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DOE OSTI2023

Search for Low mass Higgs Portal Scalars at the MicroBooNE Detector in the NuMI Beam

The MicroBooNE experiment exploits Liquid Argon Time Projection Chamber (LArTPC) technology to detect neutrinos from two beams at Fermilab, Chicago: on-axis Booster Neutrino Beam (BNB) and the off-axis Neutrinos at the Main Injector (NuMI) beam. While the aim of the experiment is to study the low-energy excess of electron neutrino events observed by the MiniBooNE experiment, to search for eV-scale sterile neutrinos and to characterise neutrino interactions on argon, it could also be exploited to probe Beyond Standard Model (BSM) Physics. This thesis presents a BSM search for low-mass Higgs Portal Scalars at MicroBooNE in the NuMI beam using data collected during the Run 1 and Run 3 data-taking periods with a total exposure corresponding to $7.01 \times 10^{20}$ protons on target (POT). The Higgs Portal Model is an extension to the Standard Model in which a dark-sector scalar, $S$, mixes with the Higgs boson with mixing angle $\theta$ and acquires a coupling to the Standard Model fermions via the Higgs Yukawa coupling. We search for low-mass Higgs Portal scalars, in the mass range $100-200$ MeV, at MicroBooNE via the production channel $K \rightarrow \pi + S$, where scalars are produced from kaons decaying at rest in the NuMI target and beam dump, as well as kaons decaying in flight in the decay pipe of the NuMI beam. In the MicroBooNE detector, we search for the decay channel $S \rightarrow e^+e^-$, which is the only decay channel available to the scalars in the mass range of our search. The results are expressed as limits, at the 95\% confidence level, on the scalar-Higgs mixing angle $\theta$ for scalars in the mass range $100-200$ MeV.Date of Award1 Aug 2023Original languageEnglishAwarding InstitutionThe University of ManchesterSupervisorStefan Soldner-Rembold (Supervisor) & Justin Evans (Supervisor)

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2024

A Co-Registered In-Situ and Ex-Situ Dataset of Electrical, Acoustic, and CT Characteristics from Wire Arc Additive Manufacturing Process

Recent progress in sensing techniques and data analytics tools have significantly accelerated the development of Wire Arc Additive Manufacturing (WAAM) systems. This data centric approach emphasizes leveraging available data throughout the production process to optimize performance. Integration of extensive data analysis provides the opportunity to improve precision, reduce waste, and enhance the quality of produced parts. This method relies on AI/ML models and optimization techniques, which are developed using the data collected from various sources, including in-situ sensors, ex-situ imaging, and manufacturing process parameters. The quality and diversity of this data, along with the alignment between different data streams (achieved through spatiotemporal registration) are critical for the successful development of AI/ML and optimization models. In this work, we present a spatiotemporally registered dataset generated during the WAAM process of deposition of a rectangular block. The dataset includes the comprehensive description of deposition process, process parameters, in-situ collected welding characteristics, acoustic data, and X-Ray Computed Tomography analysis data for the build. Dataset A Co-Registered In-Situ and Ex-Situ Dataset of Electrical, Acoustic, and CT Characteristics from Wire Arc Additive Manufacturing Process has arisen under UT-Battelle, LLC’s Prime Contract No. DE-AC05-00OR22725 with the U.S. Department of Energy (DOE) to manage and operate the Oak Ridge National Laboratory. UT-Battelle, LLC will not assert any rights under United States law or under the Prime Contract it has in the dataset against any user of the dataset, including any copyrights or patent rights. UT-Battelle, LLC requests that attribution to the dataset is provided as academically appropriate.

42 ENGINEERING↗

DOE OSTI2021

Challenging problems of quality assurance and quality control (QA/QC) of meteorological time series data

Abstract Representativeness and quality of collected meteorological data impact accuracy and precision of climate, hydrological, and biogeochemical analyses and predictions. We developed a comprehensive Quality Assurance (QA) and Quality Control (QC) statistical framework, consisting of three major phases: Phase I—Preliminary data exploration, i.e., processing of raw datasets, with the challenging problems of time formatting and combining datasets of different lengths and different time intervals; Phase II—QA of the datasets, including detecting and flagging of duplicates, outliers, and extreme data; and Phase III—the development of time series of a desired frequency, imputation of missing values, visualization and a final statistical summary. The paper includes two use cases based on the time series data collected at the Billy Barr meteorological station (East River Watershed, Colorado), and the Barro Colorado Island (BCI, Panama) meteorological station. The developed statistical framework is suitable for both real-time and post-data-collection QA/QC analysis of meteorological datasets.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2021

