Bernhard Schölkopf How Can We Use Machine Learning in the Search for Exoplanets?
© Maximilian Dörrbecker
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Exoplanets are planets beyond our own solar system. Since they do not emit much light and moreover are very close to their parent stars they are difficult to detect directly. When searching for exoplanets, astronomers use telescopes to monitor the brightness of the parent star under investigation: Changes in brightness can point to a passing planet that obstructs part of the star’s surface. The recorded signal, however, contains not only the physical signal of the star but also systematic errors caused by the instrument. As BERNHARD SCHÖLKOPF explains in this video, this noise can be removed by comparing the signal of the star of interest to those of a large number of other stars. Commonalities in their signals might be due to confounding effects of the instrument. Using machine learning, these observations can be used to train a system to predict the errors and correct the light curves.
LT Video Publication DOI: https://doi.org/10.21036/LTPUB10261
Removing Systematic Errors for Exoplanet Search via Latent Causes
- Bernhard Schölkopf, David W. Hogg, Dun Wang, Daniel Foreman-Mackey, Dominik Janzing, Carl-Johann Simon-Gabriel and Jonas Peters
- arXiv preprint arXiv:1505.03036
- Published in 2015