Binary-Stripped Stars as Core-Collapse Supernovae Progenitors

TL;DR

This study systematically compares the core structures and explosion outcomes of single and binary-stripped stars, revealing significant differences that impact supernova characteristics and compact remnant masses.

Contribution

It provides the first self-consistent simulation comparison of single and binary-stripped stars across a range of initial masses, highlighting their divergent core properties and explosion behaviors.

Findings

Binary-stripped stars have less compact cores than single stars.

Binary-stripped stars tend to produce neutron stars in the mass gap.

Differences influence supernova statistics and remnant mass distributions.

Abstract

Most massive stars experience binary interactions in their lifetimes that can alter both the surface and core structure of the stripped star with significant effects on their ultimate fate as core-collapse supernovae. However, core-collapse supernovae simulations to date have focused almost exclusively on the evolution of single stars. We present a systematic simulation study of single and binary-stripped stars with the same initial mass as candidates for core-collapse supernovae (11 - 21 M$_{\odot}$). Generally, we find that binary-stripped stars core tend to be less compact, with a more prominent, deeper silicon/oxygen interface, and explode preferentially to the corresponding single stars of the same initial mass. Such a dichotomy of behavior between these two modes of evolution would have important implications for supernovae statistics, including the final neutron star masses, explosion energies, and nucleosynthetic yields. Binary-stripped remnants are also well poised to populate the possible mass gap between the heaviest neutron stars and the lightest black holes. Our work presents an improvement along two fronts, as we self-consistently account for the pre-collapse stellar evolution and the subsequent explosion outcome. Even so, our results emphasize the need for more detailed stellar evolutionary models to capture the sensitive nature of explosion outcome.