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DOE OSTI2026

Electron Neutrino Selection for Neutrino Oscillation Searches in the SBN Program

The Short- Baseline Neutrino (SBN) Pprogram at Fermilab employs a near detector, SBND, and a far detector, ICARUS, to search for baseline-dependent changes to the measure muon neutrino disappearance and electron neutrino appearance and disappearance from the muon neutrino-dominated Booster Neutrino Beam flux. The use of the same liquid argon time projection chamber (LArTPC) technology in both detectors and the same neutrino beam enables a systematically well-constrained oscillation measurement, by leveraging the high statistics of O(10 million) neutrino interactions expected at SBND through 2027 to constrain flux and cross-section systematics. In this poster, we present high-statistics, high-purity electron neutrino selections in SBND and ICARUS and related electron shower energy calibration measurements in SBND enabled by the SPINE deep learning-based particle reconstruction framework. Furthermore, we discuss how these samples can enable high sensitivity to '3+1' sterile neutrino oscillations in a combined fit accounting for systematic uncertainties and correlations.

Oza, Nupur [Nevis Labs, Columbia U.; Columbia U.]↗
DOE OSTI2026

Measurement of charged-current quasi elastic-like and inclusive muon (anti-) neutrino interactions using NuMI beam the ICARUS detector

The ICARUS experiment, utilizing Liquid Argon Time Projection Chamber (LArTPC) technology, has been successfully taking physics data at Fermilab since June 2022. The experiment's primary objective is to function as the far detector of the Short Baseline Neutrino program (SBN), searching for hints of physics beyond three-flavour PMNS neutrino oscillations. ICARUS also offers other diverse physics capabilities, including searches beyond the standard model and measurements of cross-sections. In addition to being exposed to the Booster Neutrino (BNB) beamline of the SBN experiment, ICARUS sits 795 m downstream and 5.75 degrees off-axis from the NuMI beam line. Due to the off-axis angle between NuMI and ICARUS, coupled with contributions from both pion and kaon decays to neutrino fluxes, interactions of NuMI neutrinos within ICARUS can be detected over a range of several GeV in energy. Measurements of these interactions present unique opportunities to infer neutrino interaction cross sections on an argon nuclear target within an energy range that overlaps both the SBN oscillation search and a significant portion of the DUNE spectrum. This poster will summarize the current status of muon-neutrino and muon-antineutrino charged current inclusive cross section measurements in ICARUS using the FHC and RHC NuMI sample respectively. Such inclusive measurements provide robust benchmarks for the overall interaction rate and muon kinematics, with reduced dependence on hadronic final-state modeling. The measurement of the muon-antineutrino Argon charged current inclusive cross section is particularly exciting and highly relevant for DUNE, given the scarcity of antineutrino-Argon cross section measurements in the literature. In addition, this poster will highlight ICARUS's first measurement of muon-neutrino Argon charged current mesonless final states using both transverse kinematic imbalance (TKI) variables and muon-proton kinematic variables. These measurements provide sensitivity to nuclear effects, final-state interactions, and nucleon correlations in argon, offering important constraints for improving neutrino interaction modeling for upcoming DUNE.

Roy, Promita [Fermilab; Virginia Tech.]↗
DOE OSTI2026

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.]↗
DOE OSTI2026

Event Selection at the DUNE Near Detector

The Deep Underground Neutrino Experiment (DUNE) is a next generation long-baseline experiment designed with a primary goal of providing definitive measurements of the neutrino mass ordering and the Charge Parity (CP)-violating phase. A key component in reaching these goals is the DUNE Near Detector (ND) complex, placed 574 m from the beam target, and tasked with characterising the neutrino beam and constraining systematic uncertainties before oscillation effects occur. We will present primary findings from the development of an event selection for $\nu_\mu$ CC interactions within the ND liquid argon time projection chamber (ND-LAr). These findings will include a discussion of the importance of defining metrics, such as efficiency and purity, such that we can meet the required sensitivity for DUNE's physics measurements.

