%0 Generic
%A Yip, KH
%A Changeat, Q
%A Waldmann, I
%A Unlu, EB
%A Forestano, RT
%A Roman, A
%A Matcheva, K
%A Matchev, KT
%A Stefanov, S
%A Podsztavek, O
%A Morvan, M
%A Nikolaou, N
%A Al-Refaie, A
%A Jenner, C
%A Johnson, C
%A Tsiaras, A
%A Edwards, B
%A de Oliveira, CA
%A Thiyagalingam, J
%A Lagage, PO
%A Cho, J
%A Tinetti, G
%D 2022
%E Ciccone, Marco
%E Stolovitzky, Gustavo
%E Albrecht, Jacob
%F discovery:10184316
%I Proceedings of Machine Learning Research (PMLR)
%K Exoplanet atmospheres, Generative modelling, uncertainty quantification, approximate inference
%P 1-17
%T Lessons Learned from Ariel Data Challenge 2022 Inferring Physical Properties of Exoplanets From Next-Generation Telescopes
%U https://discovery-pp.ucl.ac.uk/id/eprint/10184316/
%V 220
%X Exo-atmospheric studies, i.e. the study of exoplanetary atmospheres, is an emerging frontier in Planetary Science. To understand the physical properties of hundreds of exoplanets, astronomers have traditionally relied on sampling-based methods. However, with the growing number of exoplanet detections (i.e. increased data quantity) and advancements in technology from telescopes such as JWST and Ariel (i.e. improved data quality), there is a need for more scalable data analysis techniques. The Ariel Data Challenge 2022 aims to find interdisciplinary solutions from the NeurIPS community. Results from the challenge indicate that machine learning (ML) models have the potential to provide quick insights for thousands of planets and millions of atmospheric models. However, the machine learning models are not immune to data drifts, and future research should investigate ways to quantify and mitigate their negative impact.
%Z This is an Open Access paper published under a Creative Commons Attribution 4.0 International (CC BY 4.0) Licence (https://creativecommons.org/licenses/by/4.0/).