论文标题
带有深神经网络和校准数据的宽场小光圈望远镜的点传播功能估计
Point Spread Function Estimation for Wide Field Small Aperture Telescopes with Deep Neural Networks and Calibration Data
论文作者
论文摘要
点扩散函数（PSF）反映了望远镜的状态，并在数据处理方法的开发中起着重要作用，例如基于PSF的天文学，光度法和图像恢复。但是，对于宽场小孔径望远镜（WFSAT），估算整个视野的任何位置的PSF都是很难的，因为光学系统引起的畸变非常复杂，并且星图像的噪声比通常太低，对于PSF估计。在本文中，我们进一步开发了基于深层神经网络（DNN）的PSF建模方法，并在PSF估计中显示了其应用。在望远镜对准和测试阶段，我们的方法通过修改工程公差（倾斜和分离）中的光学元素来收集系统校准数据。然后，我们使用这些数据训练DNN（TEL-NET）。训练后，TEL-NET可以通过几个离散采样的星星图像在任何视野中估算PSF。我们使用模拟和实验数据来测试方法的性能。结果表明，TEL-NET可以成功地重建任何状态的WFSAT的PSF，并处于FOV的任何位置。它的结果比比较经典方法 - 反距离重量（IDW）插值获得的结果要精确得多。我们的方法为WFSAT开发基于深神网络的数据处理方法提供了基础，这些数据处理需要PSF的强大事先信息。
The point spread function (PSF) reflects states of a telescope and plays an important role in development of data processing methods, such as PSF based astrometry, photometry and image restoration. However, for wide field small aperture telescopes (WFSATs), estimating PSF in any position of the whole field of view is hard, because aberrations induced by the optical system are quite complex and the signal to noise ratio of star images is often too low for PSF estimation. In this paper, we further develop our deep neural network (DNN) based PSF modelling method and show its applications in PSF estimation. During the telescope alignment and testing stage, our method collects system calibration data through modification of optical elements within engineering tolerances (tilting and decentering). Then we use these data to train a DNN (Tel--Net). After training, the Tel--Net can estimate PSF in any field of view from several discretely sampled star images. We use both simulated and experimental data to test performance of our method. The results show that the Tel--Net can successfully reconstruct PSFs of WFSATs of any states and in any positions of the FoV. Its results are significantly more precise than results obtained by the compared classic method - Inverse Distance Weight (IDW) interpolation. Our method provides foundations for developing of deep neural network based data processing methods for WFSATs, which require strong prior information of PSFs.
