arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 2006.08294astro-ph.IM
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使用卷积模型的金字塔波前传感器光学增益补偿

Pyramid wavefront sensor optical gains compensation using a convolutional model

Vincent Chambouleyron, Olivier Fauvarque, Pierre Janin-Potiron, Carlos Correia, Jean-François Sauvage, Noah Schwartz, Benoît Neichel, Thierry Fusco

摘要:

极大望远镜绝大多数选择金字塔波前传感器(PyWFS)而不是更广泛使用的夏克-哈特曼波前传感器(SHWFS)来执行其单共轭自适应光学(SCAO)模式。PyWFS是一种基于傅立叶滤波的传感器,已被证明在许多天文学应用中非常成功。然而,它表现出非线性行为,导致其灵敏度降低时,与非零残留波前。这种所谓的光学增益(OG)效应降低了SCAO系统的闭环性能,并阻止了非共路像差(NCPA)的精确校正。在本文中,我们的目标是计算OG使用快速和敏捷的策略,以控制PyWFS测量在自适应光学闭环系统。使用基于卷积模型的PyFWS的新理论描述,我们能够分析预测PyWFS在闭环操作中的行为。该模型使我们能够探索残余波前误差对特定方面的影响,如灵敏度和相关的OG。所提出的方法依赖于剩余波前统计的知识,并能够自动估计当前OG。端到端的数值模拟被用来验证我们的预测和测试我们的方法的相关性。我们证明,使用非侵入性策略,我们的方法提供了一个准确的估计OG。该模型本身只需要AO遥测数据来获得大气湍流的统计信息。此外,我们表明,通过仅使用当前Fried参数r_0的估计和基本的系统级特性,可以估计OG的精度小于10%。最后,我们强调了在NCPA补偿的情况下OG估计的重要性。该方法被应用到PyWFS。

英文摘要:

Extremely Large Telescopes have overwhelmingly opted for the Pyramid wavefront sensor (PyWFS) over the more widely used Shack-Hartmann WaveFront Sensor (SHWFS) to perform their Single Conjugate Adaptive Optics (SCAO) mode. The PyWFS, a sensor based on Fourier filtering, has proven to be highly successful in many astronomy applications. However, it exhibits non-linearity behaviors that lead to a reduction of its sensitivity when working with non-zero residual wavefronts. This so-called Optical Gains (OG) effect, degrades the close loop performance of SCAO systems and prevents accurate correction of Non-Common Path Aberrations (NCPA). In this paper, we aim at computing the OG using a fast and agile strategy in order to control the PyWFS measurements in adaptive optics closed loop systems. Using a novel theoretical description of the PyFWS, which is based on a convolutional model, we are able to analytically predict the behavior of the PyWFS in closed-loop operation. This model enables us to explore the impact of residual wavefront error on particular aspects such as sensitivity and associated OG. The proposed method relies on the knowledge of the residual wavefront statistics and enables automatic estimation of the current OG. End-to-End numerical simulations are used to validate our predictions and test the relevance of our approach. We demonstrate, using on non-invasive strategy, that our method provides an accurate estimation of the OG. The model itself only requires AO telemetry data to derive statistical information on atmospheric turbulence. Furthermore, we show that by only using an estimation of the current Fried parameter r_0 and the basic system-level characteristics, OGs can be estimated with an accuracy of less than 10%. Finally, we highlight the importance of OG estimation in the case of NCPA compensation. The proposed method is applied to the PyWFS.

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