Abstract
The study of extra-solar planets, or simply, exoplanets, planets outside our
own Solar System, is fundamentally a grand quest to understand our place in the
Universe. Discoveries in the last two decades have re-defined our understanding
of planets, and helped us comprehend the uniqueness of our very own Earth. In
recent years the focus has shifted from planet detection to planet
characterisation, where key planetary properties are inferred from telescope
observations using Monte Carlo-based methods. However, the efficiency of
sampling-based methodologies is put under strain by the high-resolution
observational data from next generation telescopes, such as the James Webb
Space Telescope and the Ariel Space Mission. We are delighted to announce the
acceptance of the Ariel ML Data Challenge 2022 as part of the NeurIPS
competition track. The goal of this challenge is to identify a reliable and
scalable method to perform planetary characterisation. Depending on the chosen
track, participants are tasked to provide either quartile estimates or the
approximate distribution of key planetary properties. To this end, a synthetic
spectroscopic dataset has been generated from the official simulators for the
ESA Ariel Space Mission. The aims of the competition are three-fold. 1) To
offer a challenging application for comparing and advancing conditional density
estimation methods. 2) To provide a valuable contribution towards reliable and
efficient analysis of spectroscopic data, enabling astronomers to build a
better picture of planetary demographics, and 3) To promote the interaction
between ML and exoplanetary science. The competition is open from 15th June and
will run until early October, participants of all skill levels are more than
welcomed!