100 research outputs found

    Generation of Multi-Color Attosecond X-Ray Radiation Through Modulation Compression

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    In this paper, we propose a scheme to generate tunable multi-color attosecond coherent X-ray radiation for future light source applications. This scheme uses an energy chirped electron beam, a laser modulators, a laser chirper and two bunch compressors to generate a multi-spike prebunched kilo-Ampere current electron beam from a few tens Ampere electron beam out of a linac. Such an electron beam transports through a series of undulator radiators and bunch compressors to generate multi-color coherent X-ray radiation. As an illustration, we present an example to generate two attosecond pulses with 2.22.2 nm and 33 nm coherent X-ray radiation wavelength and more than 200200 MW peak power using a 3030 Ampere 200200 nm laser seeded electron beam

    Machine learning-based direct solver for one-to-many problems of temporal shaping of electron bunches

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    To control the temporal profile of a charged beam to meet requirements of various accelerator applications, a widely-used technique is bunch compression via 4-dipole chicanes that may sometimes have a one-to-many map. Current approaches based on stochastic optimization or supervised learning can be limited because of the one-to-many properties. Here we demonstrate how to construct a direct and real-time solver with the aid of a semi-supervised machine learning method, the conditional generative adversarial network (CGAN), to solve one-to-many problems of temporal shaping. Unlike supervised learning that can only learn one-to-one maps, the CGAN solver can learn the one-to-many dynamics and accurately predict required longitudinal dispersion terms for a chicane to realize desired custom temporal profiles without any priori knowledge. Besides, the CGAN solver can simultaneously give multiple different solutions for a one-to-many problem, which breaks the limitation of stochastic optimization methods of finding one solution instead of many
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