Laboratory and telescope demonstration of the TP3-WFS for the adaptive optics segment of AOLI

Colodro-Conde, Carlos; Velasco, Sergio; Fernández-Valdivia, J. J.; López, Roberto López; Oscoz, A.; Rebolo, R.; Femenia, Bruno; King, David L.; Labadie, Lucas; Mackay, Craig D.; Muthusubramanian, Balaji; Garrido, Antonio Pérez; Puga, Marta; Rodríguez-Coira, G.; Rodríguez-Ramos, L. F.; Rodríguez-Ramos, José Manuel; Toledo-Moreo, R.; Villó-Pérez, Isidro

doi:10.1093/mnras/stx262

Cited by 15 publications

(12 citation statements)

References 31 publications

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“…For the GWFS, Colodro-Conde et al argued that the number of Zernike modes should be related to the number of Radon angles used to measure the wavefront. 14 While we agree with the argument provided by Colodro-Conde et al, we further hypothesize that the number of Radon angles needed to estimate specific Zernike modes in a wavefront is related to the radial order of said Zernike mode. We estimate that the high frequency radial symmetry seen in the different Zernike radial orders may not be detectable until the number of Radon angles is comparable to the frequency of the radial symmetry.…”

Section: Properties Investigatedsupporting

confidence: 92%

“…Due to the relatively new interest in the GWFS for astronomical AO and DWFS among the scientific community [11][12][13][14] and the authors' investment into a new imaging instrument for MJUO, 15 it is important to establish guidelines for various parameters within the GWFS to optimize its performance. Parameters investigated in this paper are: the number of Zernike modes used to construct the synthetic interaction matrix and to estimate the wavefront, the virtual propagation distance used to create the synthetic interaction matrix, the number of Radon angles used to measure the wavefront, and the sensitivity of the WFS under various signal-to-noise ratios (SNR).…”

Section: Properties Investigatedmentioning

confidence: 99%

“…10 However, it has only recently received significant interest. [11][12][13][14] Pal et al 11 first used the GWFS on-sky in open-loop DWFS in 2016, while Colodro-Conde et al 14 were the first to use the GWFS (which they call Two Pupil Plane Positions Wavefront Sensor or TP3-WFS) in closed-loop AO in 2017. While similar to the CWFS, initial investigations have shown the GWFS to have superior performance over the CWFS.…”

Section: Introductionmentioning

confidence: 99%

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Test bench demonstration of the geometric wavefront sensor

Hickman

Weddell

Clare

2020

Adaptive Optics Systems VII

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This paper investigates the performance and optimization of the Geometric Wavefront Sensor (GWFS) in openloop wavefront sensing, along with the Curvature Wavefront Sensor (CWFS) and Shack-Hartmann Wavefront Sensor (SH-WFS). The GWFS uses a ray tracing process to calculate the displacement of intensity fluctuations from two defocused point source images. While similar to the CWFS, initial simulations and testing have shown the GWFS to have superior performance compared to the CWFS. Various parameters within the GWFS -such as the signal-to-noise ratio (SNR) sensitivity, the number of Radon angles, the virtual propagation distance, and the number of reconstruction modes -are explored on a laboratory test bench. We found that the GWFS wavefront estimate error experiences an inverse relationship to the SNR, a minimum of 5 Radon angles is required to accurately estimate the single Zernike mode wavefronts (Z 4 -Z 15 ), the virtual propagation distance is confined by ray crossing and Fresnel diffraction effects, and the number of reconstruction Zernike modes is limited by noise amplification and over-fitting. This paper demonstrates the capabilities of the GWFS, illustrates the resulting wavefront estimates, and confirms the superior performance of the GWFS compared to the CWFS. The optimized GWFS will be utilized at Mt. John University Observatory (MJUO) in New Zealand for satellite and space debris imaging and tracking.

