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RIS citation export for TUPA84: Reconstructing Beam Parameters from Betatron Radiation Through Machine Learning and Maximum Likelihood Estimation

TY  - CONF
AU  - Zhang, S.
AU  - Apsimon, Ö.
AU  - Majernik, N.
AU  - Naranjo, B.
AU  - Rosenzweig, J.B.
AU  - Welsch, C.P.
AU  - Yadav, M.
ED  - Biedron, Sandra
ED  - Simakov, Evgenya
ED  - Milton, Stephen
ED  - Anisimov, Petr M.
ED  - Schaa, Volker R.W.
TI  - Reconstructing Beam Parameters from Betatron Radiation Through Machine Learning and Maximum Likelihood Estimation
J2  - Proc. of NAPAC2022, Albuquerque, NM, USA, 07-12 August 2022
CY  - Albuquerque, NM, USA
T2  - International Particle Accelerator Conference
T3  - 5
LA  - english
AB  - The dense drive beam used in plasma wakefield acceleration generates a linear focusing force that causes electrons inside the witness beam to undergo betatron oscillations, giving rise to betatron radiation. Because information about the properties of the beam is encoded in the betatron radiation, measurements of the radiation such as those recorded by the UCLA-built Compton spectrometer can be used to reconstruct beam parameters. Two possible methods of extracting information about beam parameters from measurements of radiation are machine learning (ML), which is increasingly being implemented for different fields of beam diagnostics, and a statistical technique known as maximum likelihood estimation (MLE). We assess the ability of both machine learning and MLE methods to accurately extract beam parameters from measurements of betatron radiation.
PB  - JACoW Publishing
CP  - Geneva, Switzerland
SP  - 527
EP  - 530
KW  - radiation
KW  - betatron
KW  - simulation
KW  - diagnostics
KW  - plasma
DA  - 2022/10
PY  - 2022
SN  - 2673-7000
SN  - 978-3-95450-232-5
DO  - doi:10.18429/JACoW-NAPAC2022-TUPA84
UR  - https://jacow.org/napac2022/papers/tupa84.pdf
ER  -