Abstract:
According to our standard cosmological model, the formation of cosmic structures in the Universe is driven by the gravitational collapse of small matter density fluctuations present in the early Universe. The non-linear nature of gravitational collapse makes it difficult to develop a physical understanding of how complex late-time cosmic structures emerge from these linear initial conditions. In this talk, I will present an explainable deep learning framework for extracting new knowledge about the underlying physics of cosmological structure formation. I will focus on an application to dark matter halos, which form the building blocks of cosmic large-scale structure and wherein galaxy formation takes place. The goal is to use interpretable neural networks to discover the independent degrees of freedom in the density profiles of dark matter halos. I will show that the model is able to reproduce the known variations encapsulated by previous empirical approaches. The network then goes further and discovers an additional factor of variation in the outer profile, which we identify as related to infalling dark matter onto the halo (also known as the ‘splashback’ effect).

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