A Systematic Survey of the Effects of Wind Mass Loss Algorithms on the Evolution of Single Massive Stars

Abstract

Mass loss is a key uncertainty in the evolution of massive stars. Stellar evolution calculations must employ parametric algorithms for mass loss, and usually only include stellar winds. We carry out a parameter study of the effects of wind mass loss 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 $15-35\,M_\odot$. We consider combinations drawn from two hot phase algorithms, three cool phase algorithms, and two Wolf-Rayet algorithms. We consider linear wind efficiency scale factors of $1$, $0.33$, and $0.1$ to account for 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 $\sim 50\%$ 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 observable parameters are much less sensitive for ZAMS mass $\lesssim 30\,M_\odot$. 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. Furthermore, we show that the internal structure of presupernova stars is sensitive to variations in both main sequence and post main-sequence mass loss. We find that the compactness parameter $\xi\propto M/R(M)$ varies by as much as $30\%$ for a given ZAMS mass evolved with different wind efficiencies and mass loss algorithm combinations. [abridged]Comment: Accepted for publication on A&A, 22 pages + 2 appendixes, 12 figures, online input parameters available at https://stellarcollapse.org/renzo2017 and data at https://zenodo.org/record/292924#.WK0q2tWi6W