Today Kate Storey-Fisher (NYU) showed me very nice visualizations of two matched simulations, one dark matter only, and one dark matter plus baryons. The simulations are matched in the sense that they have identical initial conditions, the only difference is that the latter simulation has baryon physics, such as cooling, star formation (approximately), AGN feedback (approximately), and so on. The simulations are from the IllustrisTNG project.
The thing that is interesting to us is whether we can model the differences between the simulations, and in particular whether building such a model will lead to insights about the fundamental physical mechanisms that lead to the differences. Large-scale gravity, after all, doesn't care about the small-scale composition or state of the matter; it only cares about the mass, so why are these simulations different at all? Of course there are lots of things about baryon physics that move matter, so it isn't a paradox, it's just interestingly non-trivial.
Now the question is: If we throw some gauge-invariant machine learning at this problem, will it lead to new insights about physical cosmology? That would be a real win.
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