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.

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.

Maximizing the discovery potential of increasingly precise neutrino experiments will require an improved theoretical understanding of neutrino-nucleus cross sections over a wide range of energies. Low-energy interactions are needed to reconstruct the energies of astrophysical neutrinos from supernovae bursts and search for new physics using increasingly precise measurement of coherent elastic neutrino scattering. Higher-energy interactions involve a variety of reaction mechanisms including quasi-elastic scattering, resonance production, and deep inelastic scattering that must all be included to reliably predict cross sections for energies relevant to DUNE and other accelerator neutrino experiments. This white paper discusses the theoretical status, challenges, required resources, and path forward for achieving precise predictions of neutrino-nucleus scattering and emphasizes the need for a coordinated theoretical effort involved lattice QCD, nuclear effective theories, phenomenological models of the transition region, and event generators.

We have performed a global extraction of the ${\rm ^{12}C}$ longitudinal (${\cal R}_L$) and transverse (${\cal R}_T$) nuclear electromagnetic response functions from an analysis of all available electron scattering data on carbon. The response functions are extracted for energy transfer $\nu$, spanning the nuclear excitation, quasielastic (QE), resonance and inelastic continuum over a large range of the square of the four-momentum transfer, $Q^2.$ In addition, we perform a universal fit to all ${\rm ^{12}C}$ electron scattering data which also provides parmeterizations of ${\cal R}_L$ and ${\cal R}_T$ over a larger kinematic range. Given the nuclear physics common to both electron and neutrino scattering from nuclei, extracted response functions from electron scattering spanning a large range of $Q^2$ and $\nu$ also provide a powerful tool for validation and tuning of neutrino Monte Carlo (MC) generators. In this paper we focus on the nuclear excitation, single nucleon (QE-1p1h) and two nucleon (2p2h) final state regions and compare the measurements to theoretical predictions including ``Energy Dependent-Relativistic Mean Field'' (ED-RMF), ``Green's Function Monte Carlo'' (GFMC), "Short Time Approximation Quantum Monte Carlo" (STA-QMC), an improved superscaling model (SuSAv2), "Correlated Fermi Gas" (CFG), as well as the \nuwro{}, and \achilles~ generators. Combining the ED-RMF-QE-1p1h predictions with the SuSAv2-MEC-2p2h predictions provides a good description of ${\cal R}_L$ and ${\cal R}_T$ for both single nucleon (from QE and nuclear excitations) and two nucleon final states over the entire kinematic range.

The determination of the direction of a stellar core collapse via its neutrino emission is crucial for the identification of the progenitor for a multimessenger follow-up. A highly effective method of reconstructing supernova directions within the Deep Underground Neutrino Experiment (DUNE) is introduced. The supernova neutrino pointing resolution is studied by simulating and reconstructing electron-neutrino charged-current absorption on Ar-40 and elastic scattering of neutrinos on electrons. Procedures to reconstruct individual interactions, including a newly developed technique called "brems flipping," as well as the burst direction from an ensemble of interactions are described. Performance of the burst direction reconstruction is evaluated for supernovae happening at a distance of 10 kpc for a specific supernova burst flux model. The pointing resolution is found to be 3.4 degrees at 68% coverage for a perfect interaction-channel classification and a fiducial mass of 40 kton, and 6.6 degrees for a 10 kton fiducial mass respectively. Assuming a 4% rate of charged-current interactions being misidentified as elastic scattering, DUNE's burst pointing resolution is found to be 4.3 degrees (8.7 degrees) at 68% coverage.

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.

The Module-0 Demonstrator is a single-phase 600 kg liquid argon time projection chamber operated as a prototype for the DUNE liquid argon near detector. Based on the ArgonCube design concept, Module-0 features a novel 80k-channel pixelated charge readout and advanced high-coverage photon detection system. In this paper, we present an analysis of an eight-day data set consisting of 25 million cosmic ray events collected in the spring of 2021. We use this sample to demonstrate the imaging performance of the charge and light readout systems as well as the signal correlations between the two. We also report argon purity and detector uniformity measurements, and provide comparisons to detector simulations.

