Discovering Undercooked Galaxies using Satellite Galaxy Counts
Keywords: Galaxies, satellites, cosmology, DESI, surveys, simulations
Supervisor: Andrew Cooper - National Tsing Hua University (NTHU)
Number of Students: 1-2
Project Description
In this project, we will investigate observational constraints on the relationship between the stellar mass of central galaxies and the mass of their surrounding dark matter halos. This is arguably the most fundamental problem in the theory of galaxy formation. The average relationship between stellar mass and halo mass is well understood, but the scatter of galaxies around the average is still poorly constrained by observations. Many outstanding problems in galaxy formation boil down to understanding that scatter. How much spread is there in the stellar masses of galaxies at a fixed halo mass, and what is the physical reason for that spread? The main practical work will be to use the spectroscopic catalog from DESI DR1 and background-subtracted photometric catalogs from the DESI Legacy Imaging Survey to count dwarf satellite galaxies around a sample of very broadly defined Milky Way-like hosts. Well-established methods exist for this counting in the literature, so we will follow those methods with more recent data. Galaxies with larger numbers of satellites live, on average, in more massive dark matter halos. This well-understood idea implies satellite counts can be a useful order-of-magnitude estimator of halo mass. Having obtained a sample of satellite counts, we will explore this principle as a way to find individual "extreme" outliers from the expected relation between galaxy mass and halo mass. Specifically, faint central galaxies with unusually rich satellite populations may correspond to cases where the central galaxy is "undercooked" -- in other words, where it ended up with a much lower stellar mass than typical for its halo mass.
We will test whether any such systems we find are consistent with the expected scatter in the stellar mass–halo mass relation, or whether they are evidence for a distinct population of “undercooked” Milky Ways. Theoretical predictions for the expected number of such galaxies are very sensitive to our currently limited understanding of how central supermassive black holes suppress star formation below the mass scale of the Milky Way.
If time permits, we will also compare our results to artificial galaxy catalogs from simulations and explore alternative constraints on halo mass, such as satellite velocities.
Required Background
- The project combines large-scale data analysis with theoretical interpretation. It will involve working with real survey catalogs, performing statistical analysis (following established methods), and testing hypotheses about the galaxy–halo connection. It will be well suited to a self-motivated student who enjoys coding in Python and is interested in computational and statistical approaches to galactic astrophysics. The work involved will be a good introduction to the hot topics and methods involved in modern galaxy formation research. Within the scope of the project you will be free to concentrate on whatever you find most interesting.
- Prior experience with scientific Python (NumPy, SciPy, matplotlib, astropy, etc.) will be extremely helpful (and almost essential for rapid progress). Some knowledge of the basic principles of galaxy formation would be extremely helpful. Communication on this project will be entirely in English. I will be travelling a lot over summer, so meetings will mostly be remote, but would benefit from at least a few "kick off" visits in person to NTHU around the start of the project.