9 citations · 16 across the 11 of their papers we have counts for
12 papers · 1 filter
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…
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…
Developments in NuWro Monte Carlo generator
Hemant Prasad, Jan T. Sobczyk, Artur M. Ankowski +4
In this article, we highlight physics improvements in the NuWro Monte Carlo event generator. The upcoming version of NuWro will incorporate the integration of the argon spectral fu…
New multinucleon knockout model in NuWro Monte Carlo generator
Hemant Prasad, Jan T. Sobczyk, Artur M. Ankowski +4
We present the implementation and results of a new model for the n-particle n-hole () contribution in the NuWro event generator, grounded in the theoretical framework e…
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…
Empirical fits to inclusive electron-carbon scattering data obtained by deep-learning methods
Beata E. Kowal, Krzysztof M. Graczyk, Artur M. Ankowski +3
Employing the neural network framework, we obtain empirical fits to the electron-scattering cross sections for carbon over a broad kinematic region, extending from the quasielastic…
JLab spectral functions of argon in NuWro and their implications for MicroBooNE
Rwik Dharmapal Banerjee, Artur M. Ankowski, Krzysztof M. Graczyk +3
The Short-Baseline Neutrino program in Fermilab aims to resolve the nature of the low-energy excess events observed in LSND and MiniBooNE, and analyze with unprecedented precision…