There’s a new post up at the ninazumel.com blog that looks at the statistics of “verification by multiplicity” — the statistical technique that is behind NASA’s announcement of 715 new planets that have been validated in the data from the Kepler Space Telescope.
We normally don’t write about science here at Win-Vector, but we do sometimes examine the statistics and statistical methods behind scientific announcements and issues. NASA’s new technique is a cute and relatively straightforward (statistically speaking) approach.
From what I understand of the introduction to the paper, there are two ways to determine whether or not a planet candidate is really a planet: the first is to confirm the fact with additional measurements of the target star’s gravitational wobble, or by measurements of the transit times of the apparent planets across the face of the star. Getting sufficient measurements can take time. The other way is to “validate” the planet by showing that it’s highly unlikely that the sighting was a false positive. Specifically, the probability that the signal observed was caused by a planet should be at least 100 times larger than the probability that the signal is a false positive. The validation analysis is a Bayesian approach that considers various mechanisms that produce false positives, determines the probability that these various mechanisms could have produced the signal in question, and compares them to the probability that a planet produced the signal.
The basic idea behind verification by multiplicity is that planets are often clustered in multi-planet star systems, while false positive measurements (mistaken identification of potential planets) occur randomly. Putting this another way: if false positives are random, then they won’t tend to occur together near the same star. So if you observe a star with multiple “planet signals,” it’s unlikely that all the signals are false positives. We can use that observation to quantify how much more likely it is that a star with multiple candidates actually hosts a planet. The resulting probability can be used as an improved prior for the planet model when doing the statistical validation described above.
You can read the rest of the article here.
Data scientist with Win Vector LLC. I also dance, read ghost stories and folklore, and sometimes blog about it all.