2023/10/02 by Flavio Giobergia, Giobergia, Flavio, Alkis Koudounas +3
Engineering · Mathematics · Physics and Astronomy · #Artificial intelligence #Astrobiology #Atmosphere (unit) #Atmospheric dynamics #Atmospheric model #Computer science #Earth and Planetary Astrophysics (astro-ph.EP) #Environmental science #Exoplanet #FOS: Computer and information sciences #FOS: Physical sciences #Field (mathematics) #Geology #Inertial Sensor and Navigation #Machine Learning (cs.LG) #Machine learning #Mathematics #Meteorology #Physics #Probabilistic logic #Remote sensing #Stars #Stellar, planetary, and galactic studies
paper · pdf · doi:10.48550/arxiv.2310.01227
openalex publication_date 2023/10/02 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.