Constraining the Equation of State for Neutron Star from Theory and Data

S@INT Seminar

I will introduce our recent progress toward constraining the EOS of dense baryonic matter in cores of the neutron star. The talk will cover:

(1) The inverse problem based on the supervised machine learning from the M-R observation data to the EOS: some highlights from our DENSE (Deep-learning Equation of state for Neutron Star Enterprise) collaboration.

(2) The perturbative approaches to the EOS and a less stringent condition for scale parameter insensitivity. The uncertainty in thermodynamic quantities was conventionally estimated for the functions of the chemical potential, but I will argue that it is a stronger condition than necessary in practice. The looser condition we found takes a quite suggestive form.

(3) Preliminary results for a possibility to probe a crossover transition to quark matter from the gravitational waves. Our results imply that the presence of a crossover transition leads to detectable changes in data from the post-merger process though, to make a conclusive statement, the thermal index needs to be better constrained.

The speaker for this event will lead the meeting remotely from Japan. All interested graduate students and faculty are invited to attend by convening in the INT seminar room (C-421).

Participants are also welcome to join via Zoom. Zoom link will be available via announcement email, or by contacting: amccoy10[at] or gsj6[at]

Kenji Fukushima
University of Tokyo