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基于混合人工神经网络的分段望远镜的活塞误差测量.

Dan Yue¹, Pengcheng Song¹, Chongshuai Wang¹

¹College of Physics, Changchun University of Science and Technology, Changchun 130022, China.

Sensors (Basel, Switzerland)

|October 28, 2023

概括

这项研究引入了一种新的混合人工神经网络,用于细分望远镜中精确检测活塞误差. 该方法通过使用焦平面图像实现了高精度 (10纳米) 和广泛的检测范围.

科学领域:

光学工程是指光学工程.
天文学仪器仪器仪表
在光学领域的人工智能.

背景情况:

分段望远镜需要精确对准子镜,以避免活塞错误.
现有的活塞错误检测方法往往复杂且难以实施.
精确测量活塞误差对于望远镜的最佳性能至关重要.

研究的目的:

开发一种新的,准确和广泛的方法来测量细分望远镜中的活塞误差.
为了利用人工智能,特别是混合神经网络,改善活塞错误检测.
为了减少与活塞误差测量相关的复杂性和硬件成本.

主要方法:

建议建立一个混合人工神经网络,将Resnet和BP网络结合起来.
Resnet学习了焦平面图像和活塞误差指示器之间的关系.
BP网络学习了调制转移函数 (MTF) 和活塞误差大小之间的关系.

主要成果:

混合网络只使用点源的焦平面图像来准确检测活塞错误.
实现了10nm的高检测精度.
展示了广泛的检测范围,覆盖了宽带照明的整个连贯长度.

关键词:

人工神经网络的人工神经网络活塞错误活塞错误活塞错误细分望远镜的细分望远镜

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结论:

拟议的混合神经网络方法为活塞误差测量提供了强大的解决方案.
该方法提供了高精度,广泛的检测范围,并且具有成本效益.
这种方法简化了活塞误差检测,有利于细分望远镜校准和性能.