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All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Muon Neutrino Reconstruction at ICARUS with Machine Learning

The ICARUS T600 LArTPC detector successfully ran for three years at the underground LNGS laboratories, providing a first sensitive search for LSND-like anomalous electron neutrino appearance in the CNGS beam. After a significant overhauling at CERN, the T600 detector has been placed in its experimental hall at Fermilab, fully commissioned, and the first events observed with full detector readout. Regular data-taking began in May 2021 with neutrinos from the Booster Neutrino Beam (BNB) and neutrinos six degrees off-axis from the Neutrinos at the Main Injector (NuMI). Modern developments in machine learning have allowed for the development of an end-to-end machine learning-based event reconstruction for ICARUS data. This reconstruction folds in 3D voxel-level feature extraction using sparse convolutional neural networks and particle clustering using graph neural networks to produce outputs suitable for physics analyses. This poster will summarize the performance of a high-purity and high-efficiency end-to-end machine learning-based selection of muon neutrinos from the BNB and highlight studies of electromagnetic shower reconstruction from a neutral pion selection.

43 PARTICLE ACCELERATORS↗
DOE OSTI2024

Track vs Shower Discrimination in the Event Reconstruction of the ICARUS Experiment

The ICARUS collaboration has employed the 760-ton T600 liquid argon TPC detector in a successful three-year physics run at the underground LNGS laboratory, performing a sensitive search for LSND-like anomalous appearance in the CERN Neutrino to Gran Sasso beam, which contributed to the constraints on the allowed neutrino oscillation parameters to a narrow region around 1 eV. After a significant overhaul at CERN, the T600 detector has been installed at Fermilab. The detector commissioning phase lasted until June 2022, then ICARUS moved to data taking for neutrino oscillation physics collecting events from the Booster Neutrino Beam (BNB) and the Neutrinos at the Main Injector (NuMI) beam off-axis. The initial experiment goals are to either confirm or refute the claim by Neutrino-4 short-baseline reactor experiment, perform measurements of neutrino cross sections with the NuMI beam and several Beyond Standard Model searches. Then, ICARUS will jointly search for evidence of sterile neutrinos with the Short-Baseline Near Detector (SBND). In this contribution, we discuss recent changes to the standard TPC event reconstruction that uses Pandora, a pattern recognition software common to liquid argon-based detectors. In particular, we performed a new training of the Boosted Decision Tree (BDT) employed to separate track-like and shower-like reconstructed particles using Monte Carlo simulations of neutrino events from BNB in ICARUS. We compare the discrimination capabilities of the old and new BDT training and discuss further improvements of this algorithm.

43 PARTICLE ACCELERATORS↗
DOE OSTI2022

Light Yield Calibration in MicroBooNE

The MicroBooNE detector is a Liquid Argon Time Projection Chamber (LArTPC) located along the Booster Neutrino Beam (BNB) at Fermilab. Its primary physics goal is to contribute to addressing the elusive short-baseline MiniBooNE low energy excess. MicroBooNE records and utilises both the ionisation charge and scintillation light produced inside the TPC to select and reconstruct neutrino interactions. The scintillation light collected using a plane of PhotoMultiplier Tubes (PMTs) is also used for accurate event timing and cosmic muon rejection with the latter being an important background for detectors, such as MicroBooNE, located on the surface. A good understanding of light modeling and its related systematic uncertainties is crucial to evaluate their impact on physics analyses. The experience acquired from MicroBooNE regarding how the stability of the scintillation light behaves and evolves during the 5 years of the primary physics run will be discussed as well as the calibration method used. This experience will help with understanding the physics of scintillation light in LArTPCs for the future long-running Short-Baseline Neutrino (SBN) and Deep Underground Neutrino Experiment (DUNE) programs.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2024

MeV-scale Physics in MicroBooNE

The scope of this public note is to present preliminary measurements of MeV energy signatures and relevant backgrounds for beam neutrino interactions using a dedicated reconstruction technique, expanding upon the original study by ArgoNeuT. We also use this technique to identify and quantitatively analyze the low energy activity emanating from the field cage support ribs in the MicroBooNE detector and discuss how the usage of similar construction materials could be a source of background for low energy physics analyses in future neutrino detectors such as the Deep Underground Neutrino Experiment (DUNE). We also highlight the application of the low energy reconstruction technique to studies of neutrino interactions from core-collapse supernovae and muons decaying at rest ($\mu$DAR).

