Starting Cosmological Simulations from the Big Bang.

The cosmic large-scale structure (LSS) provides a unique testing ground for connecting fundamental physics to astronomical observations. Modeling the LSS requires numerical N-body simulations or perturbative techniques that both come with distinct shortcomings. Here we present the first unified numerical approach, enabled by new time integration and discreteness reduction schemes, and demonstrate its convergence at the field level. In particular, we show that our simulations (i) can be initialized directly at time zero, and (ii) can be made to agree with high-order Lagrangian perturbation theory in the fluid limit. This enables fast, self-consistent, and UV-complete forward modeling of LSS observables.

Galactic Center Excess in a New Light: Disentangling the γ-Ray Sky with Bayesian Graph Convolutional Neural Networks.

A fundamental question regarding the Galactic Center excess (GCE) is whether the underlying structure is pointlike or smooth, often framed in terms of a millisecond pulsar or annihilating dark matter (DM) origin for the emission. We show that Bayesian neural networks (NNs) have the potential to resolve this debate. In simulated data, the method is able to predict the flux fractions from inner Galaxy emission components to on average ∼0.5%. When applied to the Fermi photon-count map, the NN identifies a smooth GCE in the data, suggestive of the presence of DM, with the estimates for the background templates being consistent with existing results.