Sabater Andres, Eva [Sussex U.] (ORCID:00090001748↗
DOE OSTI2026

Probing BSM Oscillatory Signals with the DUNE Detectors and the LBNF Neutrino Beam: NSI and Sterile Neutrino Sensitivities

The Deep Underground Neutrino Experiment (DUNE) is a flagship long-baseline accelerator neutrino experiment under construction in the U.S. With a 1,300 km distance between its Near Detector (ND) and Far Detector (FD), the world’s most intense LBNF (Long-Baseline Neutrino Facility) neutrino beam, and high-resolution LArTPC (Liquid Argon Time Projection Chamber) detectors, DUNE will measure the neutrino oscillation parameters with unprecedented precision. In addition to the determination of the neutrino mass ordering and the potential discovery of Charge-Parity violation in the leptonic sector, DUNE's capabilities present a unique opportunity for probing Beyond the Standard Model (BSM) physics signals with neutrinos. Examples of BSM physics manifestations include the presence of Non-Standard Interactions (NSI) of neutrinos with matter, or the existence of sterile neutrinos that mix with 3-Flavor neutrinos. In this study, we present a joint ND+FD fit to simulated data assessing DUNE's sensitivity to probing neutrino NSI, as well as scenarios including sterile neutrino mixing.

Prais, Luiz [Cincinnati U.] (ORCID:000000018224947↗
DOE OSTI2026

Pandora-based muon neutrino disappearance search in the SBN program with 1muonNp quasi-elastic like channel

The Short-Baseline Neutrino (SBN) program at Fermilab aims to perform a definitive search for light sterile neutrinos using multiple liquid argon time projection chamber detectors. We present the status of an analysis of muon neutrino charged-current interactions in two SBN detectors (SBND and ICARUS), selecting fully contained events with one muon and at least one proton in the final state, inspired by the recent ICARUS standalone results. This topology-driven selection reduces dependence on neutrino interaction cross-section modeling while preserving high statistics and good neutrino energy resolution, enabled by robust reconstruction and particle identification. Fully contained events enable precise kinematic reconstruction and support relative measurements between detectors, directly addressing the core goals of the SBN program. A detailed evaluation of systematic uncertainties is currently underway, with the objective of reducing the dominant systematics to the percent level. The current status of this analysis, with a particular focus on the event selection performance, will be presented in this poster.

Artero Pons, Maria [Padua U.; INFN, Padua]↗
DOE OSTI2026

SBND Shower Reconstruction with SPINE

The Short-Baseline Near Detector (SBND) is a liquid argon time projection chamber (LArTPC) neutrino detector in the Short-Baseline Neutrino (SBN) program at Fermilab. SBND is designed to investigate the Low-Energy Excess (LEE), an unexplained excess of electron-like events observed by previous short-baseline neutrino experiments that may point to physics beyond the Standard Model. In LArTPC detectors, precise shower reconstruction is essential for distinguishing electrons from photons, a key requirement for testing possible explanations of the LEE and improving $\nu_e$ event selection. In this poster, the reconstruction studies using the Scalable Particle Imaging with Neural Embeddings (SPINE), a machine learning based reconstruction framework for particle imaging detectors will be presented. SPINE combines sparse convolutional neural networks (CNN) and graph neural networks (GNN) to enable detailed reconstruction and characterization of neutrino interactions in LArTPC detectors. Shower calorimetry and kinematic reconstruction are performed in dedicated post-processing stages. Strong agreement between data and Monte Carlo simulation will be demonstrated, indicating high-precision detector calibration and reconstruction performance. The agreement between reconstructed and true electron shower energy will also be discussed, emphasizing the robustness of the shower reconstruction performance. These results demonstrate the unprecedented precision achievable with SPINE in SBND, highlighting their potential for future high-resolution neutrino measurements.

Fan, Castaly [Florida U.; Fermilab] (ORCID:0000000↗
DOE OSTI2026

Measurement of mean excitation energies of neutrino-relevant materials

Modern neutrino experiments require precision reconstruction of events. A crucial component of this reconstruction is the stopping power for charged particles, calculated using the Bethe equation. The main free parameter of the Bethe equation is the mean excitation energy (the "I-value"), which in most cases cannot be calculated, but must be measured for each substance. In many cases, the values are derived from very old experiments with large quoted uncertainties, or worse, small quoted uncertainties and inadequate treatment of systematics. Even if the tabulated values were reliable to the necessary degree, the I-value is affected by the phase of the substance and by chemical bonding, and only rough heuristics have been developed to convert measurements of single elements into I-values for compounds or from one phase to another. Modern neutrino experiments which need to measure an absolute energy scale, while lacking calibration sources, suffer the most from uncertainties in the I-value. DUNE is a primary example. We are performing measurements with the 400MeV Fermilab LINAC beam at the Irradiation Test Facility. We describe our efforts to measure the I-value of liquid argon, as well as those for several other materials used in past, present and potential future neutrino experiments, including water, NOvA scintillator, MINOS steel, zirconium, and molybdenum. These measurements use a set of degraders to scan the beam energy around the Bragg peak for each substance to determine the proton range. Experimental results are compared to results from Geant4 and FLUKA to determine the I-value.

