Reconstructing Atmospheric Parameters of Exoplanets Using Deep Learning
Fuente:
arXiv
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Preprint |
| Published: |
2023
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866909967885271040 |
|---|---|
| author | Giobergia, Flavio Koudounas, Alkis Baralis, Elena |
| author_facet | Giobergia, Flavio Koudounas, Alkis Baralis, Elena |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2310_01227 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Reconstructing Atmospheric Parameters of Exoplanets Using Deep Learning Giobergia, Flavio Koudounas, Alkis Baralis, Elena Earth and Planetary Astrophysics Machine Learning 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. |
| title | Reconstructing Atmospheric Parameters of Exoplanets Using Deep Learning |
| topic | Earth and Planetary Astrophysics Machine Learning |
| url | https://arxiv.org/abs/2310.01227 |