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.

We provide an overview of the status of Monte-Carlo event generators for high-energy particle physics. Guided by the experimental needs and requirements, we highlight areas of active development, and opportunities for future improvements. Particular emphasis is given to physics models and algorithms that are employed across a variety of experiments. These common themes in event generator development lead to a more comprehensive understanding of physics at the highest energies and intensities, and allow models to be tested against a wealth of data that have been accumulated over the past decades. A cohesive approach to event generator development will allow these models to be further improved and systematic uncertainties to be reduced, directly contributing to future experimental success. Event generators are part of a much larger ecosystem of computational tools. They typically involve a number of unknown model parameters that must be tuned to experimental data, while maintaining the integrity of the underlying physics models. Making both these data, and the analyses with which they have been obtained accessible to future users is an essential aspect of open science and data preservation. It ensures the consistency of physics models across a variety of experiments.

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.

Neutrino-induced single-pion production constitutes an essential interaction channel in modern neutrino oscillation experiments, with its products building up a significant fraction of the observable hadronic final states. Frameworks of oscillation analyses strongly rely on Monte Carlo neutrino event generators, which provide theoretical predictions of neutrino interactions on nuclear targets. Thus, it is crucial to integrate state-of-the-art single-pion production models with Monte Carlo simulations to prepare for the upcoming systematics-dominated landscape of neutrino measurements. In this work, we present the implementation of the Ghent Hybrid model for neutrino-induced single-pion production in the NuWro Monte Carlo event generator. The interaction dynamics includes coherently-added contributions from nucleon resonances and a non-resonant background, merged into the pythia branching predictions in the deep-inelastic regime, as instrumented by NuWro. This neutrino-nucleon interaction model is fully incorporated into the nuclear framework of the generator, allowing it to account for the influence of both initial- and final-state nuclear medium effects. We compare the predictions of this integrated implementation with recent pion production data from accelerator-based neutrino experiments. The results of the novel model show improved agreement of the generator predictions with the data and point to the significance of the refined treatment of the description of pion-production processes beyond the $\Delta$ region.

Muon neutrino and antineutrino disappearance probabilities are identical in the standard three-flavor neutrino oscillation framework, but CPT violation and non-standard interactions can violate this symmetry. In this work we report the measurements of $\sin^{2} \theta_{23}$ and $\Delta m_{32}^2$ independently for neutrinos and antineutrinos. The aforementioned symmetry violation would manifest as an inconsistency in the neutrino and antineutrino oscillation parameters. The analysis discussed here uses a total of 1.97$\times$10$^{21}$ and 1.63$\times$10$^{21}$ protons on target taken with a neutrino and antineutrino beam respectively, and benefits from improved flux and cross-section models, new near detector samples and more than double the data reducing the overall uncertainty of the result. No significant deviation is observed, consistent with the standard neutrino oscillation picture.

The T2K experiment presents new measurements of neutrino oscillation parameters using 19.7(16.3)× 10^20 protons on target (POT) in (anti-)neutrino mode at the far detector (FD). Compared to the previous analysis, an additional 4.7× 10^20 POT neutrino data was collected at the FD. Significant improvements were made to the analysis methodology, with the near-detector analysis introducing new selections and using more than double the data. Additionally, this is the first T2K oscillation analysis to use NA61/SHINE data on a replica of the T2K target to tune the neutrino flux model, and the neutrino interaction model was improved to include new nuclear effects and calculations. Frequentist and Bayesian analyses are presented, including results on sin ^2θ _13 and the impact of priors on the δ _CP measurement. Both analyses prefer the normal mass ordering and upper octant of sin ^2θ _23 with a nearly maximally CP-violating phase. Assuming the normal ordering and using the constraint on sin ^2θ _13 from reactors, sin ^2θ _23=0.561^+0.021_-0.032 using Feldman–Cousins corrected intervals, and m^2_32=2.494_-0.058^+0.041× 10^-3 eV^2 using constant χ ^2 intervals. The CP-violating phase is constrained to δ _CP=-1.97_-0.70^+0.97 using Feldman–Cousins corrected intervals, and δ _CP=0,π is excluded at more than 90 2σ credible level using a flat prior in δ _CP, and just below 2σ using a flat prior in sinδ _CP. When the external constraint on sin ^2θ _13 is removed, sin ^2θ _13=28.0^+2.8_-6.5× 10^-3, in agreement with measurements from reactor experiments. These results are consistent with previous T2K analyses.

The T2K experiment presents new measurements of neutrino oscillation parameters using 19.7(16.3)×1020 protons on target (POT) in (anti-)neutrino mode at the far detector (FD). Compared to the previous analysis, an additional 4.7×1020 POT neutrino data was collected at the FD. Significant improvements were made to the analysis methodology, with the near-detector analysis introducing new selections and using more than double the data. Additionally, this is the first T2K oscillation analysis to use NA61/SHINE data on a replica of the T2K target to tune the neutrino flux model, and the neutrino interaction model was improved to include new nuclear effects and calculations. Frequentist and Bayesian analyses are presented, including results on sin2θ13 and the impact of priors on the δCP measurement. Both analyses prefer the normal mass ordering and upper octant of sin2θ23 with a nearly maximally CP-violating phase. Assuming the normal ordering and using the constraint on sin2θ13 from reactors, sin2θ23=0.561-0.032+0.021 using Feldman-Cousins corrected intervals, and Δm322=2.494-0.058+0.041×10-3eV2 using constant Δχ2 intervals. The CP-violating phase is constrained to δCP=-1.97-0.70+0.97 using Feldman-Cousins corrected intervals, and δCP=0,π is excluded at more than 90% confidence level. A Jarlskog invariant of zero is excluded at more than 2σ credible level using a flat prior in δCP, and just below 2σ using a flat prior in sinδCP. When the external constraint on sin2θ13 is removed, sin2θ13=28.0-6.5+2.8×10-3, in agreement with measurements from reactor experiments. These results are consistent with previous T2K analyses.

We report an updated measurement of the nu(mu)-induced, and the first measurement of the (nu) over bar (mu)- induced coherent charged pion production cross section on C-12 nuclei in the Tokai-to-Kamioka experiment. This is measured in a restricted region of the final- state phase space for which p(mu,pi) > 0.2 GeV, cos(theta(mu)) > 0.8 and cos(theta(pi)) > 0.6, and at a mean ( anti)neutrino energy of 0.85 GeVusing the T2K near detector. The measured nu(mu) charged current coherent pion production flux-averaged cross section on C-12 is (2.98 +/- 0.37(stat) +/- 0.31(syst)(-0.00)(+0.49)(Q(2) model)) x 10(-40) cm(2). The new measurement of the (nu) over bar (mu)-induced cross section on C-12 is (3.05 +/- 0.71(stat) +/- 0.39(syst)(-0.00)(+-0.74) (Q(2) model)) x 10(-40) cm(2). The results are compatible with both the NEUT 5.4.0 Berger-Sehgal (2009) and GENIE 2.8.0 Rein-Sehgal (2007) model predictions.