The biggest challenge for next-generation ground-based spectroscopic surveys is the emission from the atmosphere itself. The Subaru Night Sky Spectrograph (SuNSS) studies this emission, called airglow, which allows us to improve our calibrations, sky subtraction, and understanding of the airglow itself.
Cosmology
We show that cosmic variance must be accounted for in analyses of extremely massive galaxies in high-z JWST surveys, and provide tools and considerations for doing so.
We use Graph Neural Networks to determine how galaxies relate to their environments, and determine a special neighbourhood length scale which shape galaxies the most.
We show that the highest redshift galaxies detected with the James Webb Space Telescope, will have a highly skewed distribution due to galaxy clustering, which can be quantified through an effect known as cosmic variance.
We show how building a graph neural network emulator using merger trees encoded as graphs can both dramatically improve the precision with which we make simulated galaxies and improve our understanding of the galaxies we make!
This project aimed to show how building an emulator using merger trees encoded as graphs can both dramatically improve the precision with which we make simulated galaxies and improve our understanding of the galaxies we make! We achieved that with Mangrove (Paper out now)!
Using Graph Neural Networks, me and a group of friends showed that event reconstruction speed and precision for low-energy neutrino events can be greatly improved.
Solving the issue of spatially varying PSFs for the COSMOS survey