Systematic survey of the effects of wind mass loss algorithms on the evolution of single massive stars

Abstract

Mass loss processes are a key uncertainty in the evolution of massive stars. They determine the amount of mass and angular momentum retained by the star, thus influencing its evolution and presupernova structure. Because of the high complexity of the physical processes driving mass loss, stellar evolution calculations must employ parametric algorithms, and usually only include wind mass loss. We carried out an extensive parameter study of wind mass loss and its effects on massive star evolution using the open-source stellar evolution code MESA. We provide a systematic comparison of wind mass loss algorithms for solar-metallicity, nonrotating, single stars in the initial mass range of 15 M to 35 M . We consider combinations drawn from two hot phase (i.e., roughly the main sequence) algorithms, three cool phase (i.e., post-main-sequence) algorithms, and two Wolf-Rayet mass loss algorithms. We discuss separately the effects of mass loss in each of these phases. In addition, we consider linear wind efficiency scale factors of 1, 0.33, and 0.1 to account for suggested reductions in mass loss rates due to wind inhomogeneities. We find that the initial to final mass mapping for each zero-age main-sequence (ZAMS) mass has a ∼50% uncertainty if all algorithm combinations and wind efficiencies are considered. The ad-hoc efficiency scale factor dominates this uncertainty. While the final total mass and internal structure of our models vary tremendously with mass loss treatment, final luminosity and effective temperature are much less sensitive for stars with ZAMS mass 30 M . This indicates that uncertainty in wind mass loss does not negatively affect estimates of the ZAMS mass of most single-star supernova progenitors from pre-explosion observations. Our results furthermore show that the internal structure of presupernova stars is sensitive to variations in both main sequence and post main-sequence mass loss. The compactness parameter ξ ∝ M/R(M) has been identified as a proxy for the "explodability" of a given presupernova model. We find that ξ varies by as much as 30% for models of the same ZAMS mass evolved with different wind efficiencies and mass loss algorithm combinations. This suggests that the details of the mass loss treatment might bias the outcome of detailed core-collapse supernova calculations and the predictions for neutron star and black hole formation.

Sensitivity of the lower edge of the pair-instability black hole mass gap to the treatment of time-dependent convection

“…After a dynamical pulse, if/once the core has recovered hydrostatic equilibrium, we create a new stellar model of reduced mass with the entropy and chemical profile of the bound material. We do not include any wind mass loss, although the treatment of winds is known to influence the core structure of massive stars (Renzo et al 2017). Preliminary tests including wind mass loss showed the same trends discussed here.…”

Pulsational Pair-instability Supernovae in Very Close Binaries

“…Finally, approx21 adds the 56 Cr and 56 Fe isotopes and tuned steady-state reaction sequences to attain a reasonably accurate lower electron fraction Y e (as compared to much larger reaction networks) for presupernova models (Paxton et al 2015). To test the accuracy of this few-isotope network during a pulse we have also computed the first pulse of our 84M model using the 203 isotope network of Renzo et al (2017) which is tuned to properly capture silicon burning. Figure 14 shows the results of our convergence tests.…”

60Fe in core-collapse supernovae and prospects for X-ray and gamma-ray detection in supernova remnants

“…Substantial structural changes can be triggered by seemingly small changes to the reaction rates (Tur et al 2010), illustrating the cumulative effect of the prevalent uncertainties in stellar evolution modelling such as rotation (Palacios et al 2005;Edelmann et al 2017), convection and convective boundaries (e.g. Meakin & Arnett 2007;Jones et al 2017;Davis et al 2018), mass loss (Palacios et al 2005;Limongi & Chieffi 2006;Renzo et al 2017), and opacities (Woosley & Heger 2007a).…”