Simulations predict that the large-scale structure of the universe forms a Cosmic Web, a filamentary structure laid down by the gravitational evolution of dark matter and on which baryons infall as primordial gas to eventually cool and form galaxies. While this general picture is a robust prediction, our understanding of the Web suffers from a dearth of observational constraints, leaving models uncertain on numerous important details. A telling example is the observational fact that only 10-15% of baryons form stars by the present time, while they should all have formed stars if not actively prevented from cooling. The vast majority of baryons must therefore remain in a diffuse gaseous phase, forcing models to invoke strong, and at times d'hoc, feedback prescriptions.  This situation motivated the U.S. Astrophysics Decadal Survey, Astro2020, to identify effort to better understand the Cosmic Web as one of its three priority areas, “Cosmic Ecosystems”.

The thesis project will predict observational signatures of Cosmic Web models. The student will work with data from publicly available state-of-the-art simulations (e.g., EAGLE and IllustrisTNG) to predict scaling relations between observable galaxy properties (e.g., stellar mass, star formation rate, AGN activity), the dark matter distribution, and diffuse gas emission from the circumgalactic and intergalactic media (CGM and IGM) across the electromagnetic spectrum. The student will compare his/her predictions to existing publicly available data and to the expected performance of large upcoming surveys, such as Euclid, SPHEREx, and the Nancy Roman space missions, the Vera C. Rubin Legacy Survey of Space and Time (LSST), and cosmic microwave background surveys by the Simons Observatory and CMB-S4. 

The Cosmology Group at the APC laboratory is heavily invested in the Euclid, Rubin, Simons Observatory and CMB-S4 projects. The student will become a member of these collaborations.

Candidates should have a strong background in cosmology and the astrophysics of galaxies, and technical skills in programming and an ease working with large simulation datasets. She/he should be comfortable working in large collaborations and traveling to collaboration workshops and meetings.