4 papers
Traceable Spectral Inference via Influence Functions: Efficient Data Attribution and Error Proxies for the Ariel Mission
Nikki Grens, Luís F. Simões, Kai Hou Yip +1
Interpretability is critical for machine learning models deployed in scientific space missions such as ESA's Ariel, where ground truth is unavailable during operations and physical…
On the synergetic use of Ariel and JWST for exoplanet atmospheric science
Quentin Changeat, Pierre-Olivier Lagage, Giovanna Tinetti +27
This paper explores the potential for strategic synergies between the JWST and the Ariel telescopes, two flagship observatories poised to revolutionise the study of exoplanet atmos…
Operational range bounding of spectroscopy models with anomaly detection
Luís F. Simões, Pierluigi Casale, Marília Felismino +4
Safe operation of machine learning models requires architectures that explicitly delimit their operational ranges. We evaluate the ability of anomaly detection algorithms to provid…
Towards atmospheric retrievals of panchromatic light-curves: ExPLOR-ing generalized inversion techniques for transiting exoplanets with JWST and Ariel
Quentin Changeat, Yuichi Ito, Ahmed F. Al-Refaie +2
Conventional atmospheric retrieval codes are designed to extract information, such as chemical abundances, thermal structures and cloud properties, from fully "reduced" spectra obt…