2019 Fiscal Year Annual Research Report
Can planetesimal accretion break planet resonance?
| Project/Area Number |
16K17654
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| Research Institution | Japan Aerospace EXploration Agency |
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Principal Investigator |
タスカー エリザベス 国立研究開発法人宇宙航空研究開発機構, 宇宙科学研究所, 准教授 (40620373)
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| Project Period (FY) |
2016-04-01 – 2020-03-31
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| Keywords | exoplanet: properites / numerical methods / catalogues / machine learning |
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| Outline of Annual Research Achievements |
This research aimed to predict the inflated radius of exoplanets using simulations. Initial results were disappointing, so we developed a neural network to identify patterns in the catalogue of exoplanet properties. Neural networks excel at finding trends in high-dimensional data that cannot be plotted on a graph.
While neural networks cannot reveal the association between properties, the trained network could estimate missing planetary properties, such as mass and radius. It is frequently impossible (even with further observations) to measure these properties, making these estimates valuable tools in developing planet formation theories.
This work also included a published outreach and education tool for exploring how the Earth’s environment is effected by small changes in properties.
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| Remarks |
Video and website created to support published journal papers (1) Estimating Planetary Mass with Deep Learning and (2) Earth-Like.
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