AI 中文总结
研究混合模式选择光子灯笼波前校正的低信噪比问题，用神经网络将波前传感核心强度映射为估计波前校正并应用于上游变形镜，探索不同神经网络架构评估性能以解决信噪比权衡。
AI 中文摘要
混合模式选择光子灯笼将输入复点扩展函数转换为多个单模输出，神经网络将波前传感核心强度映射为估计波前校正并应用于上游变形镜。但在为光子仪器保留光量与波前传感核心信噪比降低之间存在权衡。我们探索了用于甚大望远镜干涉仪阿斯加德套件一部分的Seidr仪器的波前校正。我们评估了不同的神经网络架构，比较了不同波前误差类型的波前估计性能，作为解决信噪比权衡的第一步。
英文摘要
Hybrid mode-selective photonic lanterns transform an input complex point-spread function into several single-mode outputs, where a selected core feeds the fundamental mode to a photonic science instrument, while the remaining cores are used for wavefront sensing in a closed-loop adaptive optics system. A neural network maps the intensities of the wavefront sensing cores to an estimated wavefront correction, which is applied to an upstream deformable mirror. However, there exists a trade between maximizing the amount of light reserved for the photonic instrument and the reduced signal-to-noise ratios for the wavefront sensing cores. We explore wavefront correction for the Seidr instrument, a part of the Asgard Suite for the Very Large Telescope Interferometer. We evaluate different neural network architectures, comparing wavefront estimation performance for different wavefront error types, as a first step toward addressing the signal-to-noise trade-off. Results show transformer neural networks as a promising solution for temporal photonic lantern-based wavefront estimation.
