Explicit Asymptotic Solutions of $\nu_e + e^-$ Neutrino Networks for Large Sets of Partial Differential Equations in Core-Collapse Supernovae

Abstract

In physics, accurately modeling large-scale phenomena such as core-collapse supernovae, (CCSN), and neutron star mergers are computationally challenging and require solving large sets of partial and ordinary differential equations. Traditional methods used widely in the scientific community are predominantly implicit, which are approximations that often require drastic simplifications and can be computationally inefficient. This thesis presents results on a new software suite titled "Fast Explicit Neutrino Networks" or "FENN", that introduces a suite of algebraically stabilized explicit methods known as explicit asymptotic for modeling Neutrino Electron Scattering, (NES), presenting a novel approach that combines the stability of traditional methods with enhanced computational efficiency. Initial results show that FENN can deliver accurate solutions for neutrino networks at improved computational speeds. This thesis further covers new results for scaled networks beyond the constraints of standard energy groupings, as well as the dynamics of neutrino interactions such as the scattering of various neutrino flavors -- electron neutrinos ($\nu_e$), electron anti-neutrinos ($\bar{\nu}_e$), and muon/tau neutrinos ($\nu_{\mu,\tau}$) as well as their anti-particles ($\bar{\nu}_{\mu,\tau}$) -- off electrons.