Recent experimental data from MINER nu A on transverse kinematics observables across four different nuclear targets-carbon, oxygen, iron, and lead-have been utilized to refine the modeling of final state interaction effects in the NuWro Monte Carlo neutrino event generator. For this purpose, we have developed an event reweighting tool for future applications to adjust the strength of final-state interactions. This study highlights the requirement for stronger nucleon reinteractions than previously assumed, but it still falls within the uncertainty range observed in a study comparing proton transparency measurements. This conclusion has significant implications for both experimental and theoretical work involving NuWro.

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 ^12C data, we fine-tune the models separately for ^3He, ^6Li, ^16O, ^27Al, ^40Ca, and ^56Fe. The resulting models improve for all targets, marginally so for oxygen, where the carbon baseline is already adequate, although their predictive robustness depends on the amount, coverage, and precision of the available target data. We systematically study how model performance depends on the number of fine-tuned layers, on the fraction and selection of the training data, and on the overlap between the source and target kinematic domains. The layer-wise analysis shows that oxygen requires only shallow adaptation, whereas helium, calcium, and iron require substantially deeper fine-tuning. Lithium represents the least robust case because of its limited dataset, while aluminum demonstrates a strong sensitivity to a small subset of highly constraining measurements. For selected kinematic configurations outside the coverage of the carbon training data, the adapted models remain consistent with the measurements within their estimated uncertainties. Finally, we compare the resulting predictions with those of the phenomenological F1F2 model.

Transfer learning (TL) is used to extrapolate the physics information encoded in a generative adversarial network trained on synthetic neutrino-carbon inclusive scattering data to related processes such as neutrinoargon and antineutrino-carbon interactions. We investigate how much of the underlying lepton-nucleus dynamics is shared across different targets and processes. We also assess the effectiveness of TL when training data is obtained from a different neutrino-nucleus interaction model. Our results show that TL not only reproduces key features of lepton kinematics, including the quasielastic and Delta-resonance peaks, but also significantly outperforms generative models trained from scratch. Using data sets of 10,000 and 100,000 events, we find that TL maintains high accuracy even with limited statistics. Our findings demonstrate that TL provides a well-motivated and efficient framework for modeling (anti)neutrinonucleus interactions and for constructing next-generation neutrino-scattering event generators, particularly valuable when experimental data are sparse.

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 function for quasi-elastic scattering, along with the MINERνA parametrization of the axial form factor. Additionally, the new release will feature the implementation of the Valencia 2020 model for meson exchange current. The previously used simplistic delta resonance model for single-pion production will be replaced by a more accurate Ghent hybrid model in the upcoming version of NuWro. We also discuss the recent advancements made by the Wroclaw Neutrino Group in applying machine-learning techniques to achieve model-independent reconstruction of lepton-nucleus interactions

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 with atomic nuclei. Here, we report an updated implementation of the spectral function approach in the Monte Carlo generator, which consistently treats multinucleon final-states in quasielastic scattering at the inclusive and exclusive level. After validating its accuracy against inclusive electron-scattering data, we compare its predictions to various neutrino cross sections from MicroBooNE. We find that with the multinucleon contribution, these data are reproduced with χ^2 per degree of freedom of 1.3–1.8, compared to 2.7–7.2 without it.

We utilize transfer learning to extrapolate the physics knowledge encoded in a Generative Adversarial Network (GAN) model trained on synthetic charged-current (CC) neutrino-carbon inclusive scattering data. This base model is adapted to generate CC inclusive scattering events (lepton kinematics only) for neutrino-argon and antineutrino-carbon interactions. Furthermore, we assess the effectiveness of transfer learning in re-optimizing a custom model when new data comes from a different neutrino-nucleus interaction model. Our results demonstrate that transfer learning significantly outperforms training generative models from scratch. To study this, we consider two training data sets: one with 10,000 and another with 100,000 events. The models obtained via transfer learning perform well even with smaller training data. The proposed method provides a promising approach for constructing neutrino scattering event generators in scenarios where experimental data is sparse.

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) models are developed to simulate charged current neutrino-carbon collisions in the few-GeV energy range. We consider a simplified framework to generate muon kinematic variables, specifically its energy and scattering angle. GAN models are trained on simulation data from uro Monte Carlo event generator. Two GAN models have been obtained: one simulating quasielastic neutrino-nucleus scatterings and another simulating all interactions at given neutrino energy. The models work for neutrino energy ranging from 300 MeV to 10 GeV. The performance of both models has been assessed using two statistical metrics. It is shown that both GAN models successfully reproduce the distribution of muon kinematics.

We present the implementation and results of a new model for the n-particle n-hole (np-nh) contribution in the NuWro event generator, grounded in the theoretical framework established by the Valencia group in 2020. For the 2p2h component, we introduce a novel nucleon sampling function with tunable parameters to approximate correlations in the momenta of outgoing nucleons. These parameters are calibrated by comparing our results to those of the Valencia model across a range of incoming neutrino energies. In addition, our model incorporates a distinct contribution from the 3p3h mechanism. We discuss the differences between the new NuWro implementation, the original Valencia model, and the previous NuWro version, focusing on the distribution of outgoing nucleon momenta. Finally, we assess the impact of the hadronic model on experimental analyses involving hadronic observables.

We present an updated deep neural network model for inclusive electron-carbon scattering. Using the bootstrap model [B. E. Kowal et al., Phys. Rev. C 110, 025501 (2024)], we incorporate recent experimental data, as well as older measurements in the deep inelastic scattering region, to derive a reoptimized 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 relevant to the Hyper-Kamiokande and DUNE experiments.

We present an updated deep neural network model for inclusive electron–carbon scattering. Using the bootstrap model [B. E. Kowal ., ], we incorporate recent experimental data, as well as older measurements in the deep inelastic scattering region, to derive a reoptimized 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 relevant to the Hyper-Kamiokande and DUNE experiments.

Transfer learning 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 transfer learning technique in physics. The DNN learns the details of one process, and after fine-tuning, it makes predictions for related processes. We consider the DNNs, trained on inclusive electron-carbon scattering data, and show that after fine-tuning, they accurately predict cross sections for electron interactions with nuclear targets ranging from helium-3 to iron.

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 neutrino interactions with argon. These studies require a reliable estimate of neutrino cross sections, in particular for charged current quasielastic scattering (CCQE). Here, we report updates of the uro Monte Carlo generator that, most notably, bring the state-of-the-art spectral functions to model the ground state properties of the argon nucleus, and improve the accuracy of the cross sections at low energies by accounting for the effects of the nuclear Coulomb potential. We discuss these developments in the context of electron and neutrino interactions, by comparing updated uro predictions to experimental data from Jefferson Laboratory Hall A and MicroBooNE. The MicroBooNE CCQE-dominated data are described with the χ2 per degree of freedom of 0.7, compared with 1.0 in the local Fermi gas model. The largest improvement is observed for the angular distributions of the produced protons, where the χ2 reduces nearly by half. Being obtained using the axial form factor parametrization from MINERvA, our results indicate a consistency between the CCQE measurements in MINERvA and MicroBooNE. Published by the American Physical Society 2024

Employing the neural network framework, we obtain empirical fits to the electron-scattering cross section for carbon over a broad kinematic region, extending from the quasielastic peak, through resonance excitation, to the onset of deep-inelastic scattering. We consider two different methods of obtaining such model-independent parametrizations and the corresponding uncertainties: based on the NNPDF approach [J. High Energy Phys. 2002, 062], and on the Monte Carlo dropout. In our analysis, the $\chi^2$ function defines the loss function, including point-to-point uncertainties and considering the systematic normalization uncertainties for each independent set of measurements. Our statistical approaches lead to fits of comparable quality and similar uncertainties of the order of $7\%$ and $12\%$ for the first and the second approaches, respectively. To test these models, we compare their predictions to a~test dataset, excluded from the training process, a~dataset lying beyond the covered kinematic region, and theoretical predictions obtained within the spectral function approach. The predictions of both models agree with experimental measurements and the theoretical predictions. However, the first statistical approach shows better interpolation and extrapolation abilities than the one based on the dropout algorithm.

