Project outline

The majority of stars will go through an asymptotic giant branch (AGB) phase, losing their outer layers by means of a stellar outflow and enriching the interstellar medium (ISM) with the building blocks for the next generation of stars and planets. The outflow is triggered by stellar pulsations that aid dust formation, which then launches a dust-driven wind. Large-scale structures, like spirals and disks, are ubiquitously observed around AGB stars and are thought to be caused by binary interaction with a (sub)stellar companion. Despite the importance of dust to launching the outflow, we still do not know exactly how it is formed. Dust formation is a fundamentally chemical process: gas-phase molecules form larger molecular clusters, condense into a seed particle and grow by accreting more molecules.

Thanks to a decade of quantum chemical calculations, it is finally possible to model dust formation in a chemical kinetic way in AGB outflows. ASHES aims to quantify how the dust produced by AGB stars depends on the specific outflow from which it originates. To do so, we will develop the first comprehensive chemical network that includes dust nucleation, allowing us to uniquely follow all chemical processes throughout the entire outflow. Using machine learning, the network will be emulated and linked to hydrodynamics, creating the first comprehensive 3D hydrochemical model. This will enable accurate predictions of composition, structure, and size of the dust, revealing how it is shaped by (sub)stellar companions and outflow dynamics.

ASHES is at the forefront of astrochemistry, combining state-of-the-art observations, development of unique computational models, and novel results from theoretical chemistry.

Team

The ASHES team will consist of 2 PhD students and 2 postdoctoral researchers at University College Dublin.

We invite applications for the first PhD posistion! The student will extend the chemical reaction network to include dust nucleation and create 3D hydrochemical models by post-processing hydrodynamical models.