Mass loss—formula, recipe, prescription or algorithm?

Details Mass loss—formula, recipe, prescription or algorithm? Mass loss—formula, recipe, prescription or algorithm?

Dec 2008

adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2008phst..133a4008w&link_type=abstract

Physica Scripta, Volume 133, Issue , pp. 014008 (2008).

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Scientific paper

Many formulae have been proposed to describe how stars lose mass. Most of them were determined by fitting observational data, but a few developed from theoretical models. We may define the deathline as where \dot{M} = \dot{M}_{\textrm{crit}} = M \dot{X}/X , where X is a variable that follows the evolutionary track. For the stars near the tip of the asymptotic giant branch (AGB) this may be expressed in terms of L, and all empirical formulae take 1 Modot stars to \dot{M}_{\textrm{crit}} = 5\times 10^{-7}\,M_{\odot}\,\textrm{yr}^{-1} at log L=3.6 3.7, and 2 Modot to \dot{M}_{\textrm{crit}} =10^{-6}\,M_{\odot}\,\textrm{yr}^{-1} between log L=4.0 and 4.1. However, the log log slopes of these relations, the exponents in a fitted power law near the deathline, vary from <3 to nearly 20 from one rule to another. How can mass loss be included in stellar evolution modelling if the formulae are so varied? Here, we describe an approach that emphasizes what the observations actually tell us: a prescription for mass loss to be included in stellar evolution models. We then examine examples of two cases that describe most of the mass loss that affects the future of a star like our Sun, and discuss how these may be handled. The first is the case of positive feedback, mass loss on the AGB; the second is a case of negative feedback, mass loss from a subset of red giant branch (RGB) stars.

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