Training on Machine Learning Applied to Space Astronomy Data and Exoplanet Research
- Paper number
IAC-24,A3,IP,97,x91503
- Author
Mrs. Esther Jiaxi Cheng, ILEWG "EuroMoonMars", China
- Coauthor
Ms. Celina You, ILEWG "EuroMoonMars", China
- Coauthor
Ms. Chenming Zhou, ILEWG "EuroMoonMars", China
- Coauthor
Ms. Fatemeh Fazel Hesar, ILEWG "EuroMoonMars", The Netherlands
- Coauthor
Prof. Bernard Foing, ILEWG "EuroMoonMars", The Netherlands
- Coauthor
Dr. Anna Guan, ILEWG "EuroMoonMars", China
- Coauthor
Ms. Lily Yan, ILEWG "EuroMoonMars", China
- Coauthor
Ms. Clara Laforet, ILEWG "EuroMoonMars", France
- Year
2024
- Abstract
Machine learning allows to evaluate the data code provided by different astronomy database repositories such as from NASA or ESA missions. These data can be obtained through the data provided by satellites that are tasked with exploring space. The data are fed back, used and imaged in python through the popular libraries to better help us understand the discoveries of different satellites and what their aim is. Ms. Fazel was able to provide us the code that will be used in python in order to learn more about mission learning and its different aspects. In our Eurospacehub VGCC camp, we have learned about the usages of Python as well as the steps in which we use to image data, through the use of libraries such as astropy etc. We addedmore correlated features and graph analysis such as the random forest regression model to make graphs that can present the data of exoplanets, as well as we were able to evaluate the aims of different satellites, why they are designed as they are and in what ways do they excel that discovering exoplanets.
- Abstract document
- Manuscript document
IAC-24,A3,IP,97,x91503.pdf (🔒 authorized access only).
To get the manuscript, please contact IAF Secretariat.