2014 | 2013 | 2012 | 2011 | 2010 | 2009 | 2008 | 2007 | 2006 | 2005 | 2004 | 2003 | 2002 | 2001 | 2000 | 1999 | 1998 | 1997 | 1996 | 1995 | 1994 | 1993 | 1992 | 1991 | 1990 | 1989 | 1988 | 1987 | 1986 | 1985 | 1984 | 1983 | 1982 | 1981 | 1980 | 1979 | 1978 | 1977 | 1976 | 1975 | 1974 | 1973 | 1972 | 1971 | 1970 | 1969 | 1968 | 1967 | 1966 | 1965 | 1964 | 1963 | 1962 | 1961 | 500 | 0
Inferring the equation of state from neutron star observables via machine learning
Authors: Patra, N.K.; Malik, T.; Pais, H.; Zhou, K.; Agrawal, B.K.; Providencia, C.
Ref.: Phys. Rev. B 865, 139470 (2025)
Abstract: We have conducted an extensive study using a diverse set of equations of state (EoSs) to uncover relationships between neutron star (NS) observables and the underlying EoS parameters using symbolic regression method. These EoS models, derived from a mix of agnostic and physics-based approaches, considered stars composed of nucleons, hyperons, and other exotic degrees of freedom in beta equilibrium. The maximum mass of a NS is found to be strongly correlated with the pressure and baryon density at an energy density approximately 800 MeV.fm-3. We have also demonstrated that the EoS can be expressed as a function of and tidal deformability within the NS mass range 1-2M circle dot. These insights offer a promising and efficient framework to decode the dense matter EoS directly from the accurate knowledge of NS observables.