The elastic and inelastic neutral current v (v over bar ) scattering off the polarized nucleon is discussed. The inelastic scattering concerns the single-pion production process. We show that the spin asymmetries' measurement can help to distinguish between neutrino and antineutrino neutral current scattering processes. The spin asymmetries also encode information about a type of target. Eventually, detailed studies of the inelastic spin asymmetries can improve understanding of the resonant-nonresonant pion production mechanism.

The work presents the studies of the polarization observables in the single pion production (SPP) induced by the interaction of the muon neutrino (antineutrino) with nucleons. The SPP cross-sections and spin asymmetries are computed within two phenomenological models. One is basing on the nonlinear sigma model \cite{Hernandez:2007qq}; the other has origin in linear sigma model~\cite{Fogli:1979cz}. Firstly, we show that the final nucleon polarization and target spin asymmetries are good observables to obtain information about the $C_5^A$ axial form factor. Secondly, we demonstrate that the nucleon polarization and the target spin asymmetries are sensitive to the relative phase between resonance and nonresonance amplitudes. We conclude that the polarization of the final nucleon and the target spin asymmetry are promising observables for testing SPP models, including studies of the axial content of $\Delta(1232)$ resonance and unitarization procedures.

The work concerns the quasielastic charged current neutrino-neutron and antineutrino-proton interactions. Single, double, and triple spin asymmetries are computed and analyzed. The spin asymmetries are sensitive to the axial form factor of the nucleon. In particular, the target-recoil double spin asymmetry and the lepton-target-recoil triple spin asymmetry depend strongly on the axial form factor of the nucleon. Indeed, the sign and shape of these components depend on the axial mass parameter. All the asymmetries, except the lepton polarization, are observables well suited to study the nonstandard interactions described by the second class current contribution.

The single pion production (SPP) in the charged-current neutrino (antineutrino) scattering off the polarized nucleon is discussed. The spin asymmetry is predicted within two approaches. The spin polarizations of the target nucleon that are longitudinal and perpendicular to the neutrino momentum are considered. It is shown, in several examples, that information about the SPP dynamics coming from the spin asymmetry is complementary to information obtained from measurements of spin averaged cross section. Indeed, the spin asymmetry is sensitive to the nonresonance background description of the SPP model. For the normal polarization of the target, the spin asymmetry is given by the interference between the resonance and the nonresonance contributions.

Our recent investigations of the spin asymmetry observables in the charged current inelastic and quasielastic neutrino (antineutrino)-nucleon scattering are reviewed. The spin asymmetry observables contain full information about the structure of the electroweak neutrino-nucleon vertex. Hence, they can be used to constrain the cross-section models for the singlepion production in nu-nucleon scattering and they allow to study the axial content of the nucleon and the second class current contribution to the quasielastic scattering amplitudes.

Polarization transfer (PT) observables in the single pion production induced by the charged current interaction of the neutrino with the nucleon are examined. The polarization components of the final nucleon and the charged lepton are calculated within two models for the pion production. The predictions are made for neutrino energy of the order of 1~GeV as well as for the T2K energy distribution. It is demonstrated that the PT observables, the degree of polarization and the polarization components of outgoing fermions, are sensitive on assumptions about the nonresonant background model. In particular it is shown that the normal components of the polarization of the outgoing nucleon and the lepton are determined by the interference between the resonant (RES) and nonresonant (NB) amplitudes. Moreover the sign of the normal component of the polarization of the charged lepton is fixed by the relative sign between the RES and the NB amplitudes.

This talk presents some of the results of the re-analysis of [Graczyk et al.] of the bubble chamber data for single pion production induced by neutrino scattering off deuteron. It is shown that ANL and BNL data are statistically consistent. The validity of the Adler relations (between $P_{33}(1232)$ resonance axial form factors) is also investigated.