Abstract
Neutron stars contain matter at densities far beyond what can be recreated in a laboratory. Understanding how this dense matter behaves is crucial for understanding quantum chromodynamics. Despite advances in nuclear theory and experiments, many questions remain about these extreme conditions. New observations are now shedding light on these mysteries. This talk will provide an overview of the multi-messenger approach to studying neutron star matter, combining data from nuclear theory, terrestrial experiments, and astrophysical observations. By integrating these datasets with Bayesian statistics, we can obtain a state-of-the-art understanding of matter under the Universe's most extreme conditions and deepen our grasp of fundamental physics.
Dr. Peter T. H. Pang is an NWO Veni Fellow and Honorary Croucher Postdoctoral Fellow at Nikhef (Dutch National Institute for Subatomic Physics) and Utrecht University, specializing in multi-messenger nuclear astrophysics. Originally from Hong Kong, he received his PhD in Physics from Utrecht University in 2022. His research spans gravitational-wave data analysis, tests of general relativity, multi-messenger astronomy, and the study of nuclear physics through the combination of astrophysical observations and terrestrial experiments. He is advancing this work through machine learning and GPU-accelerated computing to handle increasingly complex multi-messenger datasets.