SoDaH: the SOils DAta Harmonization database, an open-source synthesis of soil data from research networks, version 1.0

Data collected from research networks present opportunities to test theories and develop models about factors responsible for the long-term persistence and vulnerability of soil organic matter (SOM). Synthesizing datasets collected by different research networks presents opportunities to expand the ecological gradients and scientific breadth of information available for inquiry. Synthesizing these data is challenging, especially considering the legacy of soil data that have already been collected and an expansion of new network science initiatives. To facilitate this effort, here we present the SOils DAta Harmonization database (SoDaH; https://lter.github.io/som-website, last access: 22 December 2020), a flexible database designed to harmonize diverse SOM datasets from multiple research networks. SoDaH is built on several network science efforts in the United States, but the tools built for SoDaH aim to provide an open-access resource to facilitate synthesis of soil carbon data. Moreover, SoDaH allows for individual locations to contribute results from experimental manipulations, repeated measurements from long-term studies, and local- to regional-scale gradients across ecosystems or landscapes. Finally, we also provide data visualization and analysis tools that can be used to query and analyze the aggregated database. The SoDaH v1.0 dataset is archived and available at https://doi.org/10.6073/pasta/9733f6b6d2ffd12bf126dc36a763e0b4 (Wieder et al., 2020).

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2026

2001 National Household Travel Survey - Wisconsin Add-On

The Wisconsin add-on survey supplements the 2001 National Household Travel Survey (NHTS) with additional household samples and detailed travel behavior for an assigned travel day. The Federal Highway Administration and Wisconsin Department of Transportation conducted the survey. The NHTS ran from March 2001 through May 2002, with data collection for the Wisconsin add-on occurring May 1, 2001, to May 6, 2002. The NHTS collected detailed information on travel behavior nationwide, as well as data on the demographic and socioeconomic composition of households. State transportation departments and metropolitan planning agencies had the opportunity to purchase extra household samples as part of the NHTS add-on program. These additional samples, along with national samples collected in the add-on areas, are compiled for use in transportation planning, forecasting, and research. The Wisconsin Department of Transportation participated in the NHTS add-on program and received a total of 17,610 household samples in Wisconsin. (The total number of household samples nationwide is 26,038.)

1Hz data↗

DOE OSTI2020

Artificial Intelligence Application to D and D - 20492

As aging facilities across the DOE complex await decommissioning, there is an ongoing need to understand any changes in the structural conditions. Many of these facilities were built over 50 years ago and, in some cases, these facilities have gone beyond the expected operational lifetime. Many facilities have been placed in a state of 'cold and dark,' sitting unused and awaiting decommissioning. Especially challenging are the aging facilities that provide unique operational/production capabilities to support critical DOE missions and cannot be shut down. In any of these scenarios, the structural integrity of these facilities may become compromised as time passes. It is critical that adequate inspections be performed on a continual basis and that the data collected undergoes sufficient analysis to support timely identification of any new or worsening structural issues as well as prompt needed maintenance and repairs to maintain the facilities in a safe condition. In recent days, Artificial Intelligence (AI) [1] and its application to various domains are growing at fast speed. FIU is performing research in this area and exploring the associated technologies to solve nuclear decommissioning problems. Artificial intelligence refers to the capability of a program to autonomously act, react and adapt to the working environment. AI enables the machine to behave like humans and perform the cognitive functions such as 'learning' and 'problem solving'. AI systems gradually moving from traditional approaches (algorithms and expert systems) towards more efficient and advanced technologies (machine learning [1] and deep learning [2] [3]). AI is the study of algorithms and statistical models that is being used by computers to perform specific tasks without using explicit instructions. FIU is working to develop a pilot-scale infrastructure to implement structural health monitoring using AI technologies with focus on machine learning, deep learning. This research is focused on Computer Vision/Image Classification area of AI applications. This can also be expanded to other areas of AI related to Object Recognition and Character Recognition in images. In addition to utilizing existing data sets, FIU will collect and investigate image and video data using FIU test-bed mockups to monitor structural health of the facility. Resulting data will be processed and analyzed using machine learning/deep learning technologies. The proposed pilot system is intended to serve as a starting point to engage the DOE field sites on related data sets and their decision making needs. It is anticipated that proposed machine learning/deep learning technologies can be effectively employed using anomaly detection to solve EM challenges in surveillance and maintenance of the D and D facilities. FIU will work with research stakeholders to identify applications at various sites and other DOE facilities. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2023

