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 - TY - CONF AU - Burger, N. AU - Andonian, G. AU - Cook, N.M. AU - Denham, P.E. AU - Diaw, A. AU - Gavryushkin, D.I. AU - Hodgetts, T.J. AU - Lamure, A.-L.M.S. AU - Musumeci, P. AU - Norvell, N.P. AU - Ody, A. AU - Ruelas, M. 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 - Experimental Characterization of Gas Sheet Transverse Profile Diagnostic 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 - Transverse profile diagnostics for high-intensity beams require solutions that are non-intercepting and single-shot. In this paper, we describe a gas-sheet ionization diagnostic that employs a precision-shaped, neutral gas jet. As the high-intensity beam passes through the gas sheet, neutral particles are ionized. The ionization products are transported and imaged on a detector. A neural-network based reconstruction algorithm, trained on simulation data, then outputs the initial transverse conditions of the beam prior to ionization. The diagnostic is also adaptable to image the photons from recombination. Preliminary tests at low energy are presented to characterize the working principle of the instrument, including comparisons to existing diagnostics. The results are parametrized as a function of beam charge, spot size, and bunch length. PB - JACoW Publishing CP - Geneva, Switzerland SP - 907 EP - 909 KW - diagnostics KW - electron KW - laser KW - operation KW - MMI DA - 2022/10 PY - 2022 SN - 2673-7000 SN - 978-3-95450-232-5 DO - doi:10.18429/JACoW-NAPAC2022-THZE4 UR - https://jacow.org/napac2022/papers/thze4.pdf ER -