报告题目:Machine learning approach to the neutron star equation of state

报  告  人:Yuki Fujimoto, University of Tokyo

报告时间:7月22日(周三) 上午 10:00

Zoom会议ID:  965 5606 5963,密码:922011;会议链接:https://zoom.us/j/96556065963?pwd=MCt1YkFPTmdkWWpoNHd5NEU1cDE3dz09

报告摘要:We develop a method of machine learning utilizing deep neural networks to estimate the equation of state (EoS) of cold dense matter. EoS is the crucial ingredient for describing neutron stars. We consider here the specific problem to find the most likely EoS using the set of neutron star data from the x-ray telescopes. In this talk, firstly we propose an efficient procedure to deal with this problem, and then we confirm the validity and the accuracy of this method using mock data sets. We apply our method to the currently observed neutron star data, and put a constraint on the EoS. We finally discuss the result in light of the recent neutron star phenomenology.

[1] Y. Fujimoto, K. Fukushima, and K. Murase, Phys. Rev. D 98, 023019 (2018).

[2] Y. Fujimoto, K. Fukushima, and K. Murase, Phys. Rev. D 101, 054016 (2020).