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The Mars Curiosity rover is frequently sending back engineering and sciencedata that goes through a pipeline of systems before reaching its finaldestination at the mission operations center making it prone to volume loss anddata corruption. A ground data system analysis (GDSA) team is charged with themonitoring of this flow of information and the detection of anomalies in thatdata in order to request a re-transmission when necessary. This work presents$\Delta$-MADS, a derivative-free optimization method applied for tuning thearchitecture and hyperparameters of a variational autoencoder trained to detectthe data with missing patches in order to assist the GDSA team in theirmission.

Tuning a variational autoencoder for data accountability problem in the Mars Science Laboratory ground data system