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 present the findings of a study based on a new inelastic electron-scattering experiment on the 12C nucleus focusing on the kinematic region of Q2=0.8 GeV2/c2. The measured cross section is sensitive to the transverse response function and provides a stringent test of theoretical models, as well as of the theoretical assumptions made in Monte-Carlo event-generator codes developed for the interpretation of neutrino-nucleus experiments, such as DUNE and HyperK. We find that modern generators such as GENIE and GiBUU reproduce our new experimental data within 10%. PAC Codes: 12.20.-m , 25.30.Bf , 41.60.-m

ProtoDUNE Single-Phase (ProtoDUNE-SP) is a 770-ton liquid argon time projection chamber that operated in a hadron test beam at the CERN Neutrino Platform in 2018. We present a measurement of the total inelastic cross section of charged kaons on argon as a function of kaon energy using 6 and 7 GeV/$c$ beam momentum settings. The flux-weighted average of the extracted inelastic cross section at each beam momentum setting was measured to be 380$\pm$26 mbarns for the 6 GeV/$c$ setting and 379$\pm$35 mbarns for the 7 GeV/$c$ setting.

We have performed a global extraction of the ^12C longitudinal (R_L) and transverse (R_T) nuclear electromagnetic response functions from an analysis of all available electron scattering data on carbon. The response functions are extracted for energy transfer ν, spanning the nuclear excitation, quasielastic (QE), resonance and inelastic continuum over a large range of the square of the four-momentum transfer, Q^2. In addition, we perform a universal fit to all ^12C electron scattering data which also provides parmeterizations of R_L and R_T over a larger kinematic range. Given the nuclear physics common to both electron and neutrino scattering from nuclei, extracted response functions from electron scattering spanning a large range of Q^2 and ν also provide a powerful tool for validation and tuning of neutrino Monte Carlo (MC) generators. In this paper we focus on the nuclear excitation, single nucleon (QE-1p1h) and two nucleon (2p2h) final state regions and compare the measurements to theoretical predictions including “Energy Dependent-Relativistic Mean Field” (ED-RMF), “Green's Function Monte Carlo” (GFMC), "Short Time Approximation Quantum Monte Carlo" (STA-QMC), an improved superscaling model (SuSAv2), "Correlated Fermi Gas" (CFG), as well as the , and generators. Combining the ED-RMF-QE-1p1h predictions with the SuSAv2-MEC-2p2h predictions provides a good description of R_L and R_T for both single nucleon (from QE and nuclear excitations) and two nucleon final states over the entire kinematic range.

We present the findings of a study based on a new inelastic electron-scattering experiment on the ^12C nucleus focusing on the kinematic region of Q^2=0.8 GeV^2/c^2. The measured cross section is sensitive to the transverse response function and provides a stringent test of theoretical models, as well as of the theoretical assumptions made in Monte-Carlo event-generator codes developed for the interpretation of neutrino-nucleus experiments, such as DUNE and HyperK. We find that modern generators such as GENIE and GiBUU reproduce our new experimental data within 10%.

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 international collaboration designing and constructing the Deep Underground Neutrino Experiment (DUNE) at the Long-Baseline Neutrino Facility (LBNF) has developed a two-phase strategy toward the implementation of this leading-edge, large-scale science project. The 2023 report of the US Particle Physics Project Prioritization Panel (P5) reaffirmed this vision and strongly endorsed DUNE Phase I and Phase II, as did the European Strategy for Particle Physics. While the construction of the DUNE Phase I is well underway, this White Paper focuses on DUNE Phase II planning. DUNE Phase-II consists of a third and fourth far detector (FD) module, an upgraded near detector complex, and an enhanced 2.1 MW beam. The fourth FD module is conceived as a "Module of Opportunity", aimed at expanding the physics opportunities, in addition to supporting the core DUNE science program, with more advanced technologies. This document highlights the increased science opportunities offered by the DUNE Phase II near and far detectors, including long-baseline neutrino oscillation physics, neutrino astrophysics, and physics beyond the standard model. It describes the DUNE Phase II near and far detector technologies and detector design concepts that are currently under consideration. A summary of key R&D goals and prototyping phases needed to realize the Phase II detector technical designs is also provided. DUNE's Phase II detectors, along with the increased beam power, will complete the full scope of DUNE, enabling a multi-decadal program of groundbreaking science with neutrinos.