1–6 Jun 2025
Taipei International Convention Center (TICC)
Asia/Taipei timezone

Exploring reinforcement learning for optimal bunch merge in the AGS

THPM107
5 Jun 2025, 15:30
2h
Exhibiton Hall A _Magpie (TWTC)

Exhibiton Hall A _Magpie

TWTC

Poster Presentation MC6.D13 Machine Learning Thursday Poster Session

Speaker

Georg Hoffstaetter (Cornell University (CLASSE))

Description

In BNL’s Booster, the beam bunches can be split into two or three smaller bunches to reduce their space-charge forces. They are then merged back after acceleration in the Alternating Gradient Synchrotron (AGS). This acceleration with decreased space-charge forces can reduce the final emittance, increasing the luminosity in RHIC and improving proton polarization. Parts of this procedure have already been tested and are proposed for the Electron-Ion Collider (EIC). The success of this procedure relies on a series of RF gymnastics to merge individual source pulses into bunches of suitable intensity. In this work, we explore an RF control scheme using reinforcement learning (RL) to merge bunches, aiming to dynamically adjust RF parameters to achieve minimal longitudinal emittance growth and stable bunch profiles. Machine experimental results and system developments are presented and discussed.

Region represented America
Paper preparation format LaTeX

Author

Yuan Gao (Brookhaven National Laboratory)

Co-authors

Armen Kasparian (Thomas Jefferson National Accelerator Facility) Daria Kuzovkova (Cornell University (CLASSE)) Eiad Hamwi (Cornell University (CLASSE)) Freddy Severino (Brookhaven National Laboratory) Georg Hoffstaetter (Cornell University (CLASSE)) John Morris (Brookhaven National Laboratory) Jonathan Unger (Cornell University (CLASSE)) Keith Zeno (Brookhaven National Laboratory) Kevin Brown (Brookhaven National Laboratory) Levente Hajdu (Brookhaven National Laboratory) Malachi Schram (Thomas Jefferson National Accelerator Facility) Matthew Signorelli (Cornell University (CLASSE)) Vincent Schoefer (Brookhaven National Laboratory) Weijian Lin (Brookhaven National Laboratory) Xiaofeng Gu (Brookhaven National Laboratory) Yinan Wang (Rensselaer Polytechnic Institute)

Presentation materials

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