The Level

No abstract

Driven by the need of always more accurate models, space optics instrument-based observations push constantly towards high accuracy measurements that require an excellent knowledge of the instrument. To achieve this, current classical technologies are limited by the complexity of current instruments, calling for disruptive technologies to take over. Therefore, Airbus is currently integrating Artificial Intelligence (AI), responding to the call for new concepts. Here Airbus takes benefit of deep learning to detect complex patterns that would otherwise be impossible to properly characterize classically, opening the door for completely novel characterization paradigms and enabling manifold accuracy improvements. This work first focuses on obtained results on the detection of random telegraph signals (RTS) of CCD detectors under tests. By training a convolutional neural network (CNN) with RTS data, it has been possible to setup an algorithm achieving 20x faster data processing while increasing accuracy, providing unprecedented fast and performant RTS characterization. In another domain, multi-reflection-induced ghost stray light have been also characterized using CNN. Here, Airbus uses simulated data from optical software to generate 2D ghost maps used to train an algorithm capable of segmenting individual patterns. We show in this work that the appropriate architecture with optimized hyper-parameters achieves 97% accuracy. These ground-breaking results pave the way for a complete characterization of optical instrument ghosts that were so far neglected because of their complexity. It hence enables in the future more performant straylight correction algorithms as well as providing extended freedom in the design of space optical instruments.

No abstract

The METimage instrument is an Airbus-primed passive superspectral imaging radiometer measuring thermal radiance emitted by the Earth and solar backscattered radiation in 20 spectral channels from 0.44μm to 13.35μm. The instrument achieves the entire Earth coverage at a low-Earth polar-orbit on daily basis by constantly scanning with spatial resolution of 500m at nadir and constant spatial sampling angle (SSA) across a 2670km swath. In the thermal domain, the most crucial part of the system is the pair of cryogenically-cooled HgCdTe (MCT) photodetectors intended for thermaldomain SMWIR and LVWIR, the development and manufacturing of which was contracted to LYNRED, who have successfully delivered the flight models to Airbus. In this work, the results of the Airbus-hosted flight detection-chain level test campaign in nominal vacuum and cryogenic environment are presented. Their system impacts are evaluated to conclude the expected full flight-worthiness of both delivered photodetectors into the METimage optical payload. First, both dark currents and offsets are characterized: both parameter noise levels quantitatively outperform their predicted values, minimizing their impact on the system signal-to-noise ratio (SNR). Then the response linearity of both photodetectors over the required thermal radiance ranges has been measured and has shown better performances than predicted by design over the setup-accessible solar and 185K to 280K thermal radiance ranges. Finally, the random telegraph signal (RTS) signature of both photodetectors is thoroughly characterized based on a novel sharp-edge detection method in conditions mimicking in-orbit operation: after periodic cooldowns and at stable nominal temperatures. Based on these measurements, the sub-pixel selection map (SPSM) is generated, enabling to deselect image elements that are deemed to degrade overall performance from a system perspective. Thanks to a demanding flight model selection procedure applied by LYNRED, the RTS test results of the sorted out devices are significantly better than expected, hence ensuring compliance for system SNR and homogeneity. Full flight worthiness is therefore confirmed and final integration of both photodetectors in the first METimage flight instrument optical system is currently ongoing.