Measurement of the Λ$^+_c$ Lifetime

An absolute measurement of the Λ$^+_c$ lifetime is reported using Λ$^+_c → pK^– π^+$ decays in events reconstructed from data collected by the Belle II experiment at the SuperKEKB asymmetric-energy electron-positron collider. The total integrated luminosity of the data sample, which was collected at center-of-mass energies at or near the $\textit{Υ}(4S)$ resonance, is 207.2 fb –1 . The result, $τ(Λ^+_c)$ = 203.20 ± 0.89 ± 0.77 fs , where the first uncertainty is statistical and the second

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2025

Long-term measurements of ice nucleating particles at Atmospheric Radiation Measurement (ARM) sites worldwide

Ice nucleating particles (INPs) play a critical role in cloud microphysics and precipitation formation, yet long-term, spatially extensive observational datasets remain limited. Here, we present one of the most comprehensive publicly available datasets of immersion-mode INP concentrations using a single analytical method, generated through the U.S. Department of Energy's (DOE) Atmospheric Radiation Measurement (ARM) user facility. INP filter samples have been collected across a broad range of environments – including agricultural plains, Arctic coastlines, high-elevation mountain sites, marine regions, and urban areas – via fixed observatories, mobile facility deployments, and vertically-resolved tethered balloon system operations. We describe the standardized processing and quality assurance pipeline, from filter collection and processing using the Ice Nucleation Spectrometer to final data products archived on the ARM Data Discovery portal. The dataset includes both total INP concentrations and selectively treated samples, allowing for classification of biological, organic, and inorganic INP types. It features a continuous 5-year record of INP measurements from a central U.S. site, with data collection still ongoing. Seasonal and site-specific differences in INP concentrations are illustrated through intercomparisons at −10 and −20 °C, revealing distinct regional sources and atmospheric drivers. We also outline mechanisms for researchers to access existing data, request additional sample analyses, and propose future field campaigns involving ARM INP measurements. This dataset supports a wide range of scientific applications, from observational and mechanistic studies to model development, and provides critical constraints on aerosol-cloud interactions across diverse atmospheric regimes (Creamean et al., 2024, 2020b; https://doi.org/10.5439/1770816).

Creamean, Jessie M. [Colorado State Univ., Fort Co↗

DOE OSTI2023

Genetic identification of avian samples recovered from solar energy installations

Renewable energy production and development will drastically affect how we meet global energy demands, while simultaneously reducing the impact of climate change. Although the possible effects of renewable energy production (mainly from solar- and wind-energy facilities) on wildlife have been explored, knowledge gaps still exist, and collecting data from wildlife remains (when negative interactions occur) at energy installations can act as a first step regarding the study of species and communities interacting with facilities. In the case of avian species, samples can be collected relatively easily (as compared to other sampling methods), but may only be able to be identified when morphological characteristics are diagnostic for a species. Therefore, many samples that appear as partial remains, or “feather spots”—known to be of avian origin but not readily assignable to species via morphology—may remain unidentified, reducing the efficiency of sample collection and the accuracy of patterns observed. To obtain data from these samples and ensure their identification and inclusion in subsequent analyses, we applied, for the first time, a DNA barcoding approach that uses mitochondrial genetic data to identify unknown avian samples collected at solar facilities to species. We also verified and compared identifications obtained by our genetic method to traditional morphological identifications using a blind test, and discuss discrepancies observed. Our results suggest that this genetic tool can be used to verify, correct, and supplement identifications made in the field and can produce data that allow accurate comparisons of avian interactions across facilities, locations, or technology types. We recommend implementing this genetic approach to ensure that unknown samples collected are efficiently identified and contribute to a better understanding of wildlife impacts at renewable energy projects.

