Recent societal demands in climate awareness call for rapid launch of space optical spectrographs, such as to be capable of putting state-of-the-art technology in short timeframe into orbit. As a consequence, it is of paramount importance to compress instruments’ construction schedules down to the ultimately necessary need. Because calibration and characterization (C&C) partially takes place after full instrument assembly, it is de facto on the time-plan critical path, bearing antagonist requirements: measurement accuracy shall be guaranteed without jeopardizing the instrument delivery date. To solve this problem, Airbus has explored multiple paths in order to propose an instrument's "Design for Calibration": the method consists in integrating C&C at the very beginning of the instrument development in order to respond efficiently to the identified needs. First, all planned tests are exhaustively simulated and analyzed with tools validated before measurements, ensuring full control of the overall C&C throughout the entire lifecycle of the project. Next, Airbus strongly enforces its strategy of measuring relevant parameters as soon as they are accessible, hence providing early characterization out of the critical path. Then, the remaining parameters have been thoroughly analyzed to provide a lean optical ground support equipment (OGSE) architecture capable of responding to current challenges. Moreover, it enables full automation, enforcing its time-efficiency by minimizing overheads. Although rapidity is ensured, measurement accuracies are simultaneously kept compliant. Finally, this work presents also disruptive photonics hardware investigated by Airbus to provide calibration for relaxing design: optically filtered supercontinua and optical microcombs.
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.
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