主持人：李衢智 助理教授

时间：2026年11月9日（周一）14：30

地点：犀浦校区二教2539会议室

题目：Compact stars as laboratories for dense nuclear matter: from white dwarfs to neutron stars

主讲人：Yao Ma

Yao Ma is a postdoctoral researcher at the School of Frontier Sciences, Nanjing University. He received his B.S. from Jilin University in 2016 and his Ph.D. in theoretical physics from Peking University in 2021, where he worked on hadron interactions with chiral perturbation theory and dispersion relations, in particular pion–nucleon scattering and pion photoproduction. His current research connects the nuclear physics of dense matter with the structure and observables of compact stars. He builds equations of state from chiral and chiral-scale effective theories and from relativistic mean-field models, and applies them to white dwarfs and neutron stars, together with gravitational-wave signals and Bayesian, machine-learning and AI-agent analyses of multimessenger data. His work has appeared in Physical Review C (Letters), Physical Review D, Chinese Physics C, Communications in Theoretical Physics and The Astrophysical Journal. He was selected for the 2025 Jiangsu Excellent Postdoctoral Program.

Abstract

White dwarfs and neutron stars are natural laboratories for dense nuclear matter, covering densities from that of ordinary atoms up to several times nuclear saturation density. In this talk I will present our unified calculation scheme for both classes of compact star, in which the nuclei of white-dwarf matter and the uniform matter of neutron stars follow from the same relativistic mean-field Lagrangian, so that the two branches and the transition between them are described on the same footing. This scheme provides a useful tool to investigate the strong interaction in stellar matter. I will discuss the structure of white dwarfs and the gravitational waves from their binary inspirals, and, with interactions constrained by chiral effective theories, the equation of state of neutron stars, where purely hadronic matter accounts for both nuclear-matter properties and current NICER and gravitational-wave observations, including stars above two solar masses. Finally, I will show how Bayesian analyses and AI methods, from neural networks to an autonomous AI agent, confront these interactions with nuclear and astrophysical data.