ML Applications within the Ariel Mission

This presentation surveys machine learning applications in the Ariel mission pipeline, covering data calibration, signal extraction, atmospheric retrieval, and anomaly detection. We examine operational implementations and methods under development. We discuss mission-specific constraints shaping algorithm design: computational limits, interpretability requirements, and robustness to instrumental variations. Finally, we identify open problems where community contributions can directly advance mission capabilities