Analysis of γ-ray production in neutral-current neutrino-oxygen interactions at energies above 200 MeV

Diffuse neutrino background from past core collapse supernovae

Core collapse supernovae are among the most powerful explosions in the Universe, which emit thermal neutrinos that carry away most of the gravitational binding energy released. These neutrinos produce a diffuse supernova neutrino background (DSNB), which is one of the largest energy budgets among all radiation backgrounds. Detecting the DSNB is an important goal of modern high-energy astrophysics and particle physics, which provides valuable insights into core collapse modeling, neutrino physics, and cosmic supernova rate history. In this review, the key ingredients of DSNB calculation and what can be learned from future detections, including black hole formation and non-standard neutrino interactions are discussed. Moreover, an overview of the latest updates in neutrino experiments, which could lead to the detection of the DSNB in the next decade, is provided. With the promise of this breakthrough discovery on the horizon, the study of DSNB has great potential to further our understanding of the Universe.

Using machine learning to improve neutron identification in water Cherenkov detectors

Water Cherenkov detectors like Super-Kamiokande, and the next generation Hyper-Kamiokande are adding gadolinium to their water to improve the detection of neutrons. By detecting neutrons in addition to the leptons in neutrino interactions, an improved separation between neutrino and anti-neutrinos, and reduced backgrounds for proton decay searches can be expected. The neutron signal itself is still small and can be confused with muon spallation and other background sources. In this paper, machine learning techniques are employed to optimize the neutron capture detection capability in the new intermediate water Cherenkov detector (IWCD) for Hyper-K. In particular, boosted decision tree (XGBoost), graph convolutional network (GCN), and dynamic graph convolutional neural network (DGCNN) models are developed and benchmarked against a statistical likelihood-based approach, achieving up to a 10% increase in classification accuracy. Characteristic features are also engineered from the datasets and analyzed using SHAP (SHapley Additive exPlanations) to provide insight into the pivotal factors influencing event type outcomes. The dataset used in this research consisted of roughly 1.6 million simulated particle gun events, divided nearly evenly between neutron capture and a background electron source. The current samples used for training are representative only, and more realistic samples will need to be made for the analyses of real data. The current class split is 50/50, but there is expected to be a difference between the classes in the real experiment, and one might consider using resampling techniques to address the issue of serious imbalances in the class distribution in real data if necessary.

Significant Excess of Electronlike Events in the MiniBooNE Short-Baseline Neutrino Experiment

The MiniBooNE experiment at Fermilab reports results from an analysis of ν_{e} appearance data from 12.84×10^{20} protons on target in neutrino mode, an increase of approximately a factor of 2 over previously reported results. A ν_{e} charged-current quasielastic event excess of 381.2±85.2 events (4.5σ) is observed in the energy range 200<E_{ν}^{QE}<1250  MeV. Combining these data with the ν[over ¯]_{e} appearance data from 11.27×10^{20} protons on target in antineutrino mode, a total ν_{e} plus ν[over ¯]_{e} charged-current quasielastic event excess of 460.5±99.0 events (4.7σ) is observed. If interpreted in a two-neutrino oscillation model, ν_{μ}→ν_{e}, the best oscillation fit to the excess has a probability of 21.1%, while the background-only fit has a χ^{2} probability of 6×10^{-7} relative to the best fit. The MiniBooNE data are consistent in energy and magnitude with the excess of events reported by the Liquid Scintillator Neutrino Detector (LSND), and the significance of the combined LSND and MiniBooNE excesses is 6.0σ. A two-neutrino oscillation interpretation of the data would require at least four neutrino types and indicate physics beyond the three neutrino paradigm. Although the data are fit with a two-neutrino oscillation model, other models may provide better fits to the data.

Towards a unified model of neutrino-nucleus reactions for neutrino oscillation experiments

A precise description of neutrino-nucleus reactions will play a key role in addressing fundamental questions such as the leptonic CP violation and the neutrino mass hierarchy through analyzing data from next-generation neutrino oscillation experiments. The neutrino energy relevant to the neutrino-nucleus reactions spans a broad range and, accordingly, the dominant reaction mechanism varies across the energy region from quasi-elastic scattering through nucleon resonance excitations to deep inelastic scattering. This corresponds to transitions of the effective degree of freedom for theoretical description from nucleons through meson-baryon to quarks. The main purpose of this review is to report our recent efforts towards a unified description of the neutrino-nucleus reactions over the wide energy range; recent overall progress in the field is also sketched. Starting with an overview of the current status of neutrino-nucleus scattering experiments, we formulate the cross section to be commonly used for the reactions over all the energy regions. A description of the neutrino-nucleon reactions follows and, in particular, a dynamical coupled-channels model for meson productions in and beyond the [Formula: see text](1232) region is discussed in detail. We then discuss the neutrino-nucleus reactions, putting emphasis on our theoretical approaches. We start the discussion with electroweak processes in few-nucleon systems studied with the correlated Gaussian method. Then we describe quasi-elastic scattering with nuclear spectral functions, and meson productions with a [Formula: see text]-hole model. Nuclear modifications of the parton distribution functions determined through a global analysis are also discussed. Finally, we discuss issues to be addressed for future developments.