Strait, Matthew L. [Fermilab] (ORCID:0000000157088↗
DOE OSTI2026

Neutron-Antineutron Transition Search at DUNE

Neutron-antineutron transition is a baryon number violating process with ΔB=2, providing a unique insight into potential explanations of the baryon asymmetry in our universe, especially in the context of post-sphaleron baryogenesis. Studies have been conducted across various neutron-rich environments, including free neutron sources, neutron stars, and bound neutrons in large underground neutrino detectors, among which large neutrino experiments have historically obtained the most stringent constraints. The forthcoming Deep Underground Neutrino Experiment (DUNE) will offer especially strong prospects for this search, enabled by the high spatial resolution of its liquid argon time projection chamber. This poster presents a recent study on neutron-antineutron transition at DUNE using machine-learning based event classification.

Yu, Kaiwen [UC, Irvine (main)]↗
DOE OSTI2026

Performance and Stability Characterization of the ICARUS Light Detection System

The ICARUS detector, a key component of the Short Baseline Neutrino (SBN) Program at Fermilab, consists of two identical T300 modules filled with liquid argon. It is equipped with a Light Detection System (LDS) based on 360 8-inch Hamamatsu R5912-MOD photomultiplier tubes (PMTs) arranged behind the wire planes to collect Vacuum Ultraviolet (VUV, $\sim$ 128 nm) scintillation light. Operating under cryogenic conditions ( $\sim$ 87 K), the LDS is essential for determining the event start time (t0) with nanosecond precision for beam spill synchronization, improving longitudinal spatial resolution, and contributing to the event trigger and cosmic-ray mitigation. The performance of the LDS was investigated addressing both hardware and data analysis aspects. Following a progressive degradation in PMT gain observed during operations at FNAL, systematic gain measurements were first carried out from room temperature down to low temperatures. The results show stable performance at room temperature but a significant, irreversible reduction in gain at low temperatures. Based on these findings, a series of mitigation strategies were implemented in the ICARUS detector to preserve PMT performance and ensure reliable cryogenic operation. Currently, ongoing waveform analysis of the PMT signals is being performed to characterize signal shape, charge integration, and timing properties, aiming to refine and improve the agreement between experimental data and Monte Carlo simulations.

Saia, Clara [U. Catania (main); INAF, Catania; Cat↗
DOE OSTI2026

Improving MicroBooNE's Inclusive Single-Photon Search with Low-Energy Hadronic Identification

This analysis aims to further investigate MicroBooNE’s inclusive single-photon search results, which reported a 2.2$\sigma$ excess below 600 MeV in shower energy for events with no reconstructed protons using roughly half of MicroBooNE's dataset. Taken together with MiniBooNE’s long-standing low-energy excess and MicroBooNE’s recent electron-like search results showing no observable excess with respect to Standard Model predictions, this result provides strong impetus for expanded exploration of the single-photon channel in Fermilab’s short-baseline liquid-argon time projection chamber (LArTPC) experiments. By identifying and classifying isolated MeV-scale energy depositions, or blips, in the vicinity of single-photon events selected by the Wire-Cell reconstruction framework, we establish a more comprehensive labeling scheme for nearby hadronic content. In particular, blips found backwards along the shower axis indicate the presence of previously-unidentified final-state protons, while elevated blip counts at wide angles signal the presence of final-state neutrons. By applying this new technique to its full dataset, MicroBooNE will perform a purer and higher-statistics test of the truly isolated nature of its modest photon-like excess, furthering its hunt for the presence of unexpected new physics beyond the Standard Model.

Andrade Aldana, Diego [Los Alamos; IIT, Chicago (m↗
DOE OSTI2026

Studying the SBND Cathode Structure

The Short-Baseline Near Detector (SBND) is a Liquid Argon Time Projection Chamber (LArTPC) located at Fermilab. It utilizes electrons ionized by passing particles inside the detector to create 3D reconstructions of interactions. This is achieved by drifting ionized electrons towards the instrumented wire planes. Structurally, the cathode can be mapped using cosmic ray muons by using information of their trajectories and their timing. This study shows that the cathode has rich structure. Though preliminary, this work shows evidence of non-negligible effects and warrants further investigation of these structures which could lead to improved detector simulations and more reliable event reconstructions.

