19–23 Aug 2024
POLIN
Europe/Warsaw timezone

Session

FEL theory

MOB
19 Aug 2024, 14:30
POLIN

POLIN

Mordechaja Anielewicza 6 00-157 Warszawa Poland

Conveners

FEL theory: Session-2

  • Avraham Gover (Tel Aviv University)

Presentation materials

There are no materials yet.

Eléonore Roussel (Laboratoire de Physique des Lasers, Atomes et Molécules)
19/08/2024, 14:30
FEL theory
Invited Oral Presentation

In high-gain harmonic generation (HGHG) FEL, the amplification process is triggered by an extrernal seed laser that creates periodic energy modulation in the electron phase-space, which is then converted into a density modulation in a dispersive section. This density modulation presents strong current spikes at the seed laser wavelength that leads to the harmonic bunching in the forthcoming...

River Robles (Stanford University)
19/08/2024, 15:05
FEL theory
Invited Oral Presentation

The soliton-like superradiant regime of free-electron lasers (FEL) offers a promising path towards ultrashort pulses, beyond the natural limit dictated by the bandwidth of the high-gain FEL instability. In this work we present a three-dimensional theory of the superradiant regime. Our work takes advantage of recent developments in non-linear FEL theory to provide a fully analytical description...

Bin Zhang (Tel Aviv University)
19/08/2024, 15:40
FEL theory
Contributed Oral Presentation

We present a fundamental QED theory for superradiance of bunched electrons - the enhanced coherent spontaneous emission of a correlated electron beam in an FEL configuration or in a variety of other free-electron radiation mechanisms. We derive the buildup of a quantum radiation state by multiple electrons in a configuration of a narrow beam of quantum electron wavepackets passing by a...

Petr Anisimov (Los Alamos National Laboratory)
19/08/2024, 16:00
FEL theory
Contributed Oral Presentation

Predicting X-ray Free Electron Laser (XFEL) performance using Genesis simulation code is standard approach in designing future XFELs. Running this code is time consuming that slows down exploration of the parameter space during the design stage. Thus, using surrogate models based on machine learning techniques is often employed. These models however do not know about physics behind the...

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