Re-optimization of a deep neural network model for electron-carbon scattering using new experimental data
Beata E. Kowal, Krzysztof M. Graczyk, Artur M. Ankowski, Rwik Dharmapal Banerjee, Jose L. Bonilla, Hemant Prasad, Jan T. Sobczyk
我们介绍了一个更新的深度神经网络模型,用于包容性电子碳散射。 使用bootstrap模型[Phys.Rev.C 110(2024)2,025501]作为之前,我们纳入了最近的实验数据,以及深度非弹性散射区域中较旧的测量,得出重新优化的后模型。 我们研究这些新输入对模型预测和相关不确定性的影响。 最后,我们评估与Hyper-Kamiokande和DUNE实验相关的动力学范围内的横截面预测。
We present an updated deep neural network model for inclusive electron-carbon scattering. Using the bootstrap model [Phys.Rev.C 110 (2024) 2, 025501] as a prior, we incorporate recent experimental data, as well as older measurements in the deep inelastic scattering region, to derive a re-optimized posterior model. We examine the impact of these new inputs on model predictions and associated uncertainties. Finally, we evaluate the resulting cross-section predictions in the kinematic range relevan...