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2021

Double-differential Measurements of Mesonless Charged-current Muon Neutrino Interactions on Argon with Final-State Protons using the MicroBooNE Detector

This note reports the first double-differential measurements of charged-current $ν_µ$ scattering on argon leading to final states containing zero mesons and one or more protons. This event topology (hereafter abbreviated as CC0$πNp$) is the most common at the neutrino energies typically produced by the Fermilab Booster Neutrino Beam. A detailed understanding of neutrino-argon scattering in the CC0$πNp$ channel is therefore crucial for the success of the precision neutrino oscillation analyses planned for the Short-Baseline Neutrino (SBN) program. This remains true for the upcoming Deep Underground Neutrino Experiment (DUNE), but the higher mean neutrino energy used there will ensure that more inelastic reaction modes, such as single pion production, will also play a major role. The analysis described in this note builds on a previous MicroBooNE study of the CC0$πNp$ channel which obtained the first single-differential cross-section measurements on an argon target. Since that foundational work, significant improvements have been made to MicroBooNE’s simulation software, event reconstruction algorithms, and procedure for calculating systematic uncertainties. When combined with a larger dataset (corresponding to a beam exposure of 6.79 × 10 20 protons-on-target versus 1.60 × 10 20 in Ref. [3]), these enhancements allow the important CC0$πNp$ channel to be studied in more detail. This note begins with a description of the data and simulation samples used as input to the analysis. Section 3 then defines the CC0$πNp$ signal event topology, and Section 4 describes a set of selection criteria designed to identify these events in MicroBooNE data. Binning schemes are then defined in Section 5 for two double-differential measurements of event rates. The first of these considers the momentum and scattering cosine of the outgoing muon, while the second reports the same observables for the leading proton, i.e., the final-state proton with the largest momentum. After a discussion of systematic uncertainties in Section 6, the note concludes by comparing the predictions of MicroBooNE Monte Carlo (MC) simulations to the measured double-differential distributions. These results will form the basis for a future extraction of flux-averaged double-differential CC0$πNp$ cross sections that will be immediately comparable to the theoretical predictions of multiple neutrino event generators. The selection described herein may also be used to study various other observables in CC0$πNp$ events, including those which are sensitive to correlations between leptonic and hadronic kinematics in the final state.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2024

Evaluating the Effects of Detector Modeling Uncertainties on Sterile Neutrino Oscillation Analysis with the ICARUS Detector

he ICARUS T600 LArTPC detector was refurbished after an initial run at the underground LNGS labs and is currently taking data within its experimental hall at Fermilab after full commissioning. Regular data taking began in May 2021 with neutrinos from the Booster Neutrino Beam (BNB) and the Neutrinos at the Main Injector (NuMI) off-axis beam. As the far detector of the Short-Baseline Neutrino (SBN) Program, the ICARUS detector's capability in searching for both muon neutrino disappearance and electron neutrino appearance will allow for unprecedented sensitivity to light sterile neutrinos with eV-scale mass. The ultimate sensitivity of the detector to sterile neutrino oscillations depends on the understanding of the detector response and the uncertainties remaining after calibrating the detector model. This poster will review how detector-model-related uncertainties are quantified, evaluated and their impact on expected sterile neutrino oscillation sensitivities using the existing ICARUS data. The work pursued here is broadly applicable to other ICARUS analysis pathways such as neutrino-argon cross-sections and Beyond the Standard Model physics searches. It will finally discuss improvement pathways to reduce the systematic uncertainties to the level needed for the joint sterile neutrino oscillation analysis with the Short Baseline Near Detector (SBND) within the SBN Program.

