NASA’s news conference announcing the discovery of Kepler-90i and Kepler-80g was a delightful validation of a principle that has long fascinated me. We have such vast storehouses of astronomical data that finding the time for humans to mine them is deeply problematic. The application of machine learning via neural networks, as performed on Kepler data, shows what can be accomplished in digging out faint signals and hitherto undiscovered phenomena.
Specifically, we had known that Kepler-90 was a multi-planet system already, the existing tools — human analysis coupled with automated selection methods — having determined that there were seven planets there. Kepler-90i emerged as a very weak signal, and one that would not have made the initial cut using existing methods of analysis. When subjected to the machine learning algorithms developed by Google’s Christopher Shallue and Andrew Vanderburg (UT-Austin), the light curve of Kepler-90i as well as that of Kepler-80g could be identified.
Christopher Shallue described the work at the news conference:
“Kepler produced so much data that scientists couldn’t examine it all manually. The method has been to look at the strongest signals, examining them with human eyes and automated tests, not so different from looking for needles in a ...