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At least 127 records · Page 7

Improving the NOvA 3-Flavour Neutrino Oscillation Analysis

NOvA is a long-baseline neutrino experiment studying neutrino oscillations, a quantum mechanical interference phenomena where the observed neutrino flavour differs from that measured earlier, stemming from neutrino mass and flavour states mixing. NOvA consists of two functionally identical tracking calorimeter detectors deployed in the Fermilab NuMI beam. Both detectors are placed 14.6 mrad off the beam axis to achieve a narrow energy peak at 1.8 GeV at an oscillation maximum. The NOvA 3-flavour oscillation analysis measures the neutrino oscillation parameters sin2θ23 and ∆m232 as well as sets limit to δCP , the octant of θ23 and the sign of ∆m32. The event selection for 3-flavour neutrino oscillation analysis ensures the maximum quantity of signal is made available and a minimal amount of background is present. The current disappearance analysis selection has an efficiency of 80% for selecting νμ CC events, and with some improvements additional events could be recovered into the analysis to improve the sensitivity to the aforementioned oscillation parameters. In an effort to recover the currently rejected signal event to the analysis, these events were trained in a classification neural network. The aim of the network was to divide the data into signal (νμ CC events) and background (NC and νe CC events). The highest performing network gave an additional figure of merit gain of 2.34 increasing the sensitivity by 4.3% in effective POT equal to 33 days of additional data taking. This was compared to changing the current particle identification event selection cuts, the best result out of the tested cut combinations gave an additional FOM of 3.36 equal to 3.7% increase in effective POT equivalent to 28 days of data taking.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

Measurement of exclusive 𝜋 + -argon interactions using ProtoDUNE-SP

We present the measurement of 𝜋 + -argon inelastic cross sections using the ProtoDUNE single-phase liquid argon time projection chamber in the incident 𝜋 + kinetic energy range of 500–800 MeV in multiple exclusive channels (absorption, charge exchange, and the remaining inelastic interactions). The results of this analysis are important inputs to simulations of liquid argon neutrino experiments such as the Deep Underground Neutrino Experiment and the Short Baseline Neutrino program at Fermi National Accelerator Laboratory. They will be employed to improve the modeling of final state interactions within neutrino event generators used by these experiments, as well as the modeling of 𝜋 + -argon secondary interactions within the liquid argon. This is the first measurement of 𝜋 + -argon absorption at this kinetic energy range as well as the first ever measurement of 𝜋 + -argon charge exchange.

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Toward Muon Neutrino Charged-Current Neutral Pion Cross Section Measurements at ICARUS

At ICARUS and the broader Short-Baseline Neutrino Program, few neutrino interaction channels offer as much utility as those producing neutral pions, which are an important background to electron neutrino searches and provide a standard candle for calibrating the electromagnetic shower energy scale. Beyond this, measurements of neutrino-induced neutral pions offer a probe of resonant interactions that are relevant to future accelerator neutrino experiments like the Deep Underground Neutrino Experiment. In this document, a measurement of charged-current muon neutrino interactions in liquid argon with a single neutral pion in the final state is presented, making use of neutrino interaction events recorded with the ICARUS T600 detector at Fermilab while exposed to the Booster Neutrino Beam. Event selection is carried out with a fully-automated machine learning reconstruction framework, allowing for the extraction of single-differential cross section measurements as a function of muon and neutral pion kinematic observables.

Carber, Dan [Colorado State U.]↗

Helium diffusion in the sun

We calculate improved standard solar models using the new Livermore (OPAL) opacity tables, an accurate (exportable) nuclear energy generation routine which takes account of recent measurements and analyses, and the recent Anders-Grevesse determination of heavy element abundances. We also evaluate directly the effect of the diffusion of helium with respect to hydrogen on the calculated neutrino fluxes, on the primordial solar helium abundance, and on the depth of the convective zone. Helium diffusion increases the predicted event rates by about 0.8 SNU, or 11 percent of the total rate, in the chlorine solar neutrino experiment, by about 3.5 SNU, or 3 percent, in the gallium solar neutrino experiments, and by about 12 percent in the Kamiokande and SNO solar neutrino experiments. The best standard solar model including helium diffusion and the most accurate nuclear parameters, element abundances, and radiative opacity predicts a value of 8.0 SNU +/- 3.0 SNU for the C1-37 experiment and 132 +21/-17 SNU for the Ga - 71 experiment, where the uncertainties include 3 sigma errors for all measured input parameters.

