Project E
Excavating the fossil record of KILOGAS galaxies
Amelia Fraser-McKelvie (ESO), Nikki Geesink (ESO)
Clues to a galaxy’s past are concealed in the light that we observe today. Galactic archaeology techniques including spectral energy distribution (SED) fitting employs multiwavelength data from galaxies to infer their star formation activity across cosmic time. The ultraviolet to infrared spectra of all galaxies arises from stellar light, either directly, or reprocessed by the gas and dust of the surrounding interstellar medium. Each galaxy possesses a unique SED that contains a large amount of information about the stars of a galaxy from which we can derive information on its formation and evolution. This chemical ‘fingerprint’, coupled with information on the galaxy’s current star formation and cold gas reservoir, allows us to gain a complete picture of the gas-star formation cycle in nearby galaxies.
The KILOGAS survey (https://kilogas.space/) is the largest survey of resolved cold gas in galaxies with complementary optical integral field spectroscopic observations. Its aim is to transform our understanding of the drivers of star formation activity in galaxies. The student will work with Dr Fraser-McKelvie (ESO), Nikki Geesink (ESO), and external members of the KILOGAS team (located in Europe, North America, and Australia) to perform SED fitting on the KILOGAS sample of galaxies to determine their star formation histories. In combination with complementary ALMA observations and derived properties provided by the KILOGAS team, the student will work to understand the link between a galaxy’s past behaviour, and its current day properties – effectively linking their past to their present.
Skills acquired include:
-How to run modern SED-fitting codes and interpret multi-wavelength galaxy data
-Fundamentals of extragalactic astronomy and the gas–star-formation cycle
-Practical coding, data analysis, and scientific collaboration skills
-Experience working with a world-leading international research team
Some prior knowledge of python or R would be an asset.
