3D Synthetic Convective Velocity Fields to Initialise Core-Collapse Supernova Simulations from 1D Progenitors

Abstract

Core-collapse supernovae (CCSNe) are among the most energetic and complex astrophysical phenomena, requiring threedimensional (3D) simulations to capture their intricate explosion mechanisms. One of the key ingredients for such simulations is the 3D pre-collapse structure, which can impact the development and geometry of the subsequent explosion. While stellar convection simulations can provide such 3D initial conditions, these remain too expensive and demanding for widespread use. In this work, we present a method to generate synthetic 3D velocity fields for convective zones from 1D initial conditions, creating initial conditions for CCSN simulations using a vector spherical harmonics expansion without the need for expensive hydrodynamic progenitor simulations. The synthetic velocity field is designed to capture the typical scales and velocities of the convective flow as the most relevant parameters for the subsequent explosions. In addition, it respects relevant physical constraints such as the near-anelasticity of flow, vanishing radial vorticity, and zero net angular momentum in the convective zones. A Python implementation of this method is publicly available, offering the CCSN community a practical tool for generating synthetic velocity fields for multi-dimensional simulations to study the impact of 3D progenitor asymmetries on the CCSN mechanism.