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Section: Properties Investigatedsupporting

confidence: 92%

Section: Properties Investigatedmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Test bench demonstration of the geometric wavefront sensor

Hickman

Weddell

Clare

2020

Adaptive Optics Systems VII

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“…As an alternative to classical SH sensors, the Tomographic Pupil Image Wavefront Sensor (TPI-WFS) was developed, as a modified curvature sensor [2]. This new sensor has proven successful at measuring the turbulence of the atmosphere, presenting some advantages such as, for example, better quality than a SH sensor when considering low light illumination regime; it is also more stable when changes in the optical parameters are introduced [3,4]. Nowadays, the amounts of data generated in the majority of the science branches lead to a series of techniques based on processing these amounts of data to extract features or adjust models automatically to the information contained within the data [5][6][7].…”

Section: Introductionmentioning

confidence: 99%

Defocused Image Deep Learning Designed for Wavefront Reconstruction in Tomographic Pupil Image Sensors

et al. 2020

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Mathematical modelling methods have several limitations when addressing complex physics whose calculations require considerable amount of time. This is the case of adaptive optics, a series of techniques used to process and improve the resolution of astronomical images acquired from ground-based telescopes due to the aberrations introduced by the atmosphere. Usually, with adaptive optics the wavefront is measured with sensors and then reconstructed and corrected by means of a deformable mirror. An improvement in the reconstruction of the wavefront is presented in this work, using convolutional neural networks (CNN) for data obtained from the Tomographic Pupil Image Wavefront Sensor (TPI-WFS). The TPI-WFS is a modified curvature sensor, designed for measuring atmospheric turbulences with defocused wavefront images. CNNs are well-known techniques for its capacity to model and predict complex systems. The results obtained from the presented reconstructor, named Convolutional Neural Networks in Defocused Pupil Images (CRONOS), are compared with the results of Wave-Front Reconstruction (WFR) software, initially developed for the TPI-WFS measurements, based on the least-squares fit. The performance of both reconstruction techniques is tested for 153 Zernike modes and with simulated noise. In general, CRONOS showed better performance than the reconstruction from WFR in most of the turbulent profiles, with significant improvements found for the most turbulent profiles; overall, obtaining around 7% of improvements in wavefront restoration, and 18% of improvements in Strehl.

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“…As an example, Colodro-Conde et al (2017) or Mackay et al (2012) presented the fusion of LI and adaptive optics (AOLI). In Schödel and Girard (2012), the deconvolution of a series of short exposures was shown as another possible way to enhance the quality of astronomical images.…”

Section: Introductionmentioning

confidence: 99%

Review of Image Quality Measures for Solar Imaging

2017

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Observations of the solar photosphere from the ground encounter significant problems caused by Earth's turbulent atmosphere. Before image reconstruction techniques can be applied, the frames obtained in the most favorable atmospheric conditions (the socalled lucky frames) have to be carefully selected. However, estimating the quality of images containing complex photospheric structures is not a trivial task, and the standard routines applied in nighttime lucky imaging observations are not applicable. In this paper we evaluate 36 methods dedicated to the assessment of image quality, which were presented in the literature over the past 40 years. We compare their effectiveness on simulated solar observations of both active regions and granulation patches, using reference data obtained by the Solar Optical Telescope on the Hinode satellite. To create images that are affected by a known degree of atmospheric degradation, we employed the random wave vector method, which faithfully models all the seeing characteristics. The results provide useful information about the method performances, depending on the average seeing conditions expressed by the ratio of the telescope's aperture to the Fried parameter, D/r 0 . The comparison identifies three methods for consideration by observers: Helmli and Scherer's mean, the median filter gradient similarity, and the discrete cosine transform energy ratio. While the first method requires less computational effort and can be used effectively in virtually any atmospheric conditions, the second method shows its superiority at good seeing (D/r 0 < 4). The third method should mainly be considered for the post-processing of strongly blurred images.

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Laboratory and telescope demonstration of the TP3-WFS for the adaptive optics segment of AOLI

Cited by 15 publications

References 31 publications

Test bench demonstration of the geometric wavefront sensor

Test bench demonstration of the geometric wavefront sensor

Defocused Image Deep Learning Designed for Wavefront Reconstruction in Tomographic Pupil Image Sensors

Review of Image Quality Measures for Solar Imaging

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