Drawing the line between explosion and collapse in electron-capture supernovae -- I. Impact of conductive flame speeds and ignition conditions on the explosion mechanism

Abstract

Electron-capture supernovae (ECSNe) are commonly thought to result in a collapse to a neutron star. Recent work has shown that a thermonuclear explosion is also a possible outcome. The division between the two regimes has not yet been mapped out. In this study, we investigate the conditions under which the transition from thermonuclear explosion to collapse occurs, and what physical mechanisms drive each outcome. We conducted a parameter study of 56 3D hydrodynamic simulations of ECSN in ONe white dwarfs using a level set based flame model implemented in the Leafs code. We varied both the ignition location and the central density at ignition to determine the conditions of the transition regime. Additionally, we explored two different laminar flame parameterizations and how they impact the simulation outcome. From our parameter study, we find a transition density in the range of $\log\rho_c^{ini}=10.0$ and $10.15$ g cm$^{-3}$, depending on the ignition location and utilized laminar flame speed parameterization. Importantly, we find that for sufficiently high central densities, the burned ashes can sink into the core and trap large amounts of neutron-rich material in the bound remnant. In the transition regime between explosion and collapse, we find that the laminar flame speed plays a critical role by suppressing the formation of instabilities and thereby reducing the nuclear energy generation needed to overcome the collapse. We find that a thermonuclear explosion is possible for a wide range of parameters, whereby a more off-center ignition allows for higher central densities to still result in an explosion. Both the conditions at ignition and the flame physics are critical in determining the outcome. Detailed 3D hydrodynamic simulations of the preceding stellar evolution and the ignition process of the thermonuclear flame are necessary to accurately predict the outcome of ECSNe.