Ultra-light dark matter models, which feature particles whose masses are so light that their behavior can be treated as a collective wave rather than individual particles, are an intriguing alternative to “standard” cold dark matter. They feature a rich and unique phenomenology on astrophysical scales, with implications for small-scale tensions. The most well-studied member of this family is commonly called fuzzy dark matter, where dark matter is composed of scalar particles with a mass of ≈ 10⁻²² eV. Although strong constraints have been placed on this particular model, it remains an important benchmark to understand structure formation in more general wave dark matter scenarios.
In this talk, I will present numerical simulations of cosmic structure
formation with fuzzy dark matter, and highlight the unique signatures of fuzzy and other ultra-light dark matter models. In particular, I will introduce new cosmological fuzzy dark matter simulations including
baryons, using the IllustrisTNG galaxy formation model. Since simulation efforts for fuzzy dark matter have thus far mainly focused on understanding its properties in a dark matter-only context, these
simulations will provide invaluable insight in a more realistic
scenario, especially regarding how strongly the unique behavior of fuzzy dark matter affects important baryonic processes and astronomical
observables, such as star and galaxy formation.
Ultracold atoms in optical lattices are one of the major platforms for quantum simulations and many-body physics. Their versatile nature and high controllability enable the realization of novel quantum phases, the study of phase transitions, and the observation of non-equilibrium dynamics. In the past, most experiments studied these phenomena in the simplest lattice geometries, namely the primitive 1D lattice, the square lattice, and the cubic lattice.
In this talk, we look at many-body physics in 2D in different lattice geometries. These range from the triangular lattice, where geometric frustration can give rise to chiral superfluids, over the Kagome lattice, where it gives rise to a flat band, to optical quasicrystals, where the notion of band-structure is not even directly applicable. In all cases I will present experimental results and highlight open questions.
Accessing theoretically the ground state of interacting quantum matter is a long-standing challenge, especially for complex two-dimensional systems. Recent developments have highlighted the potential to solve the quantum many-body problem by means of so-called neural quantum states. The enabling idea of this approach is to harness the power of machine learning by encoding the many-body wave function into an artificial neural network. In this talk, I will aim at introducing the main idea and central developments of this computational approach. In particular, I will outline one of the critical limitations, which until recently has prohibited the training of modern large-scale deep network architectures. I will show how this key limitation has been now resolved through the so-called minimum-step stochastic reconfiguration method. I will demonstrate for paradigmatic frustrated quantum magnets that this enables the neural quantum states method to reach regimes and accuracies beyond what is accessible by other computational approaches. Further, I will highlight the recent results on solving the real-time dynamics of correlated quantum matter, which has allowed us to verify for instance for the first time the quantum Kibble-Zurek mechanism for interacting quantum many-body systems in two spatial dimensions.