14 SOLAR ENERGY↗

DOE OSTI2026

Pilot Heavy-Duty Electric Vehicle Deployment for Anchorage, Alaska, Municipal Solid Waste Collection

Through a grant awarded by the U.S. Department of Energy, the Municipality of Anchorage initiated a pilot program in their Solid Waste Services (SWS) department to add heavy-duty electric trucks to its vehicle fleet. The project involves the purchase and deployment of a Peterbilt 220EV electric box truck and two Peterbilt 520EV heavy-duty electric refuse trucks. The Alaska Center for Energy and Power at the University of Alaska Fairbanks performed data analysis. Data collected include telemetry data from both types of electric trucks, charging data from the 520EV telemetry data and a Level 2 charger, and facility-level electric use data.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2026

Pilot Heavy-Duty Electric Vehicle Deployment for Anchorage, Alaska, Municipal Solid Waste Collection

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2023

Machine Learning Automated Analysis of Enormous Synchrotron X-ray Diffraction Datasets

X-ray diffraction (XRD) data analysis can be a time-consuming and laborious task. Deep neural network (DNN) based models trained with synthetic XRD patterns have been proven to be a highly efficient, accurate, and automated method for analyzing common XRD data collected from solid samples in ambient environments. However, it remains unclear whether synthetic XRD-based models can be effective in solving micro(μ)-XRD mapping data for in situ experiments involving liquid phases, which always have lower quality and significant artifacts. In this study, we collected μ-XRD mapping data from a LaCl 3 -calcite hydrothermal fluid system and trained two categories of models to analyze the experimental XRD patterns. Here, the models trained solely with synthetic XRD patterns showed low accuracy (as low as 64%) when solving experimental μ-XRD mapping data. However, the accuracy of the DNN models significantly improved (90% or above) when we trained them with a data set containing both synthetic and a small number of labeled experimental μ-XRD patterns. This study highlights the importance of labeled experimental patterns in training DNN models to solve μ-XRD mapping data from in situ experiments involving liquid phases.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2020

Worldwide Population Estimates for Small Geographic Areas

This chapter discusses the basis for these population estimates, scope, and limitations based on experiences in development of massive global population datasets and usage of these datasets as a basis for sampling design. It presents tools and approaches for using these georeferenced population estimates for complex household survey sampling. The chapter provides a unified resource for understanding current gridded population datasets, their use in survey research, and promising areas of future work toward improved population estimates. It includes an overview of popular gridded population datasets, common methodological frameworks for developing gridded population estimates, and discusses pros and cons for selecting a gridded population dataset from a survey perspective. The chapter focuses on survey methods that use gridded population estimates specifically. It highlights differences between census population estimates and gridded population estimates that may be relevant when planning data collection. The chapter presents a case study of gridded population data and sampling methods in Nigeria.

Amer, Safaa↗

DOE OSTI2025

Open data sets for assessing photovoltaic system reliability

Photovoltaic (PV) systems have become a cornerstone of renewable energy strategies, particularly due to the significant reduction in solar power costs over the past decade. However, the long-term reliability of PV installations presents a persistent challenge, requiring the development of advanced monitoring and predictive maintenance strategies. A wide range of data types is used to evaluate the health of PV systems, including environmental conditions, electrical performance, and inspection imagery. These data enable methodologies such as machine learning (ML) models for lifetime prediction and computer vision techniques for defect detection. However, the acquisition of high-quality and comprehensive data is difficult, particularly in terms of long-term consistency and data variety. Publicly available data sets serve as valuable resources for addressing these challenges, but they often suffer from fragmentation and are difficult to access. This paper presents a comprehensive review of existing open-source data sets related to PV degradation, analyzing their features, functionalities, and potential applications. We categorize these data sets based on the specific aspects of PV system information they cover, such as environmental conditions, operational monitoring, image inspection and module materials, and propose relevant tools and ML models for processing them. In addition, we propose practices for future data collection and usage, while also discussing potential directions in data-driven research. Our aim is to enhance data utilization and publication among researchers and industry professionals, promoting a deeper understanding of the role of data in enhancing the performance and durability of PV systems.

