7 papers
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…
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…
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…
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…
Electron-nucleus cross sections from transfer learning
Krzysztof M. Graczyk, Beata E. Kowal, Artur M. Ankowski +4
Transfer learning (TL) allows a deep neural network (DNN) trained on one type of data to be adapted for new problems with limited information. We propose to use the TL technique in…
Generative adversarial neural networks for simulating neutrino interactions
Jose L. Bonilla, Krzysztof M. Graczyk, Artur M. Ankowski +4
We propose a new approach to simulate neutrino scattering events as an alternative to the standard Monte Carlo generator approach. Generative adversarial neural network (GAN) model…