Exploring Reinforcement Learning for Optimal Bunch Merge in the AGS

Not scheduled
20m
80/1-001 - Globe of Science and Innovation - 1st Floor (CERN)

80/1-001 - Globe of Science and Innovation - 1st Floor

CERN

Esplanade des Particules 1, 1211 Meyrin, Switzerland
60
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Poster Optimisation and Control Poster session

Speaker

Georg Hoffstaetter

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. Initial experimental results and ongoing system developments are presented and discussed.

Author

Co-authors

Armen Kasparian (JLab) Daria Kuzovkova (Cornell Univ.) Eiad Hamwi Freddy F Severino (Brookhaven National Lab) Georg Hoffstaetter John Morris (Brookhaven National Lab) Jonathan Unger (Cornell Univ.) Keith Zeno (Brookhaven National Lab) Kevin Brown (Brookhaven National Laboratory) Levente Hajdu (Brookhaven National Lab) Malachi Schram Matthew Signorelli (Cornell Univ.) Vincent Schoefer Weijian Lin (Brookhaven National Laboratory) Xiaofeng Gu (Brookhaven National Lab) Yinan Wang (RPI)

Presentation materials

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