用机器学习自动优化极端紫外光源的波前畸变校正。
Bayesian Optimization and Convolutional Neural Networks for Zernike-Based Wavefront Correction in High Harmonic Generation
- 结合贝叶斯优化与卷积神经网络预测最佳泽尼克系数。
- CNN在测试集上达到80.39%准确率，实现高效波前校正。
- 适合激光物理、超快光子学领域研究人员参考。
高次谐波产生（HHG）是一种非线性过程，可在桌面级系统中生成可调谐、高能量、相干且超短的极紫外（EUV）至软X射线辐射脉冲，广泛应用于凝聚态物理中的光电子能谱、高能量密度等离子体的泵浦-探测光谱以及阿秒科学。然而，产生这些脉冲所需的高功率激光系统中的光学像差会降低光束质量并削弱效率。本文提出一种基于机器学习的波前畸变校正方法，利用空间光调制器进行优化。通过实现并对比贝叶斯优化与卷积神经网络（CNN）方法，预测最优的泽尼克多项式系数以实现波前校正。实验结果显示，所提出的CNN模型在测试数据上达到80.39%的准确率，验证了其在HHG系统中实现自动化像差校正的潜力。
原文摘要 · Abstract (English)
High harmonic generation (HHG) is a nonlinear process that enables table-top generation of tunable, high-energy, coherent, ultrashort radiation pulses in the extreme ultraviolet (EUV) to soft X-ray range. These pulses find applications in photoemission spectroscopy in condensed matter physics, pump-probe spectroscopy for high-energy-density plasmas, and attosecond science. However, optical aberrations in the high-power laser systems required for HHG degrade beam quality and reduce efficiency. We present a machine learning approach to optimize aberration correction using a spatial light modulator. We implemented and compared Bayesian optimization and convolutional neural network (CNN) methods to predict optimal Zernike polynomial coefficients for wavefront correction. Our CNN achieved promising results with 80.39% accuracy on test data, demonstrating the potential for automated aberration correction in HHG systems.
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