From the 1 of 5 linked papers with an AI index.
5 papers
Beyond Constant Error: Heteroscedastic Bayesian Model Combination for Modeling Unmeasured Nuclei
B. Knight, S. Lalit, P. Giuliani +4
The paper introduces a heteroscedastic Bayesian Model Combination framework to improve uncertainty quantification when combining nuclear energy density functional models, especiall…
Finite-range pairing in nuclear density functional theory
Sudhanva Lalit, Paul-Gerhard Reinhard, Kyle Godbey +1
Pairing correlations are ubiquitous in low-energy states of atomic nuclei. To incorporate them within nuclear density functional theory, often used for global computations of nucle…
The nucleardatapy toolkit for simple access to experimental nuclear data, astrophysical observations, and theoretical predictions
Jérôme Margueron, Christian Drischler, Mariana Dutra +10
Systematic comparisons across theoretical predictions for the properties of dense matter, nuclear physics data, and astrophysical observations (also called meta-analyses) are perfo…
The mass of Sn and Bayesian extrapolations to the proton drip line
Christian M. Ireland, Georg Bollen, Scott E. Campbell +23
The favorable energy configurations of nuclei at magic numbers of neutrons and protons are fundamental for understanding the evolution of nuclear structure. The ${Z=50}…
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…