43 PARTICLE ACCELERATORS↗
DOE OSTI2024

ML-Based Reconstruction in a Pixelated LArTPC

The Deep Underground Neutrino Experiment (DUNE) will address open issues in neutrino physics such as the measurement of the CP-violating phase in neutrino oscillations and the neutrino mass ordering. DUNE is expected to have the most energetic neutrino beam in the world. The 2x2 demonstrator is a single-phase liquid argon time projection chamber (LArTPC), with four modules, operated as a prototype for the DUNE Liquid Argon Near Detector (ND-LAr). Each module in the 2x2 demonstrator is 0.67m x 0.67m x 1.8m. Based on the ArgonCube design concept, the 2x2 features a novel pixelated charge readout and advanced high-coverage photon detection system. Machine learning can be used to form a complete reconstruction pipeline for physics interactions in the 2x2 detector. This poster will describe the workings of a package under development called SPINE and its current performance.

43 PARTICLE ACCELERATORS↗
DOE OSTI2024

Constructing Data-Driven Predictions at the Far Detector for NOvA's Neutrino Oscillation Analysis.

NOvA, is a two-detector, long-baseline neutrino oscillation experiment located at Fermilab, Batavia, IL, USA. It is designed primarily to constrain neutrino oscillation parameters using $\nu_\mu \ (\bar{\nu}_\mu)$ disappearance and $\nu_e \ (\bar{\nu}_e)$ appearance data. The Neutrinos at Main Injector (NuMI) beamline at Fermilab provides a high purity 900 KW intense beam of neutrinos and anti-neutrinos to NOvA. The NOvA Near Detector, located 100m underground and 1km away from the beam source, observes the un-oscillated $\nu_\mu \ (\bar{\nu}_\mu)$ and beam $\nu_e \ (\bar{\nu}_e)$ event spectrum. The Far Detector, located in Ash River, MN, USA, is 809 km from the ND and records the oscillated $\nu_e \ (\bar{\nu}_e)$ and the un-oscillated $\nu_\mu \ (\bar{\nu}_\mu)$ event spectrum. NOvA uses a data-driven technique called extrapolation to predict the expected number of $\nu_\mu \ (\bar{\nu}_\mu)$ and $\nu_e \ (\bar{\nu}_e)$ events at the Far Detector using the Near Detector data. The use of data from a functionally equivalent Near Detector provides a powerful constraint on the systematic uncertainties in NOvA neutrino oscillation analyses. As NOvA continues to add data statistics, a robust constraint on systematics becomes more crucial for neutrino oscillation analysis. The details of the NOvA neutrino oscillation analysis framework and how it constrains dominant systematic uncertainties using the Near Detector data will be discussed in this poster.

43 PARTICLE ACCELERATORS↗
DOE OSTI2024

Achievement in Beam Power Records for the NOvA Target System

We began upgrading the NOvA target system for 1-Mega Watt (1-MW) beam operation in 2017. Major challenges included maintaining the quality of neutrino beams with reliable instrumentation, reducing instantaneous beam heating on the target, increasing cooling power to handle the high-power beam, and controlling tritium water production rate. We finally achieved a one-hour beam power record of 1.018 MW in Summer 2024. This milestone demonstrates our capability to operate at 2+ MW beam power for the future Long Baseline Neutrino Facility (LBNF) and Deep Underground Neutrino Experiment (DUNE).

43 PARTICLE ACCELERATORS↗
DOE OSTI2024

Carbon Storage Site Mapping Inquiry Tool (MapIT)

The Carbon Storage Site Mapping Inquiry Tool (MapIT) is an online web mapping tool designed to help users discover available public-sourced data to facilitate data exploration in support of Underground Injection Control (UIC) Program Class VI Well Site permitting for the geologic sequestration of carbon dioxide.

