Forecasting neutron star temperatures: predictability and variability
Dany Page (1), Sanjay Reddy (2) ((1) Instituto de Astronom\'ia,, Universidad Nacional Aut\'onoma de M\'exico,(2) Institute for Nuclear Theory,, University of Washington, Seattle)

TL;DR
This paper models neutron star thermal relaxation after accretion events, demonstrating how observations constrain internal physics parameters and enable predictions of long-term temperature variability, exemplified by the neutron star XTE J1701-462.
Contribution
It introduces a method to constrain neutron star crust physics parameters using thermal relaxation observations, improving long-term temperature predictions.
Findings
Future cooling of XTE J1701-462 is strongly constrained.
Crust physics uncertainties are minimized by fitting early observations.
Long-term variability depends mainly on core temperature.
Abstract
It is now possible to model thermal relaxation of neutron stars after bouts of accretion during which the star is heated out of equilibrium by nuclear reactions in its crust. Major uncertainties in these models can be encapsulated in modest variations of a handful of fudge parameters that change the crustal thermal conductivity, specific heat, and heating rates. Observations of thermal relaxation constrain these fudge parameters and allow us to predict longer term variability in terms of the neutron star core temperature. We demonstrate this explicitly by modeling ongoing thermal relaxation in the neutron star XTE J1701-462. Its future cooling, over the next 5 to 30 years, is strongly constrained and depends mostly on its core temperature, uncertainties in crust physics having essentially been pinned down by fitting to the first three years of observations.
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
