activity
20242026
collaborators

8 papers

astro-ph.EP2026

Gardening on the Moon: An Advection-Diffusion Model to Guide the Search for Supernova Debris in the Lunar Regolith

Emily S. Costello, John Ellis, Brian D. Fields +2

The vertical redistribution of materials in the lunar regolith - ranging from continuously produced space-weathering products to sporadic pulses of supernova- or kilonova-derived i…

astro-ph.HE2026

Correlated and uncorrelated Monte Carlo neutron capture rate variations in weak -process simulations

Atul Kedia, Jeffrey M. Berryman, Jonathan Cabrera Garcia +7

Reliable predictions of weak rapid neutron capture (-process) abundances require a systematic treatment of nuclear physics uncertainties, especially neutron capture rat…

astro-ph.HE2026

Proton-rich production of lanthanides: the process

Xilu Wang, Amol V. Patwardhan, Yangming Lin +7

The astrophysical origin of the lanthanides is an open question in nuclear astrophysics. Besides the widely studied , , and processes in moderately-to-strongly neutron-ri…

nucl-th2025

Implications of a Weakening N = 126 Shell Closure Away from Stability for r-Process Astrophysical Conditions

Mengke Li, Gail C. McLaughlin, Rebecca Surman

The formation of the third r-process abundance peak near A = 195 is highly sensitive to both nuclear structure far from stability and the astrophysical conditions that produce the…

astro-ph.HE2025

Gamma rays as a signature of r-process producing supernovae: remnants and future Galactic explosions

Zhenghai Liu, Evan Grohs, Kelsey A. Lund +5

We consider the question of whether core-collapse supernovae (CCSNe) can produce rapid neutron capture process (r-process) elements and how future MeV gamma-ray observations could…

astro-ph.SR2025

Constraining Nuclear Mass Models Using r-process Observables with Multi-objective Optimization

Mengke Li, Matthew Mumpower, Nicole Vassh +2

Predicting nuclear masses is a longstanding challenge. One path forward is machine learning (ML) which trains on experimental data, but can suffer large errors when extrapolating t…