I am in a project with Weichi Yao (NYU) and Soledad Villar (NYU) to look at building machine-learning methods that are constrained by the same symmetries as Newtonian mechanics: Rotation, translation, Galilean boost, and particle exchange, for examples. Kate Storey-Fisher (NYU) joined our weekly call today, because she has ideas about toy problems we could use to demonstrate the value of encoding these symmetries. She steered us towards things in the area of “halo occupation”, or the question of which dark-matter halos contain what kinds of galaxies. Right now halo occupation is performed with very blunt tools, and maybe a sharp tool could do better? We would have the advantage (over others) that anything we found would, by construction, obey the fundamental symmetries of physical law.
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