We propose a method for auto-optimized compensation of pixel alignment and overall curvature in digital optical phase conjugation system named AOC-DOPC system. The theory of the AOC-DOPC system is described, and the optimized compensation capability of AOC-DOPC system is verified experimentally in the situation of system instability, overall curvature and pixel match misalignment. With the proposed system, the compensation effect is improved, the size and shape of the focus are more similar to the target pattern. Compared with the DOPC, the PIB curve showed a decrease from 0.162 to 0.007 of area ratio of 50% energy with AOC-DOPC compensation, which is about 16 times relative to the DOPC compensation. Besides, the correlation coefficient (R) increases from 0.0465 to 0.7743, which shows 3.4 times of improvement of compensation effect.
In this paper, a high efficiency method to generate vector beam based on a single liquid crystal spatial light modulator (LCSLM) is proposed. In this method, the system used to generate vector beam adopts a collinear configuration which makes the system more stable and the core components of the system include a half-wave plate, a reflective phase-only LCSLM and a quarter-wave plate. With the proposed system, the polarization states distribution of output beam could be modulated by controlling the phase pattern displayed on LCSLM and the relative intensity of the two orthogonal components in the beam reflected by LCSLM. We conducted a theoretical analysis of the method and demonstrated the validity and feasibility of the method experimentally. The experiment results are highly consistent with the results obtained through theoretical simulations.
Accurate temporal characterization both in intensity and phase distribution is important in the diagnosis of the petawatt (PW) class. We present a single-shot picosecond frequency-resolved optical gating (ps-FROG) setup based on an autocorrelator with ps measurement range that is spectrally resolved through a fine grating. The modified ptychographic-based algorithm with a changing update coefficient was used for the reconstruction of the pulse distribution; it can better adapt to the reconstruction of pulse with a large time–bandwidth product. We calibrated and verified the homemade ps-FROG in a 100-μJ ps laser system and used it to characterize the pulse distribution generated by the PW laser system of the Shen Guang II facility. The system shows good performance and high accuracy in reconstructing the intensity and phase distributions of a ps pulse, which provides reference for accurately adjusting the grating pair to acquire the pulse width as a preset.
The spectrum is a crucial parameter to a petawatt laser which is adopting the chirped pulse amplification technique. In such complex systems with high gain and wide spectrum bandwidth, the shape of the spectrum is crucial to the final output pulse width. In daily operation, the width of the compressed pulse will have some abnormal fluctuation, and the shape of the spectrum before compressed is also changed at the same shot. It will mislead the power and intensity estimation in laser-matter interaction experiments. So far, no theory has been able to analyze the relationship between spectrum and pulse width completely. Because it is hard to describe the fluctuation of the compressed pulse width which the online measure spectral phase in the high power laser system is difficult. In this paper, we first found and analyzed the relation between spectral variation and pulse width in the petawatt laser. With the support of existing data, we establish an end-toend deep learning model to map the petawatt laser’s spectrum before the compressor to the compressed pulse width. The deep learning scheme which based on Bayesian Neural Network (BNN) can provide an estimate of uncertainty as a function of pulse width to improve the accuracy of the model. After 20000 iterations, the Mean Square Error (MSE) is reduced to 0.08 in the validation test. Under the experiment, the model realizes an effective predict of the compressed pulse width. With the help of deep learning, we can get more information on the spectrum rather than the center wavelength and spectrum width to predict the compressed pulse width. It should be emphasized that this method will help to avoid unstable pulse output caused by an abnormal spectrum and to improve the operating efficiency of the petawatt laser system.
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