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.
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