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RIS citation export for WEPOMS057: Simulation Studies and Machine Learning Applications at the Coherent electron Cooling experiment at RHIC

TY  - CONF
AU  - Lin, W.
AU  - Crittenden, J.A.
AU  - Hoffstaetter, G.H.
AU  - Jing, Y.C.
AU  - Sampson, M.A.
AU  - Shih, K.
ED  - Zimmermann, Frank
ED  - Tanaka, Hitoshi
ED  - Sudmuang, Porntip
ED  - Klysubun, Prapong
ED  - Sunwong, Prapaiwan
ED  - Chanwattana, Thakonwat
ED  - Petit-Jean-Genaz, Christine
ED  - Schaa, Volker R.W.
TI  - Simulation Studies and Machine Learning Applications at the Coherent electron Cooling experiment at RHIC
J2  - Proc. of IPAC2022, Bangkok, Thailand, 12-17 June 2022
CY  - Bangkok, Thailand
T2  - International Particle Accelerator Conference
T3  - 13
LA  - english
AB  - Coherent electron cooling is a novel cooling technique which cools high-energy hadron beams rapidly by amplifying the modulation induced by hadrons in electron bunches. The Coherent electron cooling (CeC) experiment at Brookhaven National Laboratory (BNL) is a proof-of-principle test facility to demonstrate this technique. To achieve efficient cooling performance, electron beams generated in the CeC need to meet strict quality standards. In this work, we first present sensitivity studies of the low energy beam transport (LEBT) section, in preparation for building a surrogate model of the LEBT line in the future. We also present preliminary test results of a machine learning (ML) algorithm developed to improve the efficiency of slice-emittance measurements in the CeC diagnostic line.
PB  - JACoW Publishing
CP  - Geneva, Switzerland
SP  - 2387
EP  - 2390
KW  - LEBT
KW  - electron
KW  - emittance
KW  - quadrupole
KW  - solenoid
DA  - 2022/07
PY  - 2022
SN  - 2673-5490
SN  - 978-3-95450-227-1
DO  - doi:10.18429/JACoW-IPAC2022-WEPOMS057
UR  - https://jacow.org/ipac2022/papers/wepoms057.pdf
ER  -