Ariel Data Challenge 2025: Methods to Extract Planetary Signals for the Ariel Space Telescope

This workshop showcases winning approaches from the 2025 Ariel Data Challenge, a Kaggle competition tackling a notoriously difficult signal processing problem: extracting extremely faint exoplanet signatures from complex, non-linear noise in spatiotemporal data. The 2024 challenge drew thousands of competitors worldwide, yet no solution achieved the mission's stringent performance thresholds. The 2025 competition raised the bar with higher-fidelity simulations that closely mirror real observational conditions from the Ariel Space Telescope.

Winners will present novel architectures and algorithms for two core problems: advanced denoising in the presence of structured noise and robust uncertainty quantification under extreme signal-to-noise ratios. These solutions emerged from a realistic constraint environment where both accuracy and calibrated confidence estimates are mission critical.

While framed within an astronomy context, the technical challenges are broadly applicable. Whether you are an researchers interested in this domain, or simply interested in ML applications in space science, come and join us!