Learning-based surface deformation recovery for large radio telescope antennas

The surface deformation of the main reflector in a large radio telescope is closely related to its working efficiency, which is important for some astronomical science studies. Here, we present a deep learning-based surface deformation recovery framework using non-interferometric intensity measurements as input. The recurrent convolutional neural network (RCNN) is developed to establish the inverse mapping relationship between the surface deformation of the main reflector and the intensity images at the aperture plane and at a near-field plane. Meanwhile, a physical forward propagation model is adopted to generate a large amount of data for pre-training in a computationally efficient manner. Then, the inverse mapping relationship is adjusted and improved by transfer learning using experimental data, which achieves a 15-fold reduction in the number of training image sets required, which is helpful to facilitate the practical application of deep learning in this field. In addition, the RCNN model can be trained as a denoiser, and it is robust to the axial positioning error of the measuring points. It is also promising to extend this method to the study of adaptive optics.