A new mass estimate method with hydrodynamical atmospheres for very massive WNh stars

Gautham N. Sabhahit, Jorick S. Vink, Andreas A.C. Sander, Matheus Bernini-Peron, Paul A. Crowther, Roel R. Lefever, Tomer Shenar

Research output: Contribution to journalArticlepeer-review

Abstract

Very massive stars with masses over 100 M are key objects in the Universe for our understanding of chemical and energetic feedback in the Universe, but their evolution and fate are almost entirely determined by their wind mass loss. Here, we aim to determine the mass of the most massive star known in the Local Group R136a1. To this end, we computed the first hydrodynamically consistent non-local thermodynamical equilibrium atmosphere models for R136a1 (WN5h), as well as the binary system R144 (WN5/6h+WN6/7h) in the Tarantula Nebula. Using the Potsdam Wolf–Rayet code, we were able to simultaneously empirically derive and theoretically predict their mass-loss rates and wind velocities. By fitting synthetic spectra derived from these models to multi-wavelength observations, we constrained the stellar and wind properties of R144 and R136a1. We first determined the clumping stratification required by our hydro-models to fit the spectra of R144, using the available dynamical mass estimates for the two components. We then utilised this clumping stratification in hydrodynamic models of R136a1 and estimated a mass of MHydro of 233 M. Remarkably, the estimated mass is close to and fully consistent with chemical homogeneous mass relations. This present-day mass of 233 M provides a lower limit to the initial stellar mass, which could be far higher due to previous wind mass loss.

Original languageEnglish
Article numberA200
JournalAstronomy and Astrophysics
Volume696
DOIs
StatePublished - 1 Apr 2025

Keywords

  • binaries: spectroscopic
  • stars: Wolf–Rayet
  • stars: atmospheres
  • stars: mass-loss
  • stars: massive
  • stars: winds, outflows

All Science Journal Classification (ASJC) codes

  • Astronomy and Astrophysics
  • Space and Planetary Science

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