Atmospheres & Climate

Reconstructing Atmospheric Parameters of Exoplanets Using Deep Learning

By Keith Cowing
Status Report
astro-ph.EP
October 5, 2023
Filed under , , ,
Reconstructing Atmospheric Parameters of Exoplanets Using Deep Learning
Exploring exoplanets has transformed our understanding of the universe by revealing many planetary systems that defy our current understanding.

Exploring exoplanets has transformed our understanding of the universe by revealing many planetary systems that defy our current understanding.

To study their atmospheres, spectroscopic observations are used to infer essential atmospheric properties that are not directly measurable. Estimating atmospheric parameters that best fit the observed spectrum within a specified atmospheric model is a complex problem that is difficult to model.

In this paper, we present a multi-target probabilistic regression approach that combines deep learning and inverse modeling techniques within a multimodal architecture to extract atmospheric parameters from exoplanets. Our methodology overcomes computational limitations and outperforms previous approaches, enabling efficient analysis of exoplanetary atmospheres. This research contributes to advancements in the field of exoplanet research and offers valuable insights for future studies.

Flavio Giobergia, Alkis Koudounas, Elena Baralis

Comments: 5 pages + references
Subjects: Earth and Planetary Astrophysics (astro-ph.EP); Machine Learning (cs.LG)
Cite as: arXiv:2310.01227 [astro-ph.EP] (or arXiv:2310.01227v1 [astro-ph.EP] for this version)
https://doi.org/10.48550/arXiv.2310.01227
Focus to learn more
Submission history
From: Alkis Koudounas
[v1] Mon, 2 Oct 2023 14:16:04 UTC (1,380 KB)
https://arxiv.org/abs/2310.01227
Astrobiology

Explorers Club Fellow, ex-NASA Space Station Payload manager/space biologist, Away Teams, Journalist, Lapsed climber, Synaesthete, Na’Vi-Jedi-Freman-Buddhist-mix, ASL, Devon Island and Everest Base Camp veteran, (he/him) πŸ––πŸ»