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<p>The Gusev crater, landing site of the MER-A mission, and the Jezero crater, site of the Mars2020 mission, currently located near the Martian equator. They may have been two fluvial-lacustrine systems from the planet's wet past, Nevertheless, cortical fractures, ridges and basaltic flows are present in the bottom of both craters. These features are well preserved and not affected by large craters, which seems to indicate that could be young and contemporary forms. Mapping of both Gusev Crater and Jezero Crater has been carried out by remote sensing onboard the Mars Reconnaissance Orbiter (MRO), of particular interest for Gusev Crater is the Context Camera (CTX)-based high-detail mapping, which improves the resolution of previous studies, and the High-Resolution Imaging Experiment (HiRISE). These are complemented by data from the Thermal Emission Imaging System (THEMIS) and Mars Orbiter Laser Altimeter (MOLA), the Mars Global Surveyor (MGS) mission. CTX and HiRISE are visible images that provide information about the surface features of morphological units in detail. The MOLA data have made it possible to determine the stratigraphic position of the mapped units and to obtain information on the slopes and elevations of the units, as well as to estimate the fill of both craters. The combination and analysis of these data show possible evidence of geological activity on the surface of these craters in more recent periods of Mars' past (millions of years). Crater counts (crater frequency) have been used to determine a possible age for the ridges described in crater Gusev. These indications may be associated with volcanic activity and horizontal “strike-slip” movements affecting the ridges observed in Gusev crater, as well as crustal fracture and the presence of basaltic plains in Jezero crater.</p>

The main objective of an Adaptive Optics (AO) system is to correct the aberrations produced in the received wavefronts, caused by atmospheric turbulence. From some measures taken by ground-based telescopes, AO systems must reconstruct all the turbulence traversed by the incoming light and calculate a correction. The turbulence is characterized as a phenomenon that can be modeled as several independent, random, and constantly changing layers. In the case of Solar Single-Conjugated Adaptive Optics (Solar SCAO), the key is to reconstruct the turbulence on-axis with the direction of the observation. Previous research has shown that ANNs are a possible alternative when they have been trained in the Sun’s regions where they must make the reconstructions. Along this research, a new solution based on Artificial Intelligence (AI) is proposed to predict the atmospheric turbulence from the data obtained by the telescope sensors that can generalize recovering wavefronts in regions of the sun completely unknown previously. The presented results show the quality of the reconstructions made by this new technique based on Artificial Neural Networks (ANNs), specifically the Multi-layer Perceptron (MLP).

Information on the correlations from solar Shack–Hartmann wavefront sensors is usually used for reconstruction algorithms. However, modern applications of artificial neural networks as adaptive optics reconstruction algorithms allow the use of the full image as an input to the system intended for estimating a correction, avoiding approximations and a loss of information, and obtaining numerical values of those correlations. Although studied for night-time adaptive optics, the solar scenario implies more complexity due to the resolution of the solar images potentially taken. Fully convolutional neural networks were the technique chosen in this research to address this problem. In this work, wavefront phase recovery for adaptive optics correction is addressed, comparing networks that use images from the sensor or images from the correlations as inputs. As a result, this research shows improvements in performance for phase recovery with the image-to-phase approach. For recovering the turbulence of high-altitude layers, up to 93% similarity is reached.