Active learning emulators for nuclear two-body scattering in momentum space
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Scientists want to know exactly how they bounce! It's like trying to guess how two bouncy balls will hit and roll away.
This new computer helper, like a super-smart robot friend, learns how these tiny bricks bounce. It watches a few bounces, then gets super good at guessing all the other bounces, super fast!
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arXiv:2512.17842v2 Announce Type: replace-cross Abstract: We extend the active learning emulators for two-body scattering in coordinate space with error estimation, recently developed by Maldonado et al. [Phys. Rev. C 112, 024002], to coupled-channel scattering in momentum space. Our full-order model (FOM) solver is based on the Lippmann-Schwinger integral equation for the scattering $t$-matrix as opposed to the radial Schr\"odinger equation. We use (Petrov-)Galerkin projections and high-fidelity calculations at a few snapshots across the parameter space of the interaction to construct efficient reduced-order models (ROMs), trained by a greedy algorithm for locally optimal snapshot selection. Both the FOM solver and the corresponding ROMs are implemented efficiently in Python using Google'
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Abstract:We extend the active learning emulators for two-body scattering in coordinate space with error estimation, recently developed by Maldonado et al. [Phys. Rev. C 112, 024002], to coupled-channel scattering in momentum space. Our full-order model (FOM) solver is based on the Lippmann-Schwinger integral equation for the scattering $t$-matrix as opposed to the radial Schrödinger equation. We use (Petrov-)Galerkin projections and high-fidelity calculations at a few snapshots across the parameter space of the interaction to construct efficient reduced-order models (ROMs), trained by a greedy algorithm for locally optimal snapshot selection. Both the FOM solver and the corresponding ROMs are implemented efficiently in Python using Google's JAX library. We present results for emulating scattering phase shifts in coupled and uncoupled channels and cross sections, and assess the accuracy of the developed ROMs and their computational speedup factors. We also develop emulator error estimation for both the $t$-matrix and the total cross section. The software framework for reproducing and extending our results is publicly available. Together with our recent advances in developing active-learning emulators for three-body scattering, these emulator frameworks set the stage for full Bayesian calibrations of chiral nuclear interactions and optical models against scattering data with quantified emulator errors.
Comments: 20 pages, 9 figures, 1 Table; minor corrections; close to published version
Subjects:
Nuclear Theory (nucl-th); High Energy Physics - Phenomenology (hep-ph); Nuclear Experiment (nucl-ex); Data Analysis, Statistics and Probability (physics.data-an)
Cite as: arXiv:2512.17842 [nucl-th]
(or arXiv:2512.17842v2 [nucl-th] for this version)
https://doi.org/10.48550/arXiv.2512.17842
arXiv-issued DOI via DataCite
Journal reference: Phys. Rev. C 113, 044001 (2026)
Related DOI:
https://doi.org/10.1103/s6my-pqs9
DOI(s) linking to related resources
Submission history
From: Christian Drischler [view email] [v1] Fri, 19 Dec 2025 17:47:27 UTC (2,097 KB) [v2] Wed, 1 Apr 2026 14:15:38 UTC (1,895 KB)
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