Browsing by Author "Bernardi, Rafael L."

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    Quasi-random frequency sampling for optical turbulence simulations
    (2024) Berdja, Amokrane; Hadjara, Massinissa; Carbillet, Marcel; Bernardi, Rafael L.; Petrov, Romain G.
    Optical turbulence modeling and simulation are crucial for developing astronomical ground-based instruments, laser communication, laser metrology, or any application where light propagates through a turbulent medium. In the context of spectrum-based optical turbulence Monte-Carlo simulations, we present an alternative approach to the methods based on the fast Fourier transform (FFT) using a quasi-random frequency sampling heuristic. This approach provides complete control over the spectral information expressed in the simulated measurable without the drawbacks encountered with FFT-based methods such as high-frequency aliasing, low- frequency under-sampling, and static sampling statistics. The method's heuristics, implementation, and an application example from the study of differential piston fluctuations are discussed.
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    Restoration of images with a spatially varying PSF of the T80-S telescope optical model using neural networks
    (2022) Bernardi, Rafael L.; Berdja, Amokrane; Dani Guzman, Christian; Torres-Torriti, Miguel; Roth, Martin M.
    Most image restoration methods in astronomy rely upon probabilistic tools that infer the best solution for a deconvolution problem. They achieve good performances when the point spread function (PSF) is spatially invariant in the image plane. However, this condition is not always satisfied in real optical systems. We propose a new method for the restoration of images affected by static and anisotropic aberrations using Deep Neural Networks that can be directly applied to sky images. The network is trained using simulated sky images corresponding to the T80-S Telescope optical model, a 80-cm survey imager at Cerro Tololo (Chile), which are synthesized using a Zernike polynomial representation of the optical system. Once trained, the network can be used directly on sky images, outputting a corrected version of the image that has a constant and known PSF across its field of view. The method is to be tested on the T80-S Telescope. We present the method and results on synthetic data.
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    Restoration of T80-S telescope's images using neural networks
    (2023) Bernardi, Rafael L.; Berdja, Amokrane; Guzman, Christian Dani; Torres-Torriti, Miguel; Roth, Martin M.
    Convolutional neural networks (CNNs) have been used for a wide range of applications in astronomy, including for the restoration of degraded images using a spatially invariant point spread function (PSF) across the field of view. Most existing development techniques use a single PSF in the deconvolution process, which is unrealistic when spatially variable PSFs are present in real observation conditions. Such conditions are simulated in this work to yield more realistic data samples. We propose a method that uses a simulated spatially variable PSF for the T80-South (T80-S) telescope, an 80-cm survey imager at Cerro Tololo (Chile). The synthetic data use real parameters from the detector noise and atmospheric seeing to recreate the T80-S observational conditions for the CNN training. The method is tested on real astronomical data from the T80-S telescope. We present the simulation and training methods, the results from real T80-S image CNN prediction, and a comparison with space observatory Gaia. A CNN can fix optical aberrations, which include image distortion, PSF size and profile, and the field position variation while preserving the source's flux. The proposed restoration approach can be applied to other optical systems and to post-process adaptive optics static residual aberrations in large-diameter telescopes.