement learning approach to learning and maintaining an optimum configuration, accounting for the relevant RF parameters and external perturbations (e.g., a changing main dipole field) using a physics-based simulator at Brookhaven Alternating Gradient Synchrotron (AGS).

Funding Agency

Work supported by Brookhaven Science Associates, LLC under Contract No. DE-SC0012704 with the U.S. Department of Energy.

Region represented North America
Paper preparation format LaTeX

Primary author

Yuan Gao (Brookhaven National Laboratory)

Co-authors

Armen Kasparian (Jefferson Lab) Auralee Edelen (SLAC National Accelerator Laboratory) David Sagan (Cornell University (CLASSE)) Eiad Hamwi (Cornell University (CLASSE)) Georg Hoffstaetter (Cornell University (CLASSE)) Jonathan Unger (Cornell University (CLASSE)) Keith Zeno (Brookhaven National Laboratory) Kevin Brown (Brookhaven National Laboratory) Linh Nguyen (Brookhaven National Laboratory) Malachi Schram (Thomas Jefferson National Accelerator Facility) Vincent Schoefer (Brookhaven National Laboratory) Weijian Lin (Cornell University (CLASSE)) Yinan Wang (Rensselaer Polytechnic Institute)

Presentation materials

There are no materials yet.