Bahcall, J. N.↗

Electron scattering and neutrino physics

Abstract A thorough understanding of neutrino–nucleus scattering physics is crucial for the successful execution of the entire US neutrino physics program. Neutrino–nucleus interaction constitutes one of the biggest systematic uncertainties in neutrino experiments—both at intermediate energies affecting long-baseline deep underground neutrino experiment, as well as at low energies affecting coherent scattering neutrino program—and could well be the difference between achieving or missing discovery level precision. To this end, electron–nucleus scattering experiments provide vital information to test, assess and validate different nuclear models and event generators intended to test, assess and validate different nuclear models and event generators intended to be used in neutrino experiments. Similarly, for the low-energy neutrino program revolving around the coherent elastic neutrino–nucleus scattering (CEvNS) physics at stopped pion sources, such as at ORNL, the main source of uncertainty in the evaluation of the CEvNS cross section is driven by the underlying nuclear structure, embedded in the weak form factor, of the target nucleus. To this end, parity-violating electron scattering (PVES) experiments, utilizing polarized electron beams, provide vital model-independent information in determining weak form factors. This information is vital in achieving a percent level precision needed to disentangle new physics signals from the standard model expected CEvNS rate. In this white paper, we highlight connections between electron- and neutrino–nucleus scattering physics at energies ranging from 10 s of MeV to a few GeV, review the status of ongoing and planned electron scattering experiments, identify gaps, and lay out a path forward that benefits the neutrino community. We also highlight the systemic challenges with respect to the divide between the nuclear and high-energy physics communities and funding that presents additional hurdles in mobilizing these connections to the benefit of neutrino programs.

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Machine Learning-Based Anomaly Detection for PMT Data Quality Monitoring in the SBN and DUNE

Maintaining high-quality detector data is essential for achieving the scientific objectives of the Short-Baseline Neutrino (SBN) Program at Fermilab. Current data quality monitoring (DQM) procedures rely primarily on threshold-based metrics and manual inspection of detector monitoring plots, making the detection of subtle or gradually developing anomalies both time-consuming and dependent on expert interpretation. This project developed and evaluated a machine-learning workflow for automatically identifying anomalous photomultiplier tube (PMT) channels in the Short-Baseline Near Detector (SBND) using optical-hit amplitude data. A Python-based analysis program was developed to process ROOT files, extract statistical features describing individual PMT amplitude distributions, and generate feature vectors for anomaly detection. These features were used to train an Isolation Forest model using data representing normal detector operation. The trained model was subsequently applied to independent detector runs to identify channels exhibiting statistically unusual behavior relative to the learned reference response. To support expert interpretation, the workflow generated complementary diagnostic products, including anomaly score distributions, normalized amplitude comparisons, decision-tree visualizations, and principal component analysis (PCA) projections. This project demonstrated the feasibility of integrating unsupervised machine learning into detector data-quality monitoring and developed a complete workflow for automated PMT performance assessment to aid expert-driven review. Beyond its technical contributions, the VFP appointment fostered a research collaboration between Aurora University and Fermilab and provided direct workforce development benefits by training the visiting faculty member in detector-scale machine-learning methods that are now being incorporated into undergraduate coursework and research. The methodology developed here provides a foundation for future applications to ProtoDUNE and other liquid argon time projection chamber (LArTPC) detectors, contributing to ongoing efforts to improve detector reliability, reduce manual monitoring requirements, and enable scalable data quality monitoring for future large-scale neutrino experiments, including the Deep Underground Neutrino Experiment (DUNE).