Pantaleone, Scott↗
DOE OSTI2024

A Workflow for Characterizing Legacy Wells as Potential Leakage Pathways for Integration to NRAP-Open-IAM

Carbon capture and storage is a crucial component of climate change mitigation strategies, involving the capture of carbon dioxide (CO2) from point sources and its injection into permeable subsurface formation. Many suitable CO2 storage sites coincide with legacy wells since the conditions that kept hydrocarbons in-situ for thousands of years are also ideal for storage of carbon dioxide. To protect underground sources of drinking water (USDW) during greenhouse gas injection, the Environmental Protection Agency (EPA) mandates area of review evaluations. These evaluations ensure that drinking water sources would not be contaminated by injected fluids. They include identification of legacy wellbores, integrity assessments, and implementing any necessary corrective action. Previous assessment approaches of legacy wells include high-level scoring of regional data and well construction and abandonment evaluation. This work describes a novel methodology that evaluates well construction and abandonment, ranks them based on complexity, and performs a risk assessment with NRAP-Open-IAM. A workflow of the methodology is presented, highlighting its capabilities and limitations.

Wise, Jarrett↗
DOE OSTI2024

Determining Optimal Running Conditions for TinyTPC Detector

Liquid argon time projection chambers, (LArTPCs), are particle detectors used to collect ionization charge information from particle trajectories, facilitating detailed particle track analysis. They are currently used as particle detectors in major physics projects such as the deep underground neutrino experiment (DUNE), to detect and study the nature of the elusive neutrino particle. TinyTPC is a small scale LArTPC featuring a pixelated readout system (LArPix) that we will use to study liquid argon doping. We expect this doping to enhance the resolution of LAr detectors, especially for low energy particles below 10 MeV which would expand the capabilities of currently running experiments. Housed within a cryostat filled with liquid argon, the TinyTPC will rely on a high and a low voltage system to collect data. In preparation for deployment we found and resolved issues in the HV and LV systems and we determined optimal running conditions in a test vessel. This presentation will go over the detector commissioning process that enabled data taking with the TinyTPC.

Gonzalez, Rebecca↗
DOE OSTI2024

Machine Learning for DUNE Supernova Trigger

One of the major scientific goals of the Deep Underground Neutrino Experiment (DUNE) is to detect and measure the neutrino flux from galactic core-collapse supernovae. These neutrinos, which exist in the low energy range of up to a few tens of MeV and are responsible for carrying away over 99% of the gravitational binding energy of the supernova, provide an opportunity to study the end of life evolution of massive stars, as well as unique properties and interactions of neutrinos. Because galactic supernovae are expected to occur only on the timespan of every few decades, it is crucial that DUNE is able to detect supernova neutrino interactions when they occur. However, detecting these supernova interactions requires sifting through a large amount of data, and DUNE detectors require a trigger to signal when supernova neutrino events occur. Machine learning provides a potential approach to creating this trigger. This project generates ADC and ground truth images of neutrino interactions in a LArTPC detector as simulated by the Model of Argon Reaction Low Energy Yields (MARLEY) to be used for machine learning. The eventual goal of this work is to facilitate DUNE s detection of supernova neutrino interactions by building a machine learning pipeline that will train the trigger algorithm.

