Characterising exoplanet atmospheres using deep neural networks
Lead Research Organisation:
University College London
Department Name: Physics and Astronomy
Abstract
In the last two and a half decades, we have undergone what is best described as a second Copernican revolution. The discovery of extrasolar planets - i.e. planets orbiting other stars - has fundamentally transformed our understanding of planets, solar systems, their formation histories and our place in the grander scheme of the Milky Way. With the avalanche of recent discoveries (over 3500 confirmed and counting), we have begun to expand comparative planetology from our small scale statistics of 8 solar-system planets to a galactic understanding of planetary science. As the field matures from its initial discovery stage, we are facing entirely new challenges of large-samples, high-dimensional parameter spaces and big data. In order to uniformly characterise large numbers of exoplanets, we require significantly faster and more accurate classification algorithms than what current models provide. In this project, we are developing deep learning and machine learning solutions to help characterise the atmospheres of planets, ranging from our solar system objects to the most extreme hot-Jupiters and lava worlds.
Publications
Hou Yip Kai
(2018)
Integrating light-curve and atmospheric modelling of transiting exoplanets
in arXiv e-prints
Hou Yip Kai
(2019)
Pushing the Limits of Exoplanet Discovery via Direct Imaging with Deep Learning
in arXiv e-prints
Studentship Projects
Project Reference | Relationship | Related To | Start | End | Student Name |
---|---|---|---|---|---|
ST/P006736/1 | 01/10/2017 | 30/09/2024 | |||
1970074 | Studentship | ST/P006736/1 | 01/10/2017 | 30/09/2021 | Kai Hou Yip |
Title | Repository for Pushing the Limits of Exoplanet Discovery via Direct Imaging with Deep Learning |
Description | A data repository to store possessed images from Hubble Space Telescope NICMOS instrument. |
Type Of Material | Database/Collection of data |
Year Produced | 2019 |
Provided To Others? | Yes |
Impact | This database provided processed data which could be used for any other machine learning or deep learning applications in the future. It will be useful especially in the field of direct imaging. |
URL | https://github.com/ucl-exoplanets/DI-Project |
Description | ORBYTS class for secondary school students |
Form Of Engagement Activity | Participation in an activity, workshop or similar |
Part Of Official Scheme? | No |
Geographic Reach | Regional |
Primary Audience | Schools |
Results and Impact | The purpose of this ORBYTS classes is to teach secondary school pupils about exoplanet science, deliver background knowledge of the field and undertook a research project with the students, which may eventually lead to a publication. |
Year(s) Of Engagement Activity | 2020 |
URL | http://www.twinkle-spacemission.co.uk/orbyts/ |