Searching for a Gravitational Wave Background with Pulsar Timing Arrays

Sarah Vigeland, Asst. Professor, Dept. of Physics, UW-Milwaukee
Pulsar timing arrays use observations of millisecond pulsars to detect nanohertz gravitational waves. The North American Nanohertz Observatory for Gravitational Waves (NANOGrav) Collaboration has recently released their 15-year data set containing observations of 68 millisecond pulsars. These data contain evidence for Hellings-Downs correlations, which are characteristic of a gravitational wave background.

The Self-force on Static and Dynamic Charges in Schwarzschild Spacetime Using the Method of Images
Alan Wiseman, Assoc. Professor, Dept. of Physics, UW-Milwaukee
One of the most basic examples of a self-force phenomenon (sometimes called the radiation reaction force) is that of a small, charged particle near a large spherical mass such as a Schwarzschild black hole. If the particle is held stationary, there are novel electrostatic forces on the particle. If the particle is orbiting the mass, the fields created by the particle back-react on the particle and cause it to depart from its otherwise free-fall motion. There are many ways to solve for the forces and motion in these circumstances, but past solutions have involved considerable technical machinery, and the results are messy and "non-intuitive".

Professor Gabor Csathy, Department of Physics and Astronomy, Purdue University

Emergent Particles and Topology in Flat Landau Bands

Electronic systems with flat energy bands support a variety of topological phases of current interest. The two-dimensional electron gas in the fractional quantum Hall regime is such a system. Ground states of this system found an elegant description in terms of emergent particles called composite fermions.

Dr. Marcus Noack, Research Scientist, Lawrence Berkeley National Lab
Next-Generation Gaussian Processes for Function Approximation, Uncertainty Quantification, and Decision-Making
Gaussian processes (GPs) and Gaussian-related stochastic processes are powerful tools for function approximation, uncertainty quantification, global optimization, and autonomous data acquisition due to their robustness, analytical tractability, and natural inclusion of Bayesian uncertainty estimates. Even so, Gaussian processes are often criticized for poor approximation performance and neck-breaking computational costs in real-life applications. The reason for this gap, however, is not the methodology itself but rather a user-caused lack of flexibility and domain awareness of the underlying prior probability distribution.