The Galaxy-Halo Connection
I am interested in the physics that drives galaxy formation and evolution. This consists of a wide array of processes. In our modern understanding, galaxies form within dark matter halos. These host halos presumably play a role in shaping the galaxies that form inside them, but the nature of dark matter makes this relationship particularly hard to divine. However, like trees in a forest, the properties of dark matter halos are tied to their environment. My research uses this connection, which is inherited by galaxies through their host halos, to learn about the nature of dark matter halos and their role in explaining the great diversity of galaxy shapes and sizes observed.
Machine Learning
The use of machine learning in science and in astronomy in particular is sky-rocketing! Machine learning provides a way forward for exploring complicated correlations and enormous datasets previously limited by traditional techniques. This is becoming more and more relevant as next-generation telescopes bring colossal observational catalogs and simulations grow in size. In my research, I use neural networks to learn the complex relationships between environment, galaxy, and dark matter halo. By training neural networks on simulated mock catalogs of galaxies and halos like those from UniverseMachine, my work opens a new window onto the invisible properties of dark matter halos and the galaxy-halo connection.
Cosmology with LSST
I have recently started working with Professor Tim Eifler and the Arizona Cosmology Lab on a new project in preparation for the upcoming Legacy Survey of Space and Time (LSST). Working with the data processing and analysis pipeline being developed by the many folks involved in the Dark Energy Science Collaboration, I intend to integrate the code developed by members of the Arizona Cosmology Lab into the pipeline for cosmological analysis for wise spread use by the cosmology community.
Papers and Other Links
Halo Properties from Observable Measures of Environment: I. Halo and Subhalo Masses: Bowden et al. 2023
Finding environmental measures sensitive to halo properties using neural networks (Talk at KITP)