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Inclusive electron-nucleus cross section models from domain adaptation
high-energy-physics-experimenthigh-energy-physics-phenomenologymachine-learningnuclear-experimentnuclear-theory
We apply transfer learning (TL) to construct data-driven models of inclusive electron-nucleus cross sections. Starting from an ensemble of deep neural networks pretrained on \(^{12}\)C data, we fine-tune the models separately for \(^{3}\)He, \(^{6}\)Li, \(^{16}\)O, \(^{27}\)Al, \(^{40}\)Ca, and \(^{
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