The Mars Curiosity rover is frequently sending back engineering and science data that goes through a pipeline of systems before reaching its final destination at the mission operations center making it prone to volume loss and data corruption. A ground data system analysis (GDSA) team is charged with the monitoring of this flow of information and the detection of anomalies in that data in order to request a re-transmission when necessary. This work presents Δ-MADS, a derivative-free optimization method applied for tuning the architecture and hyperparameters of a variational autoencoder trained to detect the data with missing patches in order to assist the GDSA team in their mission.
@article{arxiv.2006.03962,
title = {Tuning a variational autoencoder for data accountability problem in the Mars Science Laboratory ground data system},
author = {Dounia Lakhmiri and Ryan Alimo and Sebastien Le Digabel},
journal= {arXiv preprint arXiv:2006.03962},
year = {2020}
}