Open-loop tomography with artificial neural networks on CANARY: on-sky results
Open-loop tomography with artificial neural networks on CANARY: on-sky results
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DOI：
10.1093/mnras/stu758
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发表时间：
2014-07-01
影响因子：
4.8
通讯作者：
Rousset, G.
中科院分区：
文献类型：
--
作者：
Osborn, J.;Guzman, D.;Rousset, G.
我们介绍了基于人工神经网络(ANN)的层析重建器基于机器学习的复杂大气重建器(Carmen)在金丝雀上的初步测试结果，这是一个运行在拉帕尔马4.2米威廉·赫歇尔望远镜上的自适应光学演示器。用学而用(L&A)断层重建仪将重建结果与同期数据进行比较。我们发现，完全优化的L&A层析重建器在斯特雷尔比上比卡门高出约5%，在波前误差上比卡门高出15nmRMS。我们还给出了金丝雀在地面层自适应光学模式下的结果，表明重建器是层析的。结果是可比较的，这种小的缺陷归因于用于建立神经网络的训练数据的限制。实验室台架测试表明，在一定条件下，例如，如果两层模式大气的高层高度变化约300m(相当于子孔径约十分之一的偏移)，人工神经网络的性能可以超过L和A。
We present recent results from the initial testing of an artificial neural network (ANN)-based tomographic reconstructor Complex Atmospheric Reconstructor based on Machine lEarNing (CARMEN) on CANARY, an adaptive optics demonstrator operated on the 4.2 m William Herschel Telescope, La Palma. The reconstructor was compared with contemporaneous data using the Learn and Apply (L&A) tomographic reconstructor. We find that the fully optimized L&A tomographic reconstructor outperforms CARMEN by approximately 5 per cent in Strehl ratio or 15 nm rms in wavefront error. We also present results for CANARY in Ground Layer Adaptive Optics mode to show that the reconstructors are tomographic. The results are comparable and this small deficit is attributed to limitations in the training data used to build the ANN. Laboratory bench tests show that the ANN can outperform L&A under certain conditions, e.g. if the higher layer of a model two layer atmosphere was to change in altitude by similar to 300 m (equivalent to a shift of approximately one tenth of a subaperture).