95 citations · 697 across the 32 of their papers we have counts for
35 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…
Graph neural networks for exoplanet atmospheres
Antonia Vojtekova, Kai Hou Yip, Ingo P. Waldmann +4
Calculating disequilibrium chemistry in exoplanet atmospheres remains a significant computational bottleneck in atmospheric retrievals. The increasing observational precision from…
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…
A public dataset of Ariel simulated observations for developing exoplanetary atmosphere data reduction pipelines
Lorenzo V. Mugnai, Kai Hou Yip, Andrea Bocchieri +7
Detecting and characterising exoplanet atmospheres remains challenging because atmospheric signals can be comparable to residual noise and instrumental/astrophysical systematics. S…
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…