My research day started with a conversation with Teresa Huang (JHU) and Soledad Villar (JHU) about the regressions that are used to determine stellar parameters. Huang has shown that different machine-learning methods (which are generally over-parameterized) obtain very different gradients in their explicit or implicit functions that connects labels (stellar parameters) to features (stellar spectra), and very different from those indicated by the physical models we have of stellar atmospheres. These differences can be exploited for attack.
Later, Megan Bedell (Flatiron) and I spent time designing projects that are aimed at maximizing the efficiency of radial-velocity exoplanet searches. The idea is: You have a finite amount of telescope time allocated over a fixed (long) interval. How do you assign observation slots to stars to maximize expected yield (or any other statistic you care about)? The answer is going to depend strongly on what we assume about the noise properties of the measurements we make.
No comments:
Post a Comment