optimal extraction; adversarial attacks

On the plane home, I wrote words about optimal extraction, the method for spectral analysis used in most extreme precision radial-velocity pipelines. My point is so simple and dumb, it barely needs to be written. But if people got it, it would simplify pipelines. The point is about flat-field and PSF: The way things are done now is very sensitive to these two things, which are not well known for rarely or barely illuminated pixels (think: far from the spectral traces).

Once home, I met up with a crew of data-science students at the Center for Data Science to discuss making adversarial attacks against machine-learning methods in astronomy. We talked about different kinds of machine-learning structures and how they might be sensitive to attack. And how methods might be made robust against attack, and what that would cost in training and predictive accuracy. This is a nice ball of subjects to think about! I have a funny fake-data example that I want to promote, but (to their credit) the students want to work with real data.