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Title: Simulation and Analysis of Turbulence Profiling Neural Networks for Tomographic Layer Reconstruction and Wide Field Image Correction in Telescopes
Abstract:
Light that propagates through the atmosphere is subject to phase perturbations at layers of turbulent flow. For decades, traditional adaptive optics (AO) has used a deformable mirror (DM) to correct the phase at the system pupil. Since the correction is applied at the pupil – not at the layers of turbulence – traditional AO is only valid over a field of view of a few arcseconds for visible light. To obtain wide field image correction, the phase has to be compensated for at optical conjugates to the layers themselves. Doing so with traditional AO hardware increases system cost and complexity because multiple DMs are required. This has motivated the exploration of a software-based image correction technique called multi-object image correction (MOIC). As the cost of computational power continues to improve, MOIC has the potential to become a viable option for wide field turbulence compensation. Thus, the development of simulations and algorithms at the current moment will enable software-based MOIC for a range of applications in the future.
In this work, a simulation of a MOIC system is built. The simulation enables exploration of machine learning based turbulence profiling and offline tomographic layer reconstruction to further wide field turbulence compensation without a DM. After providing background on turbulence induced phase aberrations and how they are measured with a wavefront sensor, computationally efficient methods for modeling dynamic layers of turbulent phase are developed and the signal-to-noise ratio (SNR) of a layer of turbulence in a multi-layer atmosphere is presented. The turbulence simulation and SNR are then used to generate a large data set for training layer finding neural networks. We show that neural networks are capable of locating layers of turbulence with a high degree of accuracy, even for layers with an SNR less than 1. The developed atmosphere profiling and layer reconstruction pipeline is then tested using simulated wavefront sensor measurements from example atmospheres. Methods for comparing the real atmosphere to the reconstruction are developed and the test cases are analyzed to identify primary sources of reconstruction error. Deblurring of the system image from reconstructed layers is then demonstrated and options for future work are presented.
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https://arizona.zoom.us/j/81762545374
Password: turbulence