Roman Unlocks a New Frontier of Research on Low-Mass Galaxies
Program ID 19000
Science Category Stellar Populations
Program Type Analysis
Category Medium
Principal Investigator Kristen McQuinn
PI Institution Space Telescope Science Institute / STScI
Co-Investigators
  • Yao-Yun Mao (University of Utah)
  • Alexis Brown (University of Utah)
Abstract One of the most important predictions of modern cosmology and galaxy formation theory is that the universe should be teeming with extremely low‑mass galaxies, especially those with a stellar mass below 10^5 solar masses, commonly referred to as ultra-faint dwarf galaxies (UFDs). While we have confirmed the existence of UFDs at close distances, beyond the Local Group their abundance and properties are unknown as current surveys lack the sensitivity to find them. Using Roman data, we can search over untapped parameter space to build a census of very low-mass galaxies in the nearby universe. This census will inform us on how the smallest galactic structures grow, allow us to quantify evolutionary differences in low-mass galaxy assembly as a function of mass and environment, and possibly reveal new physics that our current models do not capture. Using an established and validated algorithm, we will perform a comprehensive search of the HWLAS data to (1) build a census of the low-mass galaxy population in the nearby universe including both isolated and satellite galaxies of different mass scales; (2) characterize the properties of all newly discovered galaxies including distances, structural parameters, masses, HI content, quenching timescales; (3) measure the slope of the galaxy mass function to unprecedentedly low-mass regime and constrain key characteristics of the population (e.g., gas fraction, quenched fraction) as a function of mass and environment. Our results will disentangle environmental effects, reionization physics, dark matter physics, and intrinsic mass‑assembly histories in the growth of the smallest galaxy structures, and provide decisive tests of the Lambda-CDM model.