AI 中文总结
该研究在TOTO测试平台上，将经模拟数据训练并补充真实焦面多样性数据的机器学习模型，用于低阶波前像差传感，其预测结果与真实Zernike系数合理一致，验证了算法的有效性。
AI 中文摘要
相位检索技术被用于校正空间望远镜概念中光学系统失准导致的低阶波前像差。传统相位检索需观测点扩散函数（PSF）及多样性测量（通常为焦面多样性，也可采用其他测量方式），以重建科学探测器处的入射波前。本研究采用最初基于模拟数据训练的机器学习模型，补充亚利桑那大学Tiny Observatory for Telescope Optimization（TOTO）测试平台的真实焦面多样性数据后，将该模型的波前传感性能与生成数据集的已知真值进行对比。对TOTO数据训练验证后，模型对低阶Zernike多项式的预测与真实Zernike系数表现出合理一致性。
英文摘要
Phase retrieval techniques are utilized to correct low order wavefront aberrations originating from misalignments of the optical system in space based telescope concepts. Traditional phase retrieval involves observation of the Point Spread Function (PSF) and a diversity measurement, usually focus diversity although other measures are possible, to reconstruct the incident wavefront at the science detector. We consider a Machine Learning model trained originally on simulated data, and then augmented with real focus diversity data from the Tiny Observatory for Telescope Optimization (TOTO) testbed at the University of Arizona. We then compare the wavefront sensing performance of the Machine Learning model with known truth values of the generated dataset. The model predictions for low order Zernikes on TOTO data after training and validation show a reasonable agreement with the true Zernike coefficients.
