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Learning the lantern: neural network applications to broadband photonic lantern modeling
Journal of Astronomical Telescopes, Instruments, and Systems ( IF 2.3 ) Pub Date : 2021-06-01 , DOI: 10.1117/1.jatis.7.2.028007
David Sweeney 1 , Barnaby R. M. Norris 1 , Peter Tuthill 1 , Richard Scalzo 2 , Jin Wei 1 , Christopher H. Betters 1 , Sergio G. Leon-Saval 1
Affiliation  

Photonic lanterns (PLs) allow the decomposition of highly multimodal light into a simplified modal basis such as single-moded and/or few-moded. They are increasingly finding uses in astronomy, optics, and telecommunications. Calculating propagation through a PL using traditional algorithms takes ∼1 h per simulation on a modern CPU. We demonstrate that neural networks can bridge the disparate opto-electronic systems and, when trained, can achieve a speedup of over five orders of magnitude. We show that this approach can be used to model PLs with manufacturing defects and can be successfully generalized to polychromatic data. We demonstrate two uses of these neural network models: propagating seeing through the PL and performing global optimization for purposes such as PL funnels and PL nullers.

中文翻译:

学习灯笼:神经网络在宽带光子灯笼建模中的应用

光子灯 (PL) 允许将高度多模态的光分解为简化的模态基础,例如单模和/或少模。它们越来越多地用于天文学、光学和电信。使用传统算法计算通过 PL 的传播在现代 CPU 上每次模拟需要大约 1 小时。我们证明神经网络可以桥接不同的光电系统,并且在训练后可以实现超过五个数量级的加速。我们表明这种方法可用于对具有制造缺陷的 PL 进行建模,并且可以成功地推广到多色数据。我们展示了这些神经网络模型的两种用途:通过 PL 传播观察和执行全局优化,例如 PL 漏斗和 PL 归零器。
更新日期:2021-06-30
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