Simulation-based Inference for Exoplanet Atmospheric Retrieval: Insights
  from winning the Ariel Data Challenge 2023 using Normalizing Flows
Mayeul Aubin Carolina Cuesta-Lazaro Ethan Tregidga Javier Viaña Cecilia Garraffo Iouli E. Gordon Mercedes López-Morales Robert J. Hargreaves Vladimir Yu. Makhnev Jeremy J. Drake Douglas P. Finkbeiner Phillip Cargile
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.