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Automated VLBI scheduling using AI-based parameter optimization
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DOI：10.1007/s00190-021-01512-w.png)
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Within this work, a new geodetic very long baseline interferometry (VLBI) scheduling approach inspired by evolutionary processes based on selection, crossover and mutation is presented. It mimics the biological concept surviving of the fittest to iteratively explore the scheduling parameter space looking for the best solution. Besides providing high-quality results, one main benefit of the proposed approach is that it enables the generation of fully automated and individually optimized schedules. Moreover, it generates schedules based on transparent rules, well-defined scientific goals and by making decisions based on Monte Carlo simulations. The improvements in terms of precision of geodetic parameters are discussed for various observing programs organized by the International VLBI Service for Geodesy and Astrometry (IVS), such as the OHG, R1, and T2 programs. In the case of schedules with a difficult telescope network, an improvement in the precision of the geodetic parameters up to 15% could be identified, as well as an increase in the number of observations of up to 10% compared to classical scheduling approaches. Due to the high quality of the produced schedules and the reduced workload for the schedulers, various IVS observing programs are already making use of the evolutionary parameter selection, such as the AUA, INT2, INT3, INT9, OHG, T2 and VGOS-B program.
Keyword:
VLBI
IVS
Scheduling
Evolutionary algorithm
Evolutionary strategy
AI
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