Groenewegen, M.

published in Herschel Space Observatory Proposal, id.1554, pp. 1554 (2011)

Abstract: Stellar mass loss is the main characteristic of the late stages of stellar evolution. Almost all stars with masses between 1 and 8 solar masses pass through the Red Giant Branch (RGB) and Asymptotic Giant Branch (AGB). Mass loss on the AGB has been relatively well studied and is the main subject of the MESS (Mass-loss of Evolved StarS) Herschel GTKP that the proposers are involved in. However, it is known from Horizontal Branch morphology and other indicators that about 0.2 solar mass is lost in the RGB, and for stars of low initial mass this dominates the mass lost on the AGB. Relatively little is known about the mass loss process on the RGB. It has been established that mass loss in the form of dust is present at the tip of the RGB, but little is known about the exact conditions under which dusty winds develop near the tip of the RGB, mainly because the expected excess is small. On the AGB the mass loss is strongly related to pulsation, and also the stars at the tip of the RGB that show the largest mass-loss rates show pulsation. In this study we propose to obtain PACS and SPIRE photometry for a very well defined sample of 12 field RGB stars with accurate Hipparcos parallaxes for which ground-based optical and NIR photometry are available. Ground-based high resolution optical spectra that we will obtain and the optical+NIR photometry will very precisely fix the flux from the central star. We show that we will be able to detect the FIR excess if it is present, thanks to the accurate absolute flux calibration of PACS and SPIRE. We will be able to detect total mass-loss rates as low as 1.e-9 solar masses/year. This would push the existing limit on accurate mass-loss rate determination of RGB stars by an order of magnitude, and will allow us to say how prominent dust mass-loss is on the RGB and its dependence on stellar parameters, thereby potentially improving on Reimers law like recipes in standard use in stellar evolutionary models.

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