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Description
In free-electron laser facilities, the amplitude-phase flatness of the Radio Frequency (RF) pulses driving the electron beam is a key factor determining beam energy spread. Imperfections in the RF driving chain, such as the static nonlinearities of the vector modulator and the high-power amplifier, can significantly increase the intra-pulse flattop amplitude error and degrade the pulse flatness. To address this, this paper proposes a U-Net deep neural network-based amplitude feedforward compensation algorithm for Low-Level Radio Frequency (LLRF) systems to suppress intra-pulse amplitude fluctuations. The algorithm has been validated at the output of a Solid-State Amplifier (SSA): under four randomly selected LLRF output configurations (amplitude, pulse width, and pulse delay), the average intra-pulse amplitude flatness (RMS) was reduced from 1.208 % to 0.398 %, and the average peak-to-peak variation was reduced from 4.683 % to 1.353 %, demonstrating a significant compensation effect
| Paper status | Resubmitted proceeding files received and assigned to an editor. Accepted by Submitter. |
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