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The study of extra-solar planets, or simply, exoplanets, planets outside ourown Solar System, is fundamentally a grand quest to understand our place in theUniverse. Discoveries in the last two decades have re-defined our understandingof planets, and helped us comprehend the uniqueness of our very own Earth. Inrecent years the focus has shifted from planet detection to planetcharacterisation, where key planetary properties are inferred from telescopeobservations using Monte Carlo-based methods. However, the efficiency ofsampling-based methodologies is put under strain by the high-resolutionobservational data from next generation telescopes, such as the James WebbSpace Telescope and the Ariel Space Mission. We are delighted to announce theacceptance of the Ariel ML Data Challenge 2022 as part of the NeurIPScompetition track. The goal of this challenge is to identify a reliable andscalable method to perform planetary characterisation. Depending on the chosentrack, participants are tasked to provide either quartile estimates or theapproximate distribution of key planetary properties. To this end, a syntheticspectroscopic dataset has been generated from the official simulators for theESA Ariel Space Mission. The aims of the competition are three-fold. 1) Tooffer a challenging application for comparing and advancing conditional densityestimation methods. 2) To provide a valuable contribution towards reliable andefficient analysis of spectroscopic data, enabling astronomers to build abetter picture of planetary demographics, and 3) To promote the interactionbetween ML and exoplanetary science. The competition is open from 15th June andwill run until early October, participants of all skill levels are more thanwelcomed!

ESA-Ariel Data Challenge NeurIPS 2022: Inferring Physical Properties of Exoplanets From Next-Generation Telescopes