学术活动

西南理论物理中心系列报告:Constraining Neutron-Star Matter with Microscopic and Macroscopic Collisions

作者:点击次数:更新时间:2023年10月18日

  目: Constraining Neutron-Star Matter with Microscopic and Macroscopic Collisions

报告人: Peter Tsun Ho Pang (彭浚豪,Utrecht University,博士后)

  间: 20231020号(星期五)下午 3:00(北京时间)

  点: 腾讯会议:721-728-965

邀请人: 王锶博

报告摘要:Interpreting high-energy, astrophysical phenomena, such as supernova explosions or neutron-star collisions, requires a robust understanding of matter at supranuclear densities. However, our knowledge about dense matter explored in the cores of neutron stars remains limited. Fortunately, dense matter is not probed only in astrophysical observations, but also in terrestrial heavy-ion collision experiments. Here we use Bayesian inference to combine data from astrophysical multi-messenger observations of neutron stars and from heavy-ion collisions of gold nuclei at relativistic energies with microscopic nuclear theory calculations to improve our understanding of dense matter. We find that the inclusion of heavy-ion collision data indicates an increase in the pressure in dense matter relative to previous analyses, shifting neutron-star radii towards larger values, consistent with recent observations by the Neutron Star Interior Composition Explorer mission. Our findings show that constraints from heavy-ion collision experiments show a remarkable consistency with multi-messenger observations and provide complementary information on the nuclear matter at intermediate densities. This work combines nuclear theory, nuclear experiment, and astrophysical observations, and shows how joint analyses can shed light on the properties of neutron-rich supranuclear matter over the density range probed in neutron stars.

报告人介绍:I did my BSc. in physics and MPhil. in physics at the Chinese University of Hong Kong. After that, I got my Ph.D. in physics at Utrecht University. My research focuses are constraining nuclear equation-of-state via astrophysical and interdisciplinary approaches, testing general relativity, and developing data analysis techniques.