Researchers have developed S-matrix informed neural networks (SINNs) to reconstruct scattering amplitudes from experimental data. This novel approach learns amplitudes directly from data while adhering to fundamental physics principles. A new data selection method identifies experiments consistent with these principles and with each other, which was applied to $\pi\pi$ scattering to generate reusable amplitudes and correlated uncertainties without assuming a fixed functional form. The framework is adaptable to other scattering processes and constrained physics problems facing inconsistent data. AI
IMPACT Introduces a novel AI methodology for physics research, potentially improving data analysis and model development in high-energy physics.
RANK_REASON Academic paper detailing a new methodology. [lever_c_demoted from research: ic=1 ai=1.0]
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