Simulating Turbulence-aided Neutrino-driven Core-collapse Supernova Explosions in One Dimension

Abstract

The core-collapse supernova (CCSN) mechanism is fundamentally three-dimensional with instabilities, convection, and turbulence playing crucial roles in aiding neutrino-driven explosions. Simulations of CCNSe including accurate treatments of neutrino transport and sufficient resolution to capture key instabilities remain amongst the most expensive numerical simulations in astrophysics, prohibiting large parameter studies in 2D and 3D. Studies spanning a large swath of the incredibly varied initial conditions of CCSNe are possible in 1D, though such simulations must be artificially driven to explode. We present a new method for including the most important effects of convection and turbulence in 1D simulations of neutrino-driven CCSNe, called Supernova Turbulence In Reduced-dimensionality, or STIR. Our new approach includes crucial terms resulting from the turbulent and convective motions of the flow. We estimate the strength of convection and turbulence using a modified mixing length theory (MLT) approach introducing a few free parameters to the model which are fit to the results of 3D simulations. For sufficiently large values of the mixing length parameter, turbulence-aided neutrino-driven explosions are obtained. We compare the results of STIR to high-fidelity 3D simulations and perform a parameter study of CCSN explosion using 200 solar-metallicity progenitor models from 9 to 120 M . We find that STIR is a better predictor of which models will explode in multidimensional simulations than other methods of driving explosions in 1D. We also present a preliminary investigation of predicted observable characteristics of the CCSN population from STIR, such as the distributions of explosion energies and remnant masses.

The explosion of 9–29M⊙stars as Type II supernovae: Results from radiative-transfer modeling at one year after explosion

“…This behavior and most of its consequences were confirmed by independent numerical modeling approaches in 1D (e.g., Pejcha & Thompson 2015;Ebinger et al 2019) as well as a semi-analytic description that accounts for 3D effects in a parametrized way (Müller et al 2016). Although some authors express concerns that 3D effects in 1D models are lacking (e.g., Couch et al 2020), the results of these models are compatible with a number of observational constraints, e.g., galactic chemical abundances, a rough correlation between explosion energy and 56 Ni mass (Müller et al 2017b), the spread of observed neutron star masses (within uncertainties), and the observed rarity of Type II SN progenitors above 16-20 M (e.g., Smartt 2009; Sukhbold & Adams 2020, and references therein). Since the exact outcome of the 1D modeling depends on the strength of the neutrino engine, which was calibrated in the S16 explosion simulations for different suggestions of the SN 1987A progenitor, further validation will have to come from observations.…”

Supernova Model Discrimination with Hyper-Kamiokande

“…This family of models (Warren 2019;Couch et al 2020) uses an approach for including effects of convection and turbulence in a one-dimensional simulation, which the authors call STIR (Supernova Turbulence In Reduced-dimensionality). In this approach, the effective strength of convection depends on one parameter, a L , which can be tuned to reproduce results from a three-dimensional simulation of the same progenitor (O'Connor & Couch 2018a).…”

The antesonic condition for the explosion of core-collapse supernovae – I. Spherically symmetric polytropic models: stability and wind emergence

“…The data underlying this article will be shared on reasonable request to the corresponding author. Requests for data originating from Couch et al (2020) should be sent directly to SMC.…”