Abstract The application high intensity ultrafast lasers to compact plasma-based electron accelerators has recently been an extremely active area of research. Here, for the first time, we show experimentally and theoretically that carefully sculpting an intense ultrafast pulse in the spatio-temporal domain allows ponderomotive pressure to be used for direct acceleration of electron bunches from rest to relativistic energies. With subluminal group velocity and above-threshold intensity, a laser pulse can capture and accelerate electrons, pushing on them like a snowplow. Acceleration of electrons from rest requires a substantial reduction of group velocity. In this demonstration experiment, we achieve a group velocity of ∼0.6c in a tilted pulse by focusing the output of a novel asymmetric pulse compressor we developed for the petawatt-class ALEPH system at Colorado State University. This direct laser-electron approach opens a route towards exploiting optical spatio-temporal control techniques to sculpt electron beams with desired properties such as narrow energy and angular distributions. The tilted-pulse snowplow technique can be scaled from small-scale to facility-scale amplifiers to produce short electron bunches in the 10 keV−10 MeV range for applications including ultrafast electron diffraction and efficient injection into laser wakefield accelerators for acceleration beyond the GeV level.
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Towards a Data Science Enabled MeV Ultrafast Electron Diffraction System
A MeV ultrafast electron diffraction (MUED) instrument is a unique characterization technique to study ultrafast processes in materials by a pump-probe technique. This relatively young technology can be advanced further into a turn-key instrument by using data science and artificial intelligence (AI) mechanisms in conjunctions with high-performance computing. This can facilitate automated operation, data acquisition and real time or near- real time processing. AI based system controls can provide real time feedback on the electron beam which is currently not possible due to the use of destructive diagnostics. Deep learning can be applied to the MUED diffraction patterns to recover valuable information on subtle lattice variations that can lead to a greater understanding of a wide range of material systems. A data science enabled MUED facility will also facilitate the application of this technique, expand its user base, and provide a fully automated state-of-the-art instrument. We will discuss the progress made on the MUED instrument in the Accelerator Test Facility of Brookhaven National Laboratory.
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- Award ID(s):
- 1757207
- PAR ID:
- 10315809
- Date Published:
- Journal Name:
- International Particle Accelerator Conference (12th)
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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