Egger, Jo Ann; Alibert, Yann (6 May 2022). A Neural Network Based Approach to Modelling the Internal Structure of Transiting Exoplanets (Unpublished). In: Bern Data Science Day 2022. University of Bern. 06 May 2022.
|
Text (Poster)
Poster_BDSD2022_JoAnnEgger.pdf - Other Available under License Creative Commons: Attribution (CC-BY). Download (413kB) | Preview |
Characterising the composition and internal structure of transiting exoplanets is a problem of high interest in current exoplanetary science. Since the observable parameters of such planets are very scarce, it is not possible to fully constrain the internal structure parameters from the observations. Instead, Bayesian inference is used, a method based on Bayes’ theorem which updates the previously assumed probability of the internal structure parameters (the prior) based on the probability of the observation given the same parameters (the likelihood), returning the posterior distribution of these parameters. In our case, the likelihood is determined based on an internal structure model, which calculates the radius of a planet with a given mass and composition.
We developed a neural network based grid scheme that has multiple advantages over the traditionally used approach based on Markov chain Monte Carlo methods. In addition to training a deep neural network to replace the internal structure model, we also take advantage of the correlation between observations of planets in a multiplanetary system, leading to an increase in computation time that is only linear when adding an additional planet. This makes our method ideal to apply e.g. to planetary systems that were observed by ESA’s CHEOPS mission.
Item Type: |
Conference or Workshop Item (Poster) |
|---|---|
Division/Institute: |
08 Faculty of Science > Physics Institute > Space Research and Planetary Sciences > Theoretical Astrophysics and Planetary Science (TAPS) 08 Faculty of Science > Physics Institute > Space Research and Planetary Sciences 08 Faculty of Science > Physics Institute |
UniBE Contributor: |
Egger, Jo Ann, Alibert, Yann Daniel Pierre |
Subjects: |
500 Science > 520 Astronomy 500 Science > 530 Physics |
Projects: |
[1587] Bern Data Science Day 2022-05-06 Official URL |
Language: |
English |
Submitter: |
Jo Ann Egger |
Date Deposited: |
25 May 2022 15:06 |
Last Modified: |
05 Dec 2022 16:20 |
Additional Information: |
Bern Data Science Day 2022-05-06 collection |
BORIS DOI: |
10.48350/170230 |
URI: |
https://boris.unibe.ch/id/eprint/170230 |
Actions (login required)
 |
Edit item |