14 SOLAR ENERGY↗

DOE OSTI2025

Opening doors to physical sample tracking and attribution in Earth and environmental sciences

Physical samples and their associated data and metadata underpin scientific discoveries across disciplines and can enable new science when appropriately archived. However, there are significant gaps in current practices and infrastructure that prevent accurate provenance tracking, reproducibility, and attribution. For most samples, descriptive metadata are often sparse, inaccessible, or absent. Samples and associated data and metadata may also be scattered across numerous physical collections, data repositories, laboratories, data files, and papers with no clear linkage or provenance tracking as new information is generated over time. The Earth Science Information Partners (ESIP) Physical Samples Curation Cluster has therefore developed guidance for scientific authors on ‘Publishing Open Research Using Physical Samples.’ This involved synthesizing existing practices, gathering community feedback, and assessing real-world examples. We identified improvements needed to enable authors to efficiently cite and link Earth science samples and related data, and track their use. Our goal is to help improve discoverability, interoperability, and reuse of physical samples, and associated data and metadata. Though primarily focused on the needs of Earth and environmental sciences, these guidelines are broadly applicable.

58 GEOSCIENCES↗

DOE OSTI2020

Planck intermediate results

We present the NPIPE processing pipeline, which produces calibrated frequency maps in temperature and polarization from data from the Planck Low Frequency Instrument (LFI) and High Frequency Instrument (HFI) using high-performance computers. NPIPE represents a natural evolution of previous Planck analysis efforts, and combines some of the most powerful features of the separate LFI and HFI analysis pipelines. For example, following the LFI 2018 processing procedure, NPIPE uses foreground polarization priors during the calibration stage in order to break scanning-induced degeneracies. Similarly, NPIPE employs the HFI 2018 time-domain processing methodology to correct for bandpass mismatch at all frequencies. In addition, NPIPE introduces several improvements, including, but not limited to: inclusion of the 8% of data collected during repointing manoeuvres; smoothing of the LFI reference load data streams; in-flight estimation of detector polarization parameters; and construction of maximally independent detector-set split maps. For component-separation purposes, important improvements include: maps that retain the CMB Solar dipole, allowing for high-precision relative calibration in higher-level analyses; well-defined single-detector maps, allowing for robust CO extraction; and HFI temperature maps between 217 and 857 GHz that are binned into 0'.9 pixels (Nside = 4096), ensuring that the full angular information in the data is represented in the maps even at the highest Planck resolutions. The net effect of these improvements is lower levels of noise and systematics in both frequency and component maps at essentially all angular scales, as well as notably improved internal consistency between the various frequency channels. Based on the NPIPE maps, we present the first estimate of the Solar dipole determined through component separation across all nine Planck frequencies. The amplitude is (3366.6 ± 2.7) μK, consistent with, albeit slightly higher than, earlier estimates. From the large-scale polarization data, we derive an updated estimate of the optical depth of reionization of τ = 0.051 ± 0.006, which appears robust with respect to data and sky cuts. There are 600 complete signal, noise and systematics simulations of the full-frequency and detector-set maps. As a Planck first, these simulations include full time-domain processing of the beam-convolved CMB anisotropies. The release of NPIPE maps and simulations is accompanied with a complete suite of raw and processed time-ordered data and the software, scripts, auxiliary data, and parameter files needed to improve further on the analysis and to run matching simulations.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2020

Online Parameter Estimation Methods for Adaptive Cruise Control Systems

Modeling Adaptive Cruise Control (ACC) vehicles enables the understanding of the impact of these vehicles on traffic flow. In this work, two online methods are used to provide real time system identification of ACC enabled vehicles. The first technique is a recursive least squares (RLS) approach, while the second method solves a nonlinear joint state and parameter estimation problem via particle filtering (PF). We provide a parameter identifiability analysis for both methods to analytically show that the model parameters are not identifiable using equilibrium driving. The accuracy and computational runtime of the online methods are compared to a commonly used offline simulation-based optimization (i.e., batch optimization) approach. The methods are tested on synthetic data as well as on empirical data collected directly from a 2019 model year ACC vehicle using data from sensors that are part of the stock ACC system. The online methods are scalable and provide comparable accuracy to the batch method. RLS runs in real time and is two orders of magnitude faster than the batch method for modest sized (e.g., 15 min) datasets. The particle filter also runs in real- time, and is also suitable in streaming applications in which the datasets can grow arbitrarily large.

33 ADVANCED PROPULSION SYSTEMS↗

DOE OSTI2020

Characterization of cubic Li$_{2}$$^{100}$MoO$_4$ crystals for the CUPID experiment

The CUPID Collaboration is designing a tonne-scale, background-free detector to search for double beta decay with sufficient sensitivity to fully explore the parameter space corresponding to the inverted neutrino mass hierarchy scenario. One of the CUPID demonstