Drew Jamieson received his undergraduate degrees and master's degree at the University of Guelph. Currently, he is finishing his PhD at Stony Brook University, and he will be starting his first postdoc at the Max Planck Institute for Astrophysics this Fall. His work has focused on large-scale structure theory, cosmic biases, and N-body simulations. Most recently, he has been working as a guest researcher at the Flatiron Institute's Center for Computational Astrophysics on applications of machine learning for large-scale structure.
Talk Title and Abstract:
Large-scale structure in the separate universe: applications and new observables
Upcoming surveys will map out the large-scale structure of the universe over the next decade, providing us with a wealth of data that could greatly improve our constraints on cosmological parameters and potentially give evidence of new physics in cosmology. However, there are many challenges involved in analyzing and interpreting this data. These challenges include the nonlinearity of cosmic structure formation, and the bias of objects that we have access to observationally. In this talk I will review how the separate universe method can address some of these challenges. I will present applications from my own work, including investigations of cosmic void bias, scale-dependent halo bias due to dynamical dark energy isocurvature, and scale-dependent bias from radiation. I will also present a new application of separate universe methods to study the probability distribution function (PDF) of the smoothed density field in simulations. The linear response of the shape of this PDF to the presence of long wavelength density modes can be interpreted as the bias of regions of the universe selected based on their density. This linear response is sensitive to cosmological parameters and is much easier to accurately model than the shape of the PDF.
Would you like to join this Zoom seminar? Please email Donna Hayes.