Macroclumping as solution of the discrepancy between H# and P v mass loss diagnostics for O-type stars
B. Surlan, W.-R. Hamann, A. Aret, J. Kubat, L.M. Oskinova, A.F. Torres
Recent studies of O-type stars demonstrated that discrepant mass-loss
rates are obtained when different diagnostic methods are employed -
fitting the unsaturated UV resonance lines (e.g. P v) gives drastically
lower values than obtained from the Hα emission. Wind clumping
may be the main cause for this discrepancy. In a previous paper, we have
presented 3-D Monte-Carlo calculations for the formation of scattering
lines in a clumped stellar wind. In the present paper we select five
O-type supergiants (from O4 to O7) and test whether the reported
discrepancies can be resolved this way. In the first step, the analyses
start with simulating the observed spectra with Potsdam Wolf-Rayet
(PoWR) non-LTE model atmospheres. The mass-loss rates are adjusted to
fit best to the observed Hα emission lines. For the unsaturated
UV resonance lines (i.e. P v) we then apply our 3-D Monte-Carlo code,
which can account for wind clumps of any optical depths, a non-void
inter-clump medium, and a velocity dispersion inside the clumps. The
ionization stratifications and underlying photospheric spectra are
adopted from the PoWR models. From fitting the observed resonance line
profiles, the properties of the wind clumps are constrained. Our results
show that with the mass-loss rates that fit Hα (and other Balmer
and He II lines), the UV resonance lines (especially the unsaturated
doublet of P v) can also be reproduced without problem when
macroclumping is taken into account. There is no need to artificially
reduce the mass-loss rates, nor to assume a sub-solar phosphorus
abundance or an extremely high clumping factor, contrary to what was
claimed by other authors. These consistent mass-loss rates are lower by
a factor of 1.3 to 2.6, compared to the mass-loss rate recipe from Vink
et al. Macroclumping resolves the previously reported discrepancy
between Hα and P v mass-loss diagnostics.
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