Speaker
Description
Simulations have revealed correlations between the properties of dark matter halos and their environment, made visible by the galaxies which inherit these connections through their host halos. We define a measure of the environment based on the location and observable properties of a galaxy’s nearest neighbors in order to capture the broad information content available in the environment. We then use a neural network to learn the connection between the multi-dimensional space defined by the observable properties of galaxies and the properties of their host halos using mock galaxy-catalogs from UNIVERSEMACHINE. The trained networks will: 1) reveal new connections between galaxy, halo, and environment; 2) serve as a powerful tool for placing galaxies into halos in future cosmological simulations; and 3) be a framework for inferring the properties of real halos from next-generation survey data, allowing for direct comparison between observational statistics and theory. We will first show the results of estimating the masses of halos and sub-halos. This will be followed by preliminary results on halo properties beyond mass, including satellite membership and concentration.
