Artificial Intelligence is Now Helping Kepler Find Planets

Artificial Intelligence Used to Discover Eighth Planet Circling Distant Star

"The discovery came about after researchers Christopher Shallue and Andrew Vanderburg and trained a computer to learn how to identify exoplanets in the light readings recorded by Kepler - the miniscule change in brightness captured when a planet passed in front of, or transited, a star. Inspired by the way neurons connect in the human brain, this artificial "neural network" sifted through Kepler data and found weak transit signals from a previously-missed eighth planet orbiting Kepler-90, in the constellation Draco."

http://images.spaceref.com/news/2017/vanderburg.jpgMedia Telecon Dec. 14th to Announce Latest Kepler Discovery

"NASA will host a media teleconference at 1 p.m. EST (18:00 UTC) Thursday, Dec. 14, to announce the latest discovery made by its planet-hunting Kepler space telescope. The discovery was made by researchers using machine learning from Google. Machine learning is an approach to artificial intelligence, and demonstrates new ways of analyzing Kepler data."

Briefing participants:

* Paul Hertz, Astrophysics Division director at NASA Headquarters in Washington
* Christopher Shallue, senior software engineer at Google AI in Mountain View, California
* Andrew Vanderburg, astronomer and NASA Sagan Postdoctoral Fellow at The University of Texas, Austin
* Jessie Dotson, Kepler project scientist at NASA's Ames Research Center in California's Silicon Valley

Keith's 13 Dec note: This is at the heart of what Christopher Shallue at Google Brain and Andrew Vanderburg at UT Austin have been working on:

- Towards Better Planet Occurrence Rates from Kepler and K2. Andrew Vanderburg. NASA Sagan Fellow The University of Texas at Austin, Sagan/Michelson Fellows Symposium November 9, 2017, PDF
- (Larger image above)

"Kepler is incomplete and unreliable for Earth-sized planets in Earth-like orbits.

Our Approach

1. Increase sensitivity (and therefore completeness) by allowing weaker signals to be considered as planet candidates, at the cost of a higher false positive rate.
2. Use deep learning to more effectively distinguish real signals from false alarms and false positives, keeping reliability high."

Related papers

- Planetary Candidates Observed by Kepler. VIII. A Fully Automated Catalog With Measured Completeness and Reliability Based on Data Release 25
- Zodiacal Exoplanets in Time (ZEIT) V: A Uniform Search for Transiting Planets in Young Clusters Observed by K2

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This page contains a single entry by Keith Cowing published on December 14, 2017 9:16 PM.

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