6 papers
aRieL - Reinforcement Learning for the Ariel Space Telescope Scheduling
James Kostas Ray, Kai Hou Yip, Alexandra Thompson +2
The Ariel mission will conduct a population level survey of hundreds of exoplanet atmospheres, requiring observations to be scheduled across a large and diverse catalogue of potent…
Operational Range Bounding in Spectroscopy: A Safety Cage Framework for Machine Learning Models
Nikki Grens, Luís F. Simões, Kai Hou Yip +1
Ensuring the reliability of black-box machine learning models in safety-critical space missions remains a significant challenge, particularly when ground-truth is unavailable for v…
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…
Adaptive Online Emulation for Accelerating Complex Physical Simulations
Tara P. A. Tahseen, Nikolaos Nikolaou, Luís F. Simões +3
Complex physical simulations often require trade-offs between model fidelity and computational feasibility. We introduce Adaptive Online Emulation (AOE), which dynamically learns n…
Extreme Learning Machines for Exoplanet Simulations: A Faster, Lightweight Alternative to Deep Learning
Tara P. A. Tahseen, Luís F. Simões, Kai Hou Yip +3
Increasing resolution and coverage of astrophysical and climate data necessitates increasingly sophisticated models, often pushing the limits of computational feasibility. While em…
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…