Related papers: First Estimation of Model Parameters for Neutrino-…
We describe the implementation of a model for charged-current quasi-elastic (CCQE) neutrino-nucleus scattering in the NEUT Monte Carlo event generator. This model employs relativistic momentum distributions obtained from mean field theory…
Neutrino non-standard interactions (NSI) with the first generation of standard model fermions can span a parameter space of large dimension and exhibit degeneracies that cannot be broken by a single class of experiment. Oscillation…
We describe how the main neutrino interaction generators (GENIE, NEUT and NuWro) used by current neutrino oscillation experiments treat the shallow and deep inelastic region. We then compare their predictions for charged current events in…
Distributions of transverse kinematic imbalance in neutrino-nucleus interactions in the few GeV regime are sensitive to nuclear effects. We present a study comparing the latest predictions of transverse kinematic imbalance from the…
Current and future neutrino oscillation experiments utilize information of hadronic final states to improve sensitivities on oscillation parameters measurements. Among the physics of hadronic systems in neutrino interactions, the…
Neutrino-nucleus cross section measurements are needed to improve interaction modeling to meet the precision needs of neutrino experiments in efforts to measure oscillation parameters and search for physics beyond the Standard Model. We…
The deformation of heavy nuclei leaves characteristic imprints on the initial conditions of relativistic heavy-ion collisions. However, event-by-event fluctuations make the quantitative extraction of this information challenging. This study…
Simulation-based inference (SBI) is emerging as a new statistical paradigm for addressing complex scientific inference problems. By leveraging the representational power of deep neural networks, SBI can extract the most informative…
Accelerator-based neutrino oscillation experiments have the potential to revolutionise our understanding of fundamental physics, offering an opportunity to characterise charge-parity violation in the lepton sector; to determine the neutrino…
The recently observed event excess in MiniBooNE anti-neutrino data is in agreement with the LSND evidence for electron anti-neutrino appearance. We propose an explanation of these data in terms of a (3+1) scheme with a sterile neutrino…
A thorough understanding of neutrino-nucleus interactions physics is crucial to achieving precision goals in broader neutrino physics programs. The complexity of nuclei comprising the detectors and limited understanding of their weak…
We first present the implementation and validation of the SuSAv2-MEC 1p1h and 2p2h models in the GENIE neutrino-nucleus interaction event generator and a comparison of the subsequent predictions to measurements of lepton and hadron…
Simulation-based inference (SBI) offers a flexible and general approach to performing Bayesian inference: In SBI, a neural network is trained on synthetic data simulated from a model and used to rapidly infer posterior distributions for…
Non-Standard Interactions (NSI) of neutrinos with matter has received renewed interest in recent years. In particular, it has been shown that NSI can reconcile the $3+1$ solution with IceCube atmospheric data with $E_\nu >500$ GeV, provided…
Background: Long-baseline experiments such as the planned Deep Underground Neutrino Experiment (DUNE) require theoretical descriptions of the complete event in a neutrino-nucleus reaction. Since nuclear targets are used this requires a good…
Understanding the quark and gluon substructure of the nucleon has been a prime goal of both nuclear and particle physics for more than thirty years and has led to much of the progress in strong interaction physics. Still the flavor…
The NOvA experiment is an off-axis long-baseline neutrino oscillation experiment measuring $\nu_{\mu}$ disappearance and $\nu_{e}$ appearance in a $\nu_{\mu}$ beam originating at Fermilab. Oscillations are observed in the far detector…
The application of Bayesian Neural Networks(BNN) to discriminate neutrino events from backgrounds in reactor neutrino experiments has been described in Ref.\cite{key-1}. In the paper, BNN are also used to identify neutrino events in reactor…
We present a deep learning-based method for estimating the neutrino energy of charged-current neutrino-argon interactions. We employ a recurrent neural network (RNN) architecture for neutrino energy estimation in the MicroBooNE experiment,…
In this work, we investigate the implications of a novel non-standard interaction (NSI) of neutrinos. This interaction is geometric in origin -- it arises because the propagation of fermions in curved spacetime induces torsion. This torsion…