Colón Santana, Juan A. [Unlisted, US, IL]↗

Region of Interest Filter in DUNE DAQ

The Deep Underground Neutrino Experiment (DUNE) is the next generation neutrino experiment currently under construction. It consists of a broadband neutrino beam at Fermilab, a high precision near detector, and the largest liquid argon time projection chamber far detector ever designed at the Sanford Underground Research Facility (SURF). The Region of Interest (ROI) filter is designed for DUNE’s online Data Acquisition (DAQ) system to address data rate constraints and enable low energy physics in the <10 MeV range. The filter employs zero suppression on the detector signal, and by tuning the readout window and threshold the data rates can be reduced by >90%. Performance of the ROI is analyzed in LArsoft on MARLEY generated low energy MC events propagated through the detector simulation. Notably, the optimized ROI filter enables a lower trigger threshold for readout at ~5-10 MeV, allowing DUNE to explore low-energy physics, specifically focusing on solar boron 8 neutrinos which are relevant in this energy range. This advancement enhances DUNE's scientific capabilities, opening avenues for detailed analyses of previously inaccessible low-energy neutrino interactions.

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The DUNE Near Detector

The Deep Underground Neutrino Experiment (DUNE) is an upcoming long-baseline neutrino experiment which will study neutrino oscillations. Neutrino oscillations will be detected at the DUNE Far Detector 1300 km away from the start of the beam at Fermilab. The DUNE Near Detector (ND) will be located on-site at Fermilab, and will be used to provide an initial characterization of the neutrino beam, as well as to constrain systematic uncertainties on neutrino oscillation measurements. The detector suite consists of a modular LArTPC (ND-LAr), a magnetized gaseous argon time projection chamber (ND-GAr) surrounded by an electromagnetic calorimeter, and the System for on-Axis Neutrino Detection (SAND), composed of a magnetized electromagnetic calorimeter and inner tracker. In these proceedings, these detectors and their physics goals will be discussed.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

Energy Dependence in the Neutrino Scattering Data of MINERvA

As we prepare for lower neutrino energy beams where physics is highly energy-dependent, it is important to isolate factors that can contribute largely to these low energy neutrino experiments. The world of neutrino oscillation experiments uses a wide range of energies: from 0.7 GeV beam of T2K and MicroBooNE, 2 GeV beam of NOvA, to the 0.6 to 6.0 GeV beam produced by DUNE in the future. MINERvA’s data are currently the best place to test the upper end of the range for DUNE and can be extrapolated into both DUNE’s and NOvA’s oscillation maximum. The goal of the research is to constrain energy dependence in the neutrino scattering experiment using data from MINERvA and then determine what factors contribute to the observed energy dependence. The research is divided into two parts. First, I will analyze different theoretical models in different channels of interaction, such as the quasi-elastic and delta resonance channels, in terms of neutrino energy dependence. The goal is to study the models at the level of the structure functions, which emphasizes the W2 structure function (the C function for QE) dominates the MINERvA data, but the W3 is increasingly important for NOvA and DUNE. The second half of the research is data-driven and focuses on the different detectors and systematic effects. An experimental effect, the angle acceptance, is larger than the structure function effects. However, it is a detector geometry effect and is well measured and well modeled. Other effects such as the muon energy scale are small and localized. Additionally, the research will determine if the MINERvA GENIE model correctly predicts all of the energy dependence seen in the data, and identify the remaining unmodeled energy dependence between the MINERvA data and its best simulation.

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Status and Plan of DUNE at Fermilab

The discovery and the solid confirmation of neutrino flavor oscillation have been made throughout late 1990 to 2000s. Such oscillation is the clear proof of non-zero neutrino mass since this is caused by the fact that the neutrino mass eigenstates differ from that of the flavor. The neutrinos in the Standard Model, however, are massless leptons, and thus the neutrino flavor oscillation necessitates either the Standard Model to be significantly modified or a new theoretical paradigm to describe the universe better. In order to provide experimental information, it is essential for the properties of the neutrino oscillation be well understood through precision measurements of oscillation parameters, including the mixing angles, determination of the mass hierarchy between the three mass eigenstates and determination of the CP phase (Delta_CP) between neutrino and anti-neutrino oscillations. Among the several next generation neutrino experiments, this presentation covers the Deep Underground Neutrino Experiment (DUNE) which, with the high intensity neutrino beam facility, will help accomplish these goals. In this report, I will discuss the current status and plan of the DUNE experiment, including the results of the detector performance studies from the large scale prototype detectors operating at CERN and the timeline for construction and data taking of the experiment, as well as its physics prospects.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

