Work

Reconstructing Atmospheric Parameters of Exoplanets Using Deep Learning

Year: 2023

Type: conference-paper

Abstract: 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.

Source: IEEE International Conference on Application of Information and Communication Technologies (AICT)

DOI: 10.1109/aict59525.2023.10313185

Authors: Flavio Giobergia, Alkis Koudounas, Elena Baralis

Institution: Politecnico di Torino

Language: English

Topic: Stellar, planetary, and galactic studies

Subfield: Astronomy and Astrophysics

Field: Physics and Astronomy

Domain: Physical Sciences

SDG: Climate action

Open Access status: green

Funder: European Commission