5 papers
Toward a Unified Understanding of the Dense Matter Equation of State
Kshitij Agarwal, Johannes Jahan, Behruz Kardan +3
Efforts to understand the equation of state (EOS) of dense nuclear matter at supra-saturation densities have grown more sophisticated over the past decade, driven by a surge in hig…
Microscopic constraints for the equation of state and structure of neutron stars: a Bayesian model mixing framework
A. C. Semposki, C. Drischler, R. J. Furnstahl +1
Bayesian model mixing (BMM) is a statistical technique that can combine constraints from different regions of an input space in a principled way. Here we extend our BMM framework f…
Star Log-extended eMulation: a method for efficient computation of the Tolman-Oppenheimer-Volkoff equations
Sudhanva Lalit, Alexandra C. Semposki, Joshua M. Maldonado
We emulate the Tolman-Oppenheimer-Volkoff (TOV) equations, including tidal deformability, for neutron stars using a new method based upon the Dynamic Mode Decomposition (DMD). This…
From chiral EFT to perturbative QCD: a Bayesian model mixing approach to symmetric nuclear matter
A. C. Semposki, C. Drischler, R. J. Furnstahl +2
Constraining the equation of state (EOS) of strongly interacting, dense matter is the focus of intense experimental, observational, and theoretical effort. Chiral effective field t…
Taweret: a Python package for Bayesian model mixing
Kevin Ingles, Dananjaya Liyanage, Alexandra C. Semposki +1
Uncertainty quantification using Bayesian methods is a growing area of research. Bayesian model mixing (BMM) is a recent development which combines the predictions from multiple mo…