data predicting data; bad Solar System

First thing in the morning, I met with Judy Hoffman (Berkeley) to discuss her computer-vision and machine-learning work. She suggested that machine-learning methods that are auto-encoder-like could be repurposed to make predictions from one kind of data to another kind of data on the same object. For instance, we could train an encoder to predict exoplanet RV signal, given Kepler light curve. Or etc! This appeals to me because it uses machine learning to connect data to data, without commitment to latent quantities or true labels for anything. She pointed me (relatedly) to a new kind of model called ADDA, for which she is responsible.

In the afternoon, Chiara Mingarelli (Flatiron) gave the NYU Astro Seminar about pulsar timing and gravitational radiation, expressing the hope and expectation that this method will deliver signals soon. She told a very interesting story about a false-positive detection that nearly went to press when they figured out that it was resulting from residuals in the Solar System ephemerides. The SS comes in because you have to correct Earth-bound timings to a frame that is at rest (or constant velocity with respect to) the SS barycenter.

This isn't the first time I have heard this complaint. The astronomical community really needs an open-source and probabilistic SS ephemeris, so we can use the SS model responsibly inside of inferences. Freedom-of-information act time?

1 comment:

  1. Seems like this would also be a problem for the 'atomic clocks in space to detect GWs' project. Is JPL opposed to open sourcing?