Astrophysical environments at the extremes play crucial roles in astronomy, as well as being extraordinary laboratories for studying fundamental physics. One of these, Core-Collapse Supernovae (CCSNe), result when a massive star reaches the end of its life, collapses, and then violently explodes. In astronomer's and physicist’s attempts to understanding the history of the Universe, these explosions play many roles. They are the site where neutron stars and black hole are formed and therefore a gateway to our study of high energy astrophysics; the incredible energy released in these events drives the evolution of galaxies; and the elements forged and dispersed by the explosion are the building blocks of life as we know it. For over 60 years, intense research has gone into understanding the implosion and subsequent explosion of these massive stars. Progress has always been driven by numerical simulations at the forefront of our high performance computing abilities. Only recently have we reached the stage where the computing power available is enough to handle the demands sets by the physics. Modern simulations of CCSNe are readily achieving successful explosions, especially in 2D, but also, importantly, in 3D. What is learned from these simulations will represent a leap forward in our understanding of the connected astrophysics. With this allocation, we propose to continue our exploration of CCSNe with the FLASH code. For this large allocation, we propose three research directions (computational work packages; CWPs) for our multidimensional CCSNe simulations in support of the PI’s VR Consolidator Grant during 2021-2026.
CWP #1: Explosive Failed Supernovae. This project will explore a new channel for failed supernovae and generate observational predictions to aid astronomers to search for these objects.
CWP #2: Magnetorotational Explosions. This project will support our efforts to model hypernovae and allow participation in international comparisons of magnetohydrodynamic simulations.
CWP #3: Implementation of Efficient Neutrino Transport. This project aims to utilize new methods to push our knowledge of long-term multidimensional simulations.