As the Extremely Large Telescope (ELT) nears operational status, the focus is made on maximizing its capabilities to produce the best possible images. Among other influential factors, the differential pistons – caused by the joint presence of spiders breaking the spatial continuity of the wavefront and low-order aberrations from various origins – significantly impact the overall image quality and must therefore be addressed. Our study is centered on utilizing Neural Networks (NN) to accurately estimate the ELT’s six differential pistons in the presence of turbulence residuals using a 2x2 Shack-Hartmann wavefront sensor (SHWFS). The results of this work could be applied to instruments like HARMONI or MORFEO, which will be equipped with 2x2 SHWFS for their natural guide star wavefront sensing. In our earlier work, the use of ResNet networks fed with data covering all pupil/SHWFS rotation angles demonstrated success in piston estimation. In that study, a simplified atmospheric turbulence model was employed, consisting solely of turbulence residuals, implying perfect correction for frequencies below the deformable mirror’s cutoff frequency. In this paper, we propose to evaluate the network’s performance under more realistic atmospheric turbulence conditions relevant to the ELT. We show that the network can still extract differential piston information from single-frame images, while exhibiting increased uncertainty in its estimation. However, using 10-frame averaged images leads to a significant improvement in root mean square error (RMSE) performance. We conclude this work by addressing remaining open questions and outlining potential future research directions. These findings contribute to refining the NN models usage for cophasing applications, addressing alignment challenges, and enhancing ELT instrumentation performance.
Achieving both high angular resolution and frequent revisit times for Earth (or planet) observation from low Earth orbit poses numerous challenges. The trade-off between increasing aperture size and the associated costs necessitates a novel approach. AZIMOV is a payload prototype project of a 6U CubeSat segmented deployable telescope with an aperture diameter of 30 cm currently in design phase. The large primary mirror enables a 1 m ground sampling distance in the visible. Optimal telescope performance requires precise phasing of the primary mirror, but Cubesat limitations (volume, power, computing) preclude conventional dedicated wavefront sensing methods. Only focal plane sensing appears feasible on small platforms. However, classical methods are iterative and computationally heavy due to the non-linearity between phase and image intensity. In this paper, we investigate deep learning for correcting piston, tip, and tilt aberrations across the primary mirror's four segments from a single focal plane image. We demonstrate diffraction-limited performance on a point source. This method, based on Convolutional Neural Network (CNN), is robust to noise and higher order aberrations, and outperforms classical iterative methods in terms of speed, accuracy and robustness. Finally, when imaging an unknown extended object on Earth’s surface, we demonstrate that our methods can consistently meet diffraction limited performance.
DEEPLOOP is a Python toolbox, originally dedicated to the estimation of the parameters of an Adaptive Optics (AO) Point Spread Function (PSF), describing the atmospheric turbulence and the static modes of a telescope. This toolbox is using the Tensorflow/Keras deep learning API and a Graphical Processor Unit (GPU) computing framework. DEEPLOOP is based on a small set of Python scripts dedicated to the data loading, to the Neural Network (NN) models architectures and their compiling, to the training methods, to the learning curves display and to the performances evaluation on the test sets. This toolbox has a great flexibility: it enables to make simulations on a specific parameters grid (for searching the best hyperparameters configuration), to parallelize the calculations on several GPUs (synchronous data parallelism on the same node), and to use some specific ’on-the-fly’ images loading for each batch, in order to use very few Random Access Memory (RAM). In this paper, we will first explain the main characteristics of this toolbox. Then, the first results with data simulations on Keck II telescope will be presented.
Today, the combination of high angular resolution and high revisit rate is not readily available from space, at least not at a reasonable cost. Many applications in the science, civil or defense domains would benefit from having access to detailed images of the ground as often as possible, in order to study temporal evolutions of specific events. The high angular resolution requires large optics hence large platforms, whereas the revisit rate requires constellations of multiple satellites and therefore small and affordable platforms. We proposed the concept of a deployable telescope onboard a CubeSat, called AZIMOV [1, 3, 5], to address this specific gap. Reaching a diameter of 30 cm once deployed, this concept gives access to a meter resolution on the ground from a Low Earth Orbit, or to a 70 cm resolution on Mars surface from a 400 km polar orbit. We study in this paper the performance of such a telescope in the aggressive thermal environment of space, with respect to the tight optical requirements of the system.
For space-based Earth Observations and solar system observations, obtaining both high revisit rates (using a constellation of small platforms) and high angular resolution (using large optics and therefore a large platform) is an asset for many applications. Unfortunately, they prevent the occurrence of each other. A deployable satellite concept has been suggested that could grant both assets by producing jointly high revisit rates and high angular resolution of roughly 1 meter on the ground. This concept relies however on the capacity to maintain the phasing of the segments at a sufficient precision (a few tens of nanometers at visible wavelengths), while undergoing strong and dynamic thermal gradients. In the constrained volume environment of a CubeSat, the system must reuse the scientific images to measure the phasing errors. We address in this paper the key issue of focal-plane wave-front sensing for a segmented pupil using a single image with deep learning. We show a first demonstration of measurement on a point source. The neural network is able to identify properly the phase piston-tip-tilt coefficients below the limit of 15nm per petal.
Available volumes of nanosats such as CubeSats impose physical limits to the telescope diameter, limiting achievable spatial resolution and photometric capability. For example, a 12U CubeSat typically only has sufficient volume to host a 20 cm diameter monolithic telescope. In this paper, we present recent advances in deployable optics to host a 30 cm+ diameter telescope in a 6U CubeSat, with a volume of 4U dedicated to the payload and 2U to the satellite bus. To reach this high level of compactness, we fold the primary and secondary mirrors for launch, which are then unfolded and aligned in space. Diffraction-limited imaging quality in the visible part of the spectrum is achieved by controlling each mirror segment in piston, tip, and tilt. In this paper, we first describe overall satellite concept, we then report on the optomechanical design of the payload to deploy and adjust the mirrors. Finally, we discuss the automatic phasing of the primary to control the final optical quality of the telescope.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.