Relation of Observable Stellar Parameters to Mass-Loss Rate of AGB Stars in the LMC
- 1. New Mexico Tech
- 2. Iowa State University
Description
Mass loss in Asymptotic Giant Branch (AGB) stars has historically proven difficult to characterize accurately.
This is due to a multitude of factors such as differing composition--including chemistry, metallicity, and differences in carbon and oxygen ratios--and differences in their ongoing motion--particularly, differing pulsation modes.
In mass-loss formulations, the mass-loss rate depends on some combinations of stellar parameters, including but not limited to pulsation period, mass, and luminosity.
Using a combination of stellar models and archival data, we have been working at improving the relation between these parameters and the mass-loss rate of the stars. We have used the models and data to improve on the period-mass-luminosity and radius-mass-luminosity relations for M and C stars in both the fundamental and overtone pulsation modes. Current work is focused on accounting for any remaining sources of scatter in the relation between
Files
Prager_et_al_Relation_Observable_Stellar_Parameters-1.png
Files
(6.3 MB)
Name | Size | Download all |
---|---|---|
md5:9d69042427479fd24450f67e84487bc8
|
5.9 MB | Preview Download |
md5:ddcaa0ca41d766a3f70f5c5d16161442
|
404.3 kB | Preview Download |
Additional details
References
- Riebel et al. (2012). THE MASS-LOSS RETURN FROM EVOLVED STARS TO THE LARGE MAGELLANIC CLOUD. VI. LUMINOSITIES AND MASS-LOSS RATES ON POPULATION SCALES.
- Trabucchi et al. (2018). Modelling long-period variables – I. A new grid of O-rich and C-rich pulsation models.
- Willson (2000). MASS LOSS FROM COOL STARS: Impact on the Evolution of Stars and Stellar Populations.
- Willson (2009). Big, Cool, and Losing Mass: Dependence of Mass Loss Rates on L, R, M and Z.
- Willson and Marengo (2012). Miras.
- Harris, C.R., Millman, K.J., van der Walt, S.J. et al. (2020). Array programming with NumPy.
- Virtanen et al. (2020). SciPy 1.0: Fundamental Algorithms for Scientific Computing in Python.
- Hunter, J.D. (2007). Matplotlib: A 2D graphics environment.