PaperPanorama

Nuclear Theory·nucl-th

Monday·October 6, 2025

9 papers7 primary·2 cross-listed

  1. 08

    ArgoLOOM: agentic AI for fundamental physics from quarks to cosmos

    S. D. Bakshi🇺🇸 · P. Barry🇺🇸 · C. Bissolotti🇺🇸 · I. Cloet🇺🇸 · S. Corrodi🇺🇸 · Z. Djurcic🇺🇸 · S. Habib🇺🇸 · K. Heitmann🇺🇸 · T. J. Hobbs🇺🇸 · W. Hopkins🇺🇸 · S. Joosten🇺🇸 · B. Kriesten🇺🇸 and 3 other authors

    Progress in modern physics has been supported by a steadily expanding corpus of numerical analyses and computational frameworks, which in turn form the basis for precision calculations and baseline predictions in experimental programs. These tools play a central role in navigating a complex landscape of theoretical models and current and potential observables to identify and understand fundamental interactions in physics. In addition, efforts to search for new fundamental interactions increasingly have a cross-disciplinary nature, such that understanding and leveraging interoperabilities among computational tools may be a significant enhancement. This work presents a new agentic AI framework, which we call ArgoLOOM, designed to bridge methodologies and computational analyses across cosmology, collider physics, and nuclear science. We describe the system contours, key internal aspects, and outline its potential for unifying scientific discovery pipelines. In the process, we demonstrate the use of ArgoLOOM on two small-scale problems to illustrate its conceptual foundations and potential for extensibility into a steadily growing agentic framework for fundamental physics.

    hep-phastro-ph.COnucl-th17 citations
  2. 09

    Statistical framework for nuclear parameter uncertainties in nucleosynthesis modeling of r- and i-process

    S. Martinet · G. Goriely · A. Choplin · L. Siess

    Propagating nuclear uncertainties to nucleosynthesis simulations is key to understand the impact of theoretical uncertainties on the predictions, especially for processes far from the stability region, where nuclear properties are scarcely known. While systematic (model) uncertainties have been thoroughly studied, the statistical (parameter) ones have been more rarely explored, as constraining them is more challenging. We present here a methodology to determine coherently parameter uncertainties by anchoring the theoretical uncertainties to the experimentally known nuclear properties through the use of the Backward Forward Monte Carlo method. We use this methodology for two nucleosynthesis processes: the intermediate neutron capture process (i-process) and the rapid neutron capture process (r-process). We determine coherently for the i-process the uncertainties from the (n,) rates while we explore the impact of nuclear mass uncertainties for the r-process. The effect of parameter uncertainties on the final nucleosynthesis is in the same order as model uncertainties, suggesting the crucial need for more experimental constraints on key nuclei of interest. We show how key nuclear properties, such as relevant (n,) rates impacting the i-process tracers, could enhance tremendously the prediction of stellar evolution models by experimentally constraining them.

    astro-ph.SRnucl-exnucl-thEPJA(2025)·2 citations

Affiliations

first authorsco-authorsvia INSPIRE