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Theoretical analysis of frequency modulation-to-amplitude modulation on the final optics and target of the SG Ⅱ-Up laser facility 被引量:1
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作者 Yujia Zhang Wei Fan +9 位作者 Jiangfeng Wang Xiaochao Wang Xinghua Lu Dajie Huang Shouying Xu Yanli Zhang Mingying Sun Zhaoyang Jiao Shenlei Zhou Xiuqing Jiang 《High Power Laser Science and Engineering》 SCIE CAS CSCD 2024年第1期84-98,共15页
Frequency modulation(FM)-to-amplitude modulation(AM) conversion is an important factor that affects the time±power curve of inertial confinement fusion(ICF) high-power laser facilities. This conversion can impact... Frequency modulation(FM)-to-amplitude modulation(AM) conversion is an important factor that affects the time±power curve of inertial confinement fusion(ICF) high-power laser facilities. This conversion can impact uniform compression and increase the risk of damage to optics. However, the dispersive grating used in the smoothing by spectral dispersion technology will introduce a temporal delay and can spatially smooth the target. The combined effect of the dispersive grating and the focusing lens is equivalent to a Gaussian low-pass filter, which is equivalent to 8 GHz bandwidth and can reduce the intensity modulation on the target to below 5% with 0.3 nm @ 3 GHz + 20 GHz spectrum phase modulation. The results play an important role in the testing and evaluating of the FM-to-AM on the final optics and the target, which is beneficial for comprehensively evaluating the load capacity of the facility and isentropic compression experiment for ICF. 展开更多
关键词 dispersion grating frequency modulation-to-amplitude modulation conversion high-power laser facility inertial confinement fusion phase modulation
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Temporal waveform denoising using deep learning for injection laser systems of inertial confinement fusion high-power laser facilities
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作者 Wei Chen Xinghua Lu +1 位作者 Wei Fan Xiaochao Wang 《High Power Laser Science and Engineering》 CSCD 2024年第6期207-220,共14页
For the pulse shaping system of the SG-Ⅱ-up facility, we propose a U-shaped convolutional neural network that integrates multi-scale feature extraction capabilities, an attention mechanism and long short-term memory ... For the pulse shaping system of the SG-Ⅱ-up facility, we propose a U-shaped convolutional neural network that integrates multi-scale feature extraction capabilities, an attention mechanism and long short-term memory units, which effectively facilitates real-time denoising of diverse shaping pulses. We train the model using simulated datasets and evaluate it on both the simulated and experimental temporal waveforms. During the evaluation of simulated waveforms, we achieve high-precision denoising, resulting in great performance for temporal waveforms with frequency modulationto-amplitude modulation conversion(FM-to-AM) exceeding 50%, exceedingly high contrast of over 300:1 and multistep structures. The errors are less than 1% for both root mean square error and contrast, and there is a remarkable improvement in the signal-to-noise ratio by over 50%. During the evaluation of experimental waveforms, the model can obtain different denoised waveforms with contrast greater than 200:1. The stability of the model is verified using temporal waveforms with identical pulse widths and contrast, ensuring that while achieving smooth temporal profiles,the intricate details of the signals are preserved. The results demonstrate that the denoising model, trained utilizing the simulation dataset, is capable of efficiently processing complex temporal waveforms in real-time for experiments and mitigating the influence of electronic noise and FM-to-AM on the time–power curve. 展开更多
关键词 deep learning frequency modulation-to-amplitude modulation conversion inertial confinement fusion SG-II facility temporal waveform denoising
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