machine learning, twins, excitation temperature

After our usual start at the Coffee Nerd, it was MW Group Meeting, where we discussed (separately) Cepheids and Solar-neighborhood nucleosynthesis. On the latter, Oliver Philcox (St Andrews) has taken the one-zone models of Jan Rybizki (MPIA) and made them 500 times faster using a neural-network emulator. This emulator is tuned to interpolate a set of (slowly computed) models very quickly and accurately. That's a good use of machine learning! Also, because of backpropagation, it is possible to take the derivatives of the emulator with respect to the inputs (I think) and therefore you also get derivatives, for optimization and sampling.

The afternoon's PSF Coffee meeting had presentations by Meg Bedell (Chicago) about Solar Twin abundances, and by Richard Teague (Michigan) about protoplanetary disk TW Hya. On the former, Bedell showed that she can make extremely precise measurements, because a lot of theoretical uncertainties cancel out. She finds rock-abundance anomalies (that is, abundance anomalies that are stronger in high-condensation-temperature lines) all over the place, which is context for results from Semyeong Oh (Princeton). On TW Hya, Teague showed that it is possible to get pretty consistent temperature information out of line ratios. I would like to see two-dimensional maps of those: Are there embedded temperature anomalies in the disk?

No comments:

Post a Comment