Damish, Stephanie↗
DOE OSTI2024

Predicting Missing Regions in Charged Particle Tracks Using a Sparse 3D Convolutional Neural Network

The 2x2 Demonstrator is a prototype detector for the Deep Underground Neutrino Experiment (DUNE)'s Near Detector. Both the 2x2 Demonstrator and the Near Detector itself will have inactive regions wherein there is no sensitivity to charge deposition and light signals that arise from charged particle interactions with liquid argon. In the 2x2, these inactive regions are positioned in-between the active detector modules, which introduces the challenge of inferring what charge signals ought to look like in these regions. This study explores the use of a Sparse 3D Convolutional Neural Network (ConvNet) to infer missing regions in charged particle tracks. Hits corresponding to energy depositions are voxelized into a three-dimensional (3D) grid for each track. Inactive regions within the tracks are replaced with a dense, rectangular 3D grid of voxels, ensuring consistent step sizes in X, Y, and Z directions. Voxels in these dense regions are initialized with an energy value of -1, indicating nonphysical energy or charge. The model is trained to predict which voxels should activate as part of the track and which should not, with the goal of eventually inferring the missing charge or energy values in these voxels. Results indicate that the model accurately predicts track voxels within ±1 unit in X, Y, or Z directions and effectively identifies non-track voxels, despite some overprediction. The approach shows promise in prediction of missing track regions with some accuracy.

43 PARTICLE ACCELERATORS↗
DOE OSTI2024

LYNM PE1 Pre-Experiment A Site Characterization Report

Underground chemical explosive experiments such as LYNM PE1 generate large multiphenomenological datasets, require complex site preparation and build out, and utilize cutting edge models and analysis techniques to analyze and simulate the explosion-induced signals. This wide range of outcomes makes it a necessity to thoroughly characterize the testbed in advance of experiments in a way that complements the wide suite of data being generated. Here, we present a broad overview of the site characterization work and data collection that was conducted before Experiment A, which is the first in a series of three PE1 experiments. This work includes, but is not limited to, geologic mapping, physical sample collection, analysis of material properties, geophysical borehole logging, and in-situ measurements. This information was collected by a large, dedicated team and was used to inform site construction, finalize instrumentation placement, generate Geologic Framework Models, feed pre-experiment predictions, and facilitate post-experiment data analysis

58 GEOSCIENCES↗
DOE OSTI2023

Strategic Petroleum Reserve Enhanced Monitoring Compendium (FY23)

The Strategic Petroleum Reserve (SPR) is the world’s largest supply of emergency crude oil. The reserve consists of four sites in Louisiana and Texas. Each site stores crude in deep, underground salt caverns. It is the mission of the SPR’s Enhanced Monitoring Program to examine available sensing data to inform our understanding of each site. This report discusses the monitoring data, processes, and results for each of the four sites for fiscal year 2023.

02 PETROLEUM↗
DOE OSTI2023

M4 Summary of EBS International Activity

Thermal-Hydrologic (TH) modeling of DECOVALEX 2023, Task C has continued in FY23. This report summarizes progress in TH modeling of Step 1c, with calibration modeling and the addition of shotcrete. The work involves 3-D modeling of the full-scale emplacement experiment at the Mont Terri Underground Rock Laboratory (Nagra, 2019). While Step 1 is focused on modeling the heating phase of the FE experiment with changes in pore pressure in the Opalinus clay resulting from heating, Step 1c is focused on calibration of models using available data.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Assessment of InSAR Seasonal Movements at Bayou Choctaw SPR Site

Ground deformation is important to monitor for the ongoing safety and stability of underground caverns. Implementing InSAR technology to monitor site-wide surface deformation at the Strategic Petroleum Reserve has revealed seasonal ground movements at Bayou Choctaw in Louisiana. The cyclic, seasonal pattern shows soil shrinkage during the spring and summer months and soil expansion during the fall and winter months. Prior to this report, no in-depth investigation was conducted to explain this seasonal phenomenon. However, the ground movement is believed to be near-surface and not geological due to the relatively insignificant movement between years. To better understand seasonality movements, soil properties, land cover, and climatic conditions are assessed to relate near-surface water and soil interactions. The soil, land, and climatic properties all contribute to seasonal ground movement, and vegetation cover and the soil's water capacity contribute to the spatial variability of InSAR seasonal measurements at Bayou Choctaw.

02 PETROLEUM↗