data-driven model of supernova yields

I spent the last few days working on abundance-space structure, with some detours to hang out with colleagues in town for the Future of AI meeting at NYU, including Bernhard Schölkopf. Today Schölkopf and I spent some quality time today talking about our next round of projects, in two categories. In the first category, we talked about simple situations in astronomy in which independent components analysis might be useful. One is supernova yields: Jennifer Johnson (OSU) had asked me last weekend what kinds of supernovae create potassium; I promised her not an answer but a method for getting an answer. Schölkopf suggested that this is a perfect case for ICA: We want to matrix factorize, but in a way that separates causes, not variance! ICA is based on some great math, to which he pointed me.

In the second category, we talked about the next generation of what astronomers call “image differencing”. We want to build out the Causal Pixel Model we built for Kepler self-calibration so that it could work in situations (like LSST) in which there is heterogeneous temporal and spatial coverage of the sky. Then, if it works, we can use everything we have to predict the imaging data we are trying to subtract (or really just predict precisely).

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