4 research outputs found
Recommended from our members
Predictive control for adaptive optics using neural networks
Adaptive optics (AO) has become an indispensable tool for ground-based telescopes to mitigate atmospheric seeing and obtain high angular resolution observations. Predictive control aims to overcome latency in AO systems: the inevitable time delay between wavefront measurement and correction. A current method of predictive control uses the empirical orthogonal functions (EOFs) framework borrowed from weather prediction, but the advent of modern machine learning and the rise of neural networks (NNs) offer scope for further improvement. Here, we evaluate the potential application of NNs to predictive control and highlight the advantages that they offer. We first show their superior regularization over the standard truncation regularization used by the linear EOF method with on-sky data before demonstrating the NNs' capacity to model nonlinearities on simulated data. This is highly relevant to the operation of pyramid wavefront sensors (PyWFSs), as the handling of nonlinearities would enable a PyWFS to be used with low modulation and deliver extremely sensitive wavefront measurements. © 2021 Society of Photo-Optical Instrumentation Engineers (SPIE).Immediate accessThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]
Recommended from our members
Nonlinear Wave Front Reconstruction from a Pyramid Sensor using Neural Networks
The pyramid wave front sensor (PyWFS) has become increasingly popular to use in adaptive optics (AO) systems due to its high sensitivity. The main drawback of the PyWFS is that it is inherently nonlinear, which means that classic linear wave front reconstruction techniques face a significant reduction in performance at high wave front errors, particularly when the pyramid is unmodulated. In this paper, we consider the potential use of neural networks (NNs) to replace the widely used matrix vector multiplication (MVM) control. We aim to test the hypothesis that the NN's ability to model nonlinearities will give it a distinct advantage over MVM control. We compare the performance of a MVM linear reconstructor against a dense NN, using daytime data acquired on the Subaru Coronagraphic Extreme Adaptive Optics system (SCExAO) instrument. In a first set of experiments, we produce wavefronts generated from 14 Zernike modes and the PyWFS responses at different modulation radii (25, 50, 75, and 100 mas). We find that the NN allows for a far more precise wave front reconstruction at all modulations, with differences in performance increasing in the regime where the PyWFS nonlinearity becomes significant. In a second set of experiments, we generate a data set of atmosphere-like wavefronts, and confirm that the NN outperforms the linear reconstructor. The SCExAO real-time computer software is used as baseline for the latter. These results suggest that NNs are well positioned to improve upon linear reconstructors and stand to bring about a leap forward in AO performance in the near future. © 2023. The Author(s). Published by IOP Publishing Ltd on behalf of the Astronomical Society of the Pacific (ASP). All rights reserved.Open access articleThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]
Unraveling Disks in AGB Stars
It is commonly accepted that asymmetries found in the post-AGB stars and planetary nebulae should originate as early as during the AGB phase. We present results from our high-angular resolution observing programs, with an aperture masking technique on the VLT, of a sample of evolved stars that were known to present asymmetries at larger spatial scales (e.g. jets, torii and/or bipolar nebulae). Disk-like structures have been found in the vicinity of at least two of these stars