nuclear masses
· 41 papers, 27 presentations · all tags
Papers filter the publication list
- TITAN mass measurements of neutron-rich Cs, Ba and r-process lanthanide abundances
- Exploration for astromers near $^{132}$Sn with the Canadian Penning Trap
- Astromeric nature of 119m-Ag unveiled through direct mass measurement
- Constraining nuclear mass models using r-process observables with multi-objective optimization
- Mass measurements of neutron-rich nuclides using the Canadian Penning Trap to inform predictions in the r-process rare-earth peak region
- Investigating the effects of precise mass measurements of Ru and Pd isotopes on machine learning mass modeling
- Uncertainty quantification of mass models using Ensemble Bayesian Model Averaging
- Direct mass measurements to inform the behavior of $^{128m}$Sb in nucleosynthetic environments
- Atomic masses with Machine Learning for the astrophysical r process
- Precise mass measurements of radioactive nuclides for astrophysics
- Bayesian averaging for ground state masses of atomic nuclei in a Machine Learning approach
- Solar data uncertainty impacts on MCMC methods for r-process nucleosynthesis
- Physically Interpretable Machine Learning for nuclear masses
- Nuclear masses learned from a probabilistic neural network
- Searching for the origin of the rare-earth peak with precision mass measurements across Ce–Eu isotopic chains
- First application of mass measurement with the Rare-RI Ring reveals the solar $r$-process abundance trend at $A=122$ and $A=123$
- The need for a local nuclear physics feature in the neutron-rich rare-earths to explain solar $r$-process abundances
- Reconstructing masses of merging neutron stars from stellar r-process abundance signatures
- Markov Chain Monte Carlo predictions of neutron-rich lathanide properties as a probe of $r$-process dynamics
- Propagation of statistical uncertainties of Skyrme mass models to simulations of $r$-process nucleosynthesis
- Exploring the mass surface near the rare-earth abundance peak via precision mass measurements at JYFLTRAP
- The nuclear physics uncertainty of kilonova heating rates and the role of fission
- Using excitation-energy dependent fission yields to identify key fissioning nuclei in r-process nucleosynthesis
- Nuclear properties for astrophysical and radioactive-ion-beam applications (II)
- Improving $r$-process calculations for the rare-earth abundance peak via mass measurements at JYFLTRAP
- Precision mass measurements of neutron-rich neodymium and samarium isotopes and their role in understanding rare-earth peak formation
- Masses and lifetimes for $r$-process nucleosynthesis: FRIB outlook
- Systematic and statistical uncertainties in simulated r-process abundances due to uncertain nuclear masses
- The rare earth peak and the astrophysical location of the $r$ process
- Reverse engineering nuclear properties from rare earth abundances in the $r$ process
- The link between rare earth peak formation and the astrophysical site of the $r$ process
- Strong neutron-$\gamma$ competition above the neutron threshold in the decay of $^{70}$Co
- Uncorrelated nuclear mass uncertainties and $r$-process abundance predictions
- The impact of individual nuclear masses on $r$-process abundances
- The impact of uncertain nuclear masses near closed shells on the $r$-process abundance pattern
- The impact of nuclear masses near $N=82$ on $r$-process abundances
- Variances in $r$-process predictions from uncertain nuclear rates
- The impact of global nuclear mass model uncertainties on $r$-process abundance predictions
- Nuclear masses near $N=82$ that influence $r$-process abundances
- Sensitivity studies for a main $r$ process: nuclear masses
- Formation of the rare earth peak: gaining insight into late-time $r$-process dynamics
Presentations filter the presentation list
- Nuclear masses at the predictive frontier
- Accelerating mass modeling with scientific machine learning
- Machine learning and uncertainty quantification for ground state masses
- Modeling masses with an artificial neural network
- Machine Learning the ground state masses of atomic nuclei
- Modeling masses with an artificial neural network
- Utilizing Machine Learning in low energy nuclear physics
- Nuclear data activities for medium mass and heavy nuclei at Los Alamos
- Ground state masses of nuclei with a probabilistic neural network
- Prediction of nuclear masses with an artificial neural network
- Machine Learning for Nuclear Astrophysics (a first attempt)
- Physically Interpretable Machine Learning for nuclear masses
- Machine Learning and Artificial Intelligence for computational nuclear data
- Insights into the $N=126$ shell closure
- 400 MeV/u upgrade of FRIB: advantages for $r$-process studies
- Novel Bayesian inference techniques and their implementation at LANL
- Reverse engineering nuclear properties from $r$-process abundances
- The rare earth peak and the astrophysical location of the $r$ process
- Error bars on $r$-process abundances from uncertain nuclear masses
- Impact of nuclear masses near closed shells on $r$-process abundances
- Nuclear masses near $N=82$ and their effects on $r$-process abundances
- Sensitivity of a main $r$-process to nuclear masses
- Nuclear masses near $N=82$ and their effects on $r$-process abundances
- Nuclear masses near $N=82$ and their effects on $r$-process abundances
- Nuclear masses near $N=82$ and their effects on $r$-process abundances
- Formation of the rare earth peak: gaining insight into late-time $r$-process dynamics
- Formation of the rare earth peak: gaining insight into late-time $r$-process dynamics