Speaker
Nathan Majernik
(SLAC National Accelerator Laboratory)
Description
By pairing the effects of a transverse deflecting cavity and dipole magnet, a beam's longitudinal phase space (LPS) can be imaged on a screen. However, the emittance of the beam, chromatic focusing, and other effects are convolved into the resulting screen image, functionally blurring it, reducing the fidelity of the LPS measurement. Here, we explore the use of both conventional, space-variant deconvolution as well as machine-learning approaches to better resolve the LPS.
| I have read and accept the Privacy Policy Statement | Yes |
|---|---|
| Please consider my poster for contributed oral presentation | Yes |
| Would you like to submit this poster in student poster session on Sunday (August 10th) | No |
Author
Nathan Majernik
(SLAC National Accelerator Laboratory)
Co-authors
Auralee Edelen
(SLAC National Accelerator Laboratory)
Brendan O'Shea
(SLAC National Accelerator Laboratory)
Claudio Emma
(SLAC National Accelerator Laboratory)
Douglas Storey
(SLAC National Accelerator Laboratory)
Mark Hogan
(SLAC National Accelerator Laboratory)
Ryan Roussel
(SLAC National Accelerator Laboratory)
