Reconstructing Atmospheric Parameters of Exoplanets Using Deep Learning
Flavio Giobergia Alkis Koudounas Elena Baralis
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.