Date: October 12, 2018
Wave Dark Matter Predictions from GPU-accelerated Adaptive Mesh
The conventional particle interpretation of cold dark matter (CDM) still lacks laboratory support and struggles to explain the basic properties of dwarf galaxies. This tension motivates wave dark matter (ψDM) composed of extremely light bosons, which suppresses structure below the kpc scale by the uncertainty principle but retains the large-scale structure predicted by CDM. In the first part of this talk, I will present cosmological ψDM simulations with an unprecedented high resolution capable of resolving dwarf galaxies, revealing that every ψDM halo has a prominent soliton core surrounded by fluctuating density granules. These predictions compare favorably with the observations of galaxy formation and help explain the gravitational lensing flux anomalies. I will also discuss critical challenges faced by this model. The second part of this talk will focus on GAMER, a GPU-accelerated adaptive mesh refinement (AMR) code. A rich set of physics modules is incorporated and which outperforms other AMR codes by one to two orders of magnitude. The code scales well to thousands of GPUs and achieves a uniform resolution as high as 10,2403 cells. I will present several ongoing astrophysical projects with GAMER that require substantially higher resolution than previously feasible, including turbulence cascade in galaxy cluster mergers, star formation in isolated disk galaxies, supermassive black hole accretion, and ψDM simulations.