Cataldo, Aedan [Notre Dame U.]↗
DOE OSTI2026

Improving Detector Systematic Uncertainties Through Data-Driven Machine Learning

Detector simulation in liquid argon time projection chambers (LArTPCs) is a constant challenge. In particular the modeling of electrons response on wires is highly nontrivial. However, new machine learning techniques exist which can be leveraged to ameliorate these concerns. We present a novel methodology to attempt to learn from cosmic muon data in the ICARUS detector how reconstructed wire signals are influenced by features of the hits such as location, particle direction, angle relative to the wire plane, etc. A model can then be generated which can apply the learned mapping to Monte Carlo events to create a more data-like simulation sample. By creating such a sample we expect to reduce the systematic uncertainties at ICARUS due to our detector modeling.

Hausner, Harry [Fermilab] (ORCID:0000000188932280)↗
DOE OSTI2026

Improving Detector Systematic Uncertainties Through Data-Driven Machine Learning

Detector simulation in liquid argon time projection chambers (LArTPCs) is a constant challenge. In particular the modeling of electrons response on wires is highly nontrivial. However, new machine learning techniques exist which can be leveraged to ameliorate these concerns. We present a novel methodology to attempt to learn from cosmic muon data in the ICARUS detector how reconstructed wire signals are influenced by features of the hits such as location, particle direction, angle relative to the wire plane, etc. A model can then be generated which can apply the learned mapping to Monte Carlo events to create a more data-like simulation sample. By creating such a sample we expect to reduce the systematic uncertainties at ICARUS due to our detector modeling.

Hausner, Harry [Fermilab] (ORCID:0000000188932280)↗
DOE OSTI2026

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]↗
DOE OSTI2026

SBND Detector Status and First Results

The Short-Baseline Near Detector (SBND) is one of three liquid argon time projection chamber (LArTPC) neutrino detectors positioned along the axis of the Booster Neutrino Beam (BNB) at Fermilab, and serves as the near detector in the Short-Baseline Neutrino (SBN) Program. The detector just completed its second year of running, collecting over 6.5e20 POT, equivalent to an unprecedented sample of over 5 million neutrino interactions. Initial data has demonstrated superb performance of the detector’s subsystems, enabling precise tracking and calorimetric reconstruction of events. With this large statistical data set, SBND is performing multi-dimensional cross section measurements of inclusive and exclusive topologies and precise searches for beyond the Standard Model (BSM) processes. As the near detector in the SBN Program, it will enable the full potential of the joint sterile neutrino measurements program by precisely characterizing the unoscillated neutrino beam, constraining BNB flux and neutrino-argon cross-section systematic uncertainties. In this talk, the current status of the experiment, first results from SBND’s neutrino interaction program, as well as status and prospects for BSM and oscillation searches are discussed.

Paton, Josephine [Fermilab] (ORCID:000000032651489↗
DOE OSTI2026

Towards a Public Event Display for DUNE

The Deep Underground Neutrino Experiment (DUNE) is a next generation long baseline neutrino experiment based at Fermilab, with a near detector near the beam target and a Far Detector (FD) in South Dakota. As the experiment prepares for its first data runs, creating pathways for public engagement and data transparency is essential. We present the first-ever DUNE event display designed for public outreach and education. Developed using data from the ProtoDUNE detectors at the CERN Neutrino Platform, this tool provides an intuitive and interactive interface that allows non-experts to visualise and explore particle interactions in a Liquid Argon Time Projection Chamber (LArTPC). By translating raw experimental data into a browser-accessible format, we establish the essential infrastructure for DUNE’s pathway to open data. This talk will detail the technical development of the display, its current implementation with ProtoDUNE data, and the strategic roadmap for integrating it into DUNE’s long-term open-access framework.

Sabater, Eva [U. Sussex (main)] (ORCID:00090001748↗
DOE OSTI2020

Recent Cross-Section Results from MicroBooNE

MicroBooNE is a liquid argon time projection chamber in the Booster Neutrino Beam at Fermilab. The large event rate and 3 mm wire spacing of the detector provide high-statistics, precise-resolution imaging of neutrino interactions leading to low-threshold, high-efficiency event reconstruction with full angular coverage. As such, this is an ideal place to probe neutrino-argon interactions in the hundreds-of-MeV to few-GeV energy range, and to study the impact of nuclear effects through detailed measurements of hadronic final states. These proceedings present recent measurements of neutrino interactions in MicroBooNE, including inclusive charged-current interactions, neutral-pion production, and measurements of low-energy protons.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