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Machine-learning-based neutrino-event generation constrained by experimental measurements
Jose Luis Bonilla, Krzysztof M. Graczyk, Rwik Dharmapal Banerjee +3
We present a generative surrogate for charged-current \(ν_μ\)-nucleus scattering that can be calibrated to measured differential cross sections without event-level experimental sam…
Inclusive electron-nucleus cross section models from domain adaptation
Krzysztof M. Graczyk, Beata E. Kowal, Rwik Dharmapal Banerjee +3
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…
Fine-tuning final state interactions model in NuWro Monte Carlo event generator
Hemant Prasad, Jan T. Sobczyk, Rwik Dharmapal Banerjee +4
Recent experimental data from MINERvA on transverse kinematics observables across four different nuclear targets - carbon, oxygen, iron, and lead - have been utilized to refine the…
Spectral function approach in NuWro: modeling of multinucleon final states in quasielastic scattering
Artur M. Ankowski, Rwik Dharmapal Banerjee, Jan T. Sobczyk +4
Neutrino-oscillation experiments performed in the few-GeV energy region create an urgent demand for a significant improvement in the accuracy of modeling of neutrino interactions w…
Transfer Learning for Neutrino Scattering: Domain Adaptation with GANs
Jose L. Bonilla, Krzysztof M. Graczyk, Artur M. Ankowski +4
Transfer learning (TL) is used to extrapolate the physics information encoded in a Generative Adversarial Network (GAN) trained on synthetic neutrino-carbon inclusive scattering da…
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 +4
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 rec…