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arXiv · 0911.0799

The Mass-Loss Return From Asymptotic Giant Branch Stars to The Large Magellanic Cloud Using Data From The SAGE Survey

Abstract

The asymptotic giant branch (AGB) phase is the penultimate stage of evolution for low- and intermediate-mass stars. AGB star outflows inject a significant amount of material into the interstellar medium (ISM), seeding new star formation. AGB mass loss is thus a crucial component of galactic chemical evolution. The Large Magellanic Cloud (LMC) is an excellent site for AGB studies. Over 40,000 AGB candidates have been identified using photometric data from the Spitzer Space Telescope Surveying The Agents of a Galaxy's Evolution (SAGE) mid-infrared (MIR) survey, including about 35,000 oxygen-rich, 7000 carbon-rich and 1400 "extreme" sources. For the first time, SAGE photometry reveals two distinct populations of O-rich sources in the LMC: a faint population that gradually evolves into C-rich stars and a bright, massive population that circumvents this evolution, remaining O-rich. This work aims to quantify the mass-loss return from AGB stars to the LMC, a rough estimate for which is derived from the amount of MIR dust emission in excess of that from starlight. I show that this excess flux is a good proxy for the mass-loss rate, and I calculate the total AGB injection rate to be (5.9-13) x 10{-3} Msun/yr. A more accurate determination requires detailed dust radiative transfer (RT) modeling. For this purpose, I present a grid of C-rich AGB models generated by the RT code 2DUST, spanning a range of effective temperatures, gravities, dust shell radii and optical depths as well as a baseline set of dust properties obtained by modeling a carbon star, data for which was acquired as part of the spectroscopic follow-up to SAGE. AGB stars are the best laboratories for dust studies, and the development of a model grid will reinforce future research in this field.

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Sundar Srinivasan. 2009-11-04. The Mass-Loss Return From Asymptotic Giant Branch Stars to The Large Magellanic Cloud Using Data From The SAGE Survey. https://arxiv.org/abs/0911.0799

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