A statistical view of nearby AGB stars: dust and outflow properties
Thesis Supervisor: Peter Scicluna
When low- and intermediate-mass stars approach the end of their lives, they undergo a dramatic increase in luminosity and start to lose copious amounts of mass in a phase called the Asymptotic Giant Branch (AGB). The mass lost during this phase plays an important role in driving the chemical enrichment of galaxies and enriching the interstellar medium with dust. However, despite the importance of this mass loss, it remains poorly understood; we have yet to identify what starts the mass loss, how much it is enriched and how it changes with time. To address these questions, and to understand how evolved stars impact our own galaxy, we have begun the Nearby Evolved Stars Survey (NESS), which is observing a volume-limited sample of ~1000 evolved stars within 3 kpc of the Sun. By observing and characterising these stars over a large wavelength range, we aim to answer the above questions.
A number of projects are possible in the context of NESS. These include, but are not limited to:
- developing a suite of dust radiative-transfer models for comparison to optical, infrared and sub-mm observations of AGB stars. These models will be used to determine the dust mass-loss rates of AGB stars in the NESS sample, and make predictions for follow-up observations from ground- and space-based observatories in the optical, infrared and sub-mm, including the VLTI.
- detailed modelling of infrared spectra and sub-mm photometry to derive the dust properties in AGB stars.
- developing statistical analysis methods to analyse and understand correlations between different parts of the NESS dataset, e.g. mass-loss rates, dust properties, outflow velocities, isotope ratios, extensions, variability properties, stellar properties, etc.
- reducing and analysing ALMA observations of the NESS sample, exploring the mass-loss history and outflow morphology in gas lines.
- modelling resolved emission in gas and dust to understand the photodissociation, temperature profiles and mass-loss histories.
- apply machine-learning methods to interpret the dataset and explore the consequences for other/future samples of evolved stars.
Elements from several of these projects can be combined to suit your interests. Depending on which interest you most, you may work with various members of the NESS team, which is distributed across Europe, the Americas and Asia, and with several staff members at ESO.