LEAPS Integrated Platform Workshop

Session

Virtual Diagnostic

12 May 2021, 17:30

Online (Zoom)

Conveners

Virtual Diagnostic: Overview talk + contributed talks

• Chair : Eugenio Ferrari (PSI - Paul Scherrer Institut)

38. Accurate and confident prediction of electron beam longitudinal properties using spectral virtual diagnostics

Adi Hanuka (SLAC)

12/05/2021, 17:30

Longitudinal phase space (LPS) provides a critical information about electron beam dynamics for various scientific applications. For example, it can give insight into the high-brightness X-ray radiation from a free electron laser. Existing diagnostics are invasive, and often times cannot operate at the required resolution. In this work we present a machine learning-based Virtual Diagnostic...

39. Transverse Beam Emittance Measurement by Undulator Radiation Power Noise

Ihar Lobach (University of Chicago)

12/05/2021, 18:05

Generally, turn-to-turn power fluctuations of incoherent spontaneous synchrotron radiation in a storage ring depend on the 6D phase-space distribution of the electron bunch. In some cases, if only one parameter of the distribution is unknown, this parameter can be determined from the measured magnitude of these power fluctuations. In this contribution, we report the results of our experiment...

40. Convolutional neural networks application in virtual diagnostics

Xingyi Xu (SINAP)

12/05/2021, 18:25

A large amount of information extraction work is done in the frequency domain. Information at different frequencies contains different physical meanings. The convolution kernel is also called a filter, because the convolution process is actually a filtering process. This means that a deep convolutional neural network is like a string of intelligent filter banks. It is helpful for the...

41. Mapping photocathode quantum efficiency with ghost imaging

Eric Cropp (UCLA)

12/05/2021, 18:45

Electron ghost imaging has been established as a viable method that allows for advantages over traditional methods, such as making use of compressed sensing and a resolution increase from Fellgett's Advantage [1]. It has been applied to passive photocathode quantum efficiency mapping [2] and improving resolution within this context is discussed.

[1] S. Li, F. Cropp, K. Kabra, T. J. Lane,...

42. Deep Learning-Based Autoencoder for Data-Driven Modeling of the European XFEL Photoinjector

Jun Zhu (DESY)

12/05/2021, 19:05

We present data-driving modeling of the European XFEL photoinjector using a deep learning-based autoencoder. We show that the autoencoder trained only with experimental data can make high-fidelity predictions of megapixel images
for the longitudinal phase-space measurement. We also discuss the practical challenges of building such an intelligent system for operation and propose a pragmatic...