30 March 2026 to 1 April 2026
University of Liverpool
Europe/London timezone

Reinforcement Learning for Optimal Bunch Merge in the AGS

Not scheduled
2h
Teaching Hub 502 First Floor (University of Liverpool)

Teaching Hub 502 First Floor

University of Liverpool

Speaker

Eiad Hamwi (Brookhaven National Laboratory)

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.

Student No

Primary author

Yuan Gao

Co-authors

Andrei Sukhanov Armen Kasparian (Jefferson Lab) Daria Kuzovkova David Sagan Eiad Hamwi (Brookhaven National Laboratory) Freddy Severino Georg Heinz Hoffstaetter John Morris Jonathan Unger Keith Zeno Kevin Brown Levente Hajdu Linh Nguyen Malachi Schram Matthew Signorelli Michael Costanzo Shruti Tajne Vincent Schoefer Xiaofeng Gu Yinan Wang

Presentation materials

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