Simulation-based Inference for Exoplanet Atmospheric Retrieval: Insights from winning the Ariel Data Challenge 2023 using Normalizing Flows
Advancements in space telescopes have opened new avenues for gathering vast amounts of data on exoplanet atmosphere spectra. However, accurately extracting chemical and physical properties from these spectra poses significant challenges due to the non-linear nature of the underlying physics.
This paper presents novel machine learning models developed by the AstroAI team for the Ariel Data Challenge 2023, where one of the models secured the top position among 293 competitors. Leveraging Normalizing Flows, our models predict the posterior probability distribution of atmospheric parameters under different atmospheric assumptions.
Moreover, we introduce an alternative model that exhibits higher performance potential than the winning model, despite scoring lower in the challenge. These findings highlight the need to reevaluate the evaluation metric and prompt further exploration of more efficient and accurate approaches for exoplanet atmosphere spectra analysis.
Finally, we present recommendations to enhance the challenge and models, providing valuable insights for future applications on real observational data. These advancements pave the way for more effective and timely analysis of exoplanet atmospheric properties, advancing our understanding of these distant worlds.
Mayeul Aubin (1,2), Carolina Cuesta-Lazaro (1), Ethan Tregidga (1,3), Javier Viaña (4), Cecilia Garraffo (1), Iouli E. Gordon (1), Mercedes López-Morales (1), Robert J. Hargreaves (1), Vladimir Yu. Makhnev (1), Jeremy J. Drake (1), Douglas P. Finkbeiner (1), Phillip Cargile (1) ( (1) Center for Astrophysics | Harvard & Smithsonian, (2) Ecole Polytechnique, (3) University of Southampton, (4) Kavli Institute for Astrophysics and Space Research | Massachusetts Institute of Technology)
Comments: Conference proceeding for the ECML PKDD 2023
Subjects: Earth and Planetary Astrophysics (astro-ph.EP); Instrumentation and Methods for Astrophysics (astro-ph.IM); Machine Learning (cs.LG)
Cite as: arXiv:2309.09337 [astro-ph.EP] (or arXiv:2309.09337v1 [astro-ph.EP] for this version)
Submission history
From: Mayeul Aubin
[v1] Sun, 17 Sep 2023 17:59:59 UTC (1,787 KB)
https://arxiv.org/abs/2309.09337
Astrobiology
