all the kittens in all YouTube videos. • “Learn” a very flexible function that takes a video clip as input and returns kittens! or not-kittens! as output. • Use enormous numbers of labeled videos as a training set. • What does this have to do with science? • In it’s basic form: Not much!
365-ish days. • The Sun is 10 million times the mass of the Earth. • The Sun orbits the Milky Way. • The Milky Way contains tens of billions of stars. • The Solar System is 4.6 billion years old. • The Universe is 13.8 billion years old.
of rock and metal, outer are made of gas. • Inner planets are heated by the Sun, outer planets get sunlight but also have residual heat from formation. • There appears to be a continuum between Jupiter-like planets and stars.
• The planet passes between us and the star. • Planet blots out a tiny fraction of the light. • The signal is periodic. • An Earth-like planet blots out 100 parts per million. • NASA Kepler spacecraft alone found thousands.
stars, and deliver a brightness measurement for every star every 30 minutes. • 4.1-year mission, 10 billion stellar measurements. • Found thousands of planets. • Followed by the K2 Mission which did even more. And TESS, on now!
as stars, maybe more! • Many stars have very different planetary systems from our own. • Planets of around twice Earth’s radius are the most common. • Planets with very short periods are common. • Jupiter-like outer planets are very common.
100 parts per million of the Sun’s light. • It does this for 13 hours every 365.25 days. • Stellar variability, spacecraft issues, and photon noise all are larger in amplitude than the signals we seek. • So, is this an impossible task?
many different stars are extremely informative: • We can use the enormous numbers of stars we have • Learn a very flexible model that predicts what a star can do in the future given what it has done in the past… • …and what one star can do in response to spacecraft motions, given what other stars have done. • Remember the kittens?
have this character: • The thing we care about has a very predictable, simple form. • The things we don’t care about are stochastic and complicated. • The tools of machine learning can be harnessed for these tasks. • (Commercial entities usually want performance, not understanding.)
flexible models for stellar and spacecraft variations. • We have a very rigid expectation about what a planet transit is! • Periodic, for example. • Very simple shape. • Duration and period are related. • This contrast between the flexible and rigid models is what makes planet discovery possible.
over to radial-velocity projects. • Trying to measure velocities to better than 1 m/s. • Stellar surface speeds and atmospheric distortions all much larger than this. • Elliptical orbit signature is (once again) very rigid.
and have long periods (few transits in 4.1 years). • These signals are the hardest to find in the data. • There is no way to independently validate these discoveries. • We’re still pretty confident, but there are limits to what we can know.
• No chance of sample return or even radar bounces! • What we find depends heavily on chance. • Photons (light waves) are our only (ish) messengers. • No controlled experiments or counterfactuals. • And yet…
are plentiful around other stars. • Many of them are very different from the planets around the Sun. • Planets are (mainly) found indirectly, as tiny signals. • New data-science technologies are critical to these discoveries.