Machine Learning Will be one of the Best Ways to Identify Habitable Exoplanets - Universe Today universetoday storybywill
This artist’s impression shows the planet orbiting the Sun-like star HD 85512 in the southern constellation of Vela . Credit: ESO
As they indicate, MI techniques will allow astronomers to conduct the initial characterizations of exoplanets more rapidly, allowing astronomers to prioritize targets for follow-up observations. By “following the water,” astronomers will be able to dedicate more of an observatory’s valuable survey time to exoplanets that are more likely to provide significant returns.
To make sure their algorithm was up to the task, Pham and Kaltenegger did some considerable calibrating. This consisted of creating 53,130 spectra profiles of a cold Earth with various surface components – including snow, water, and water clouds. They then simulated the spectra for this water in terms of atmosphere and surface reflectivity and assigned color profiles. As Pham explained:
Similarly, they were surprised to see how well the trained XGBoost could identify water on the surface of rocky planets based on color alone. According to Kaltenegger, this is what filters really are: a means for separating light into discreet “bins.” “Imagine a bin for all red light , then a bin for all the green light, from light to dark green ,” she said.