Stellar spectra capture the light of a star across multiple wavelengths and include absorption features of numerous atoms and molecules from a star’s photosphere. The abundance data many stars then allow us to study the chemical evolution across large scales in our Galaxy and decipher Galactic trends.

Extracting the abundances of atoms and molecules from spectra, however, is non-trivial. You will embark on a journey to go beyond the strictly physical calculation of line features and deliver data-driven measurements. The data will be provided by the large-scale stellar spectroscopic Galactic Archaeology with HERMES (GALAH) survey.

By using already known stellar properties (like stellar ages, masses, and abundances) for a training set, you will use machine-learning algorithms like “The Cannon” or “The Payne” to link stellar flux and these properties. You will start by setting up the machine-learning codes and then test where the spectra respond to different stellar properties, including stellar masses, ages, and elemental abundances of usually shallow absorption features (like molecular carbon and nitrogen).

You will then use the newly extracted data to compare with literature measurements and aim to publish a catalogue of new measurements.

Depending on the progress and length of the project, you will then use the data to study Galactic abundance-age trends with GALAH.