Measurement of the Medium Energy NuMI Flux Using the Low-$\nu$ and High-$\nu$ Methods at MINERvA

As we continue to improve our experimental capabilities, the measurements we make of accelerator-based neutrino phenomena are becoming increasingly limited by systematic, rather than statistical uncertainties. Among these are uncertainties corresponding to our understanding of the neutrino flux and to the models that we use to simulate neutrino interactions. These already are significant for currently-running neutrino experiments, and will become more important for next-generation neutrino experiments such as DUNE. Dedicated efforts at experiments such as MINERvA are pursuing improved comprehensive modeling of neutrino cross sections and improving our understanding of how to precisely measure neutrino fluxes. This thesis discusses these themes in the context of MINERvA, a neutrino scattering experiment which was located on-axis in the NuMI beamline at Fermilab. A pair of \textit{in situ} measurements of the flux are presented which utilize Medium Energy $\nu_{\mu}$ data colle cted at MINERvA between 2013 and 2017. The low-$\nu$ technique is used to make a direct measurement of the flux from low-inelasticity events, and that flux is used to measure the total charged-current inclusive cross section. The novel ``high-$\nu$'' technique is used to make a second direct measurement of the flux from a complementary selection of high-inelasticity events, and that flux is used to measure a double-differential cross section of charged-current quasielastic-like events.

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Computational techniques for the analysis of small signals in high-statistics neutrino oscillation experiments

The current and upcoming generation of Very Large Volume Neutrino Telescopes – collecting unprecedented quantities of neutrino events – can be used to explore subtle effects in oscillation physics, such as (but not restricted to) the neutrino mass ordering. The sensitivity of an experiment to these effects can be estimated from Monte Carlo simulations. With the high number of events that will be collected, there is a trade-off between the computational expense of running such simulations and the inherent statistical uncertainty in the determined values. In such a scenario, it becomes impractical to produce and use adequately-sized sets of simulated events with traditional methods, such as Monte Carlo weighting. In this work we present a staged approach to the generation of expected distributions of observables in order to overcome these challenges. By combining multiple integration and smoothing techniques which address limited statistics from simulation it arrives at reliable analysis results using modest computational resources.

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Deployment of Water-based Liquid Scintillator in the Accelerator Neutrino Neutron Interaction Experiment

The Accelerator Neutrino Neutron Interaction Experiment (ANNIE) is a 26-ton water Cherenkov neutrino detector installed on the Booster Neutrino Beam (BNB) at Fermilab. Its main physics goals are to perform a measurement of the neutron yield from neutrino-nucleus interactions, as well as a measurement of the charged-current cross section of muon neutrinos. An equally important focus is the research and development of new detector technologies and target media. Specifically, water-based liquid scintillator (WbLS) is of interest as a novel detector medium, as it allows for the simultaneous detection of Cherenkov light and scintillation. This paper presents the deployment of a 366 L WbLS vessel in ANNIE in March 2023 and the subsequent detection of both Cherenkov light and scintillation from the WbLS. Finally, this proof-of-concept allows for the future development of reconstruction and particle identification algorithms in ANNIE, as well as dedicated analyses within the WbLS volume, such as the search for neutral-current events and the hadronic scintillation component.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

Towards a neutron multiplicity measurement with the Accelerator Neutrino Neutron Interaction Experiment

The Accelerator Neutrino Neutron Interaction Experiment (ANNIE) is a 26 ton Gadolinium (Gd)-loaded water Cherenkov detector located on the Booster Neutrino Beam line at Fermilab. Its main goals are the measurement of the neutron multiplicity in neutrino-nucleus interactions as well as the cross-section of Charged Current Quasi-Elastic (CCQE) neutrino interactions on water. Besides the physics goals, the experiment also aims to be a testbed for new technologies such as Large Area Picosecond Photodetectors (LAPPDs) and Water-based Liquid Scintillators (WbLS). This thesis presents a preliminary measurement of the neutron multiplicity with {ANNIE}, using an analysis conducted on a fraction of the 2021 beam year. As preparatory measures, the efficiency of ANNIE's Front Muon Veto (FMV) was determined to be {$\bar{\varepsilon}_{\mathrm{FMV}} = (95.6 \pm 1.6)\%$} while the average efficiency for active scintillator paddles in the Muon Range Detector (MRD) was found to be {$\bar{\varepsilon}_ {\mathrm{MRD}} = (92.1 \pm 7.9)\%$}. Furthermore, the simulation framework used for ANNIE was validated and adapted to reproduce the experimental data by comparing the detector response for samples of Michel electrons, Americium Beryllium neutrons, and through-going muons. The analysis finds average neutron yields of {$\bar{n}_{\mathrm{data}} (\mathrm{beam}) = (0.272 \pm 0.010_{\mathrm{stat}})$} for an inclusive set of all identified muon neutrino candidates and {$\bar{n}_{\mathrm{data}} (\mathrm{beam,FV}) = (0.287 \pm 0.044_{\mathrm{stat}})$ for interactions which happened inside of the Fiducial Volume of ANNIE, which was optimized to increase the neutron detection acceptance. The presented neutron multiplicity values represent the number of detected neutrons after all event selection cuts and are not yet corrected for the neutron detection efficiency. An equivalent analysis on a simulated beam sample predicts neutron yields of $\bar{n}_{\mathrm{MC}}(\mathrm{beam}) = (0.515 \pm 0.0 07_{\mathrm{stat}})$ and $\bar{n}_{\mathrm{MC}}(\mathrm{beam,FV}) = (0.627 \pm 0.031_{\mathrm{stat}})$, indicating that the models tend to overpredict the number of neutrons produced in such interactions. Systematic errors have been briefly considered to contribute {$\sigma_{\mathrm{sys,FMV}} \sim 0.01\,$neutrons/$\nu$-interaction} due to the slight FMV inefficiency and {$\sigma_{\mathrm{sys,n}} \sim 0.05\,$neutrons/$\nu$-interaction} due to the neutron detection efficiency. Simulation studies further highlighted the importance of neutron detection in Diffuse Supernova Background (DSNB) searches. A combination of neutron tagging and Convolutional Neural Networks was found to reduce the most relevant Neutral Current Quasi-Elastic (NCQE) interaction background below the signal level, achieving a Signal-to-Background ratio of 4:1. In a further study, we investigated the positive impact of a deployment of a WbLS target on the energy reconstruction in ANNIE. WbLS provides a scintillation signal from hadronic recoils in addition to the charged lepton that can be included in neutrino energy reconstruction. It was found that a deployed WbLS volume in ANNIE improves the neutrino energy reconstruction from 14\% to 12\%, with the potential of going beyond this if more sophisticated reconstruction algorithms are developed in the future.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

Benchmarking intra-nuclear cascade models for neutrino scattering with relativistic optical potentials

The description of final-state interactions (FSI) in the large phase space probed in neutrino experiments poses a great challenge. In neutrino experiments, which operate under semi-inclusive conditions, cascade models are commonly used for this task, while under exclusive conditions FSI can be treated with relativistic optical potentials (ROP). We formulate conditions under which the ROP approach and cascade model can be directly compared. We feed the NEUT cascade with events from a relativistic distorted-wave impulse approximation calculation that uses the real part of an optical potential. Cuts on the missing energy of the resulting events are applied to define a set of events that can be directly compared to RDWIA calculations with the full optical potential. The NEUT cascade and ROP agree for proton kinetic energies $T_p > 150$ MeV for carbon, oxygen and calcium nuclei when a realistic nuclear density is used to introduce events in the cascade. For $T_p < 100$ MeV the ROP and NEUT cross sections differ in shape and differences in magnitude are larger than 50 %. Single transverse variables allow to distinguish different approaches to FSI, but due to a large non-QE contribution the comparison to T2K data does not give an unambiguous view of FSI. We discuss electron scattering and argue that with a cut in missing energy FSI can be studied with minimal confounding factors in e.g. $e4\nu$. The agreement of the ROP and NEUT for T2K conditions lends confidence to these models as a tool in oscillation analyses for sufficiently large nucleon kinetic energies. These results urge for caution when a cascade model is applied for small nucleon energies. The assessment of model assumptions relevant to this region are strongly encouraged. This paper provides novel constraints on cascade models from proton-nucleus scattering that can be easily applied to other neutrino event generators.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DetSuM: Detector Surrogate Model for LArTPC

In large neutrino experiments such as the Deep Underground Neutrino Experiment (DUNE), estimating detector response uncertainties typically requires simulation samples that consume substantial computing resources and time. To mitigate this challenge, we present DetSuM, an uncertainty-aware surrogate model designed to capture the detector response variations with reduced computing load compared to full simulations. This poster describes the construction and evaluation of DetSuM using simulation and reconstruction datasets in a rare-event search at DUNE. We assess DetSuM's ability to predict key detector-response variations and their associated uncertainties, discuss current limitations, and outline improvements to extend its validity in systematics studies of DUNE physics.

Li, Aobo [UC, San Diego]↗

Measuring Charged Current Muon Neutrino Cross Sections on Oxygen with the Accelerator Neutrino Neutron Interaction Experiment

The Accelerator Neutrino Neutron Interaction Experiment (ANNIE) is a $26$-ton gadolinium-doped water Cherenkov detector on-axis of the Booster Neutrino Beam (BNB) at Fermilab. With ANNIE, this dissertation provides the first results of a high statistics muon neutrino charged current cross section on oxygen at a short baseline on axis. This thesis explains the necessity of the measurements and details how the measurements are performed. Focus is given on the physics behind the models used to predict neutrino cross sections. The measurements include the CC inclusive and CC$0\pi$ cross section channels. This thesis details the processes of event selection, reconstruction, handling systematics, and unfolding as part of cross section measurements. A data unblinding procedure is documented and acts as the first implementation of data unblinding in ANNIE. These results provide additional constraints on existing neutrino interaction models and are the necessary steps for a joint analysis with liquid Argon experiments also on the BNB line.

Minock, James [Rutgers U., Piscataway (main)]↗

3D-Reconstruction of Tau Neutrinos in LArTPC Detectors

The Deep Underground Neutrino Experiment (DUNE) is a next-generation neutrino experiment currently under construction. DUNE will consist of two high-resolution neutrino interaction imaging detectors exposed to the world’s most intense neutrino beam, with the Near Detector at Fermilab and the Far Detector 1,300 km away in the Sanford Underground Research Facility in South Dakota, US. The high statistics and excellent resolution capabilities of DUNE's $^{40}$Ar detector will allow us to make precision studies of oscillation parameters capable of searching for CP violation in the lepton sector, testing interaction models, and studying phenomena that have until now, seemed too complex to measure, like $\nu_\tau$ detection and therefore, providing the completion of the 3-flavor neutrino paradigm. Knowledge of the $\nu_\tau$ detection can impact a broad spectrum of open questions. These include searching for non-standard neutrino interactions, constraining the unitarity of the PMNS matrix, searching for sterile neutrinos, and studying neutrino interactions. In the case of LArTPC data, the detector hits can be considered nodes in a graph, and the edges represent the spatial and temporal relationships between them. By using graph neural networks, it is possible to exploit these relationships and improve the accuracy of particle identification and reconstruction. During my presentation and specifically for tau neutrino reconstruction, I will show the effectiveness and reliability of our in-house developed graph neural network (GNN), NuGraph. This GNN classifies detector hits based on the particle type responsible for their production, assuring that the system accurately identifies and categorizes information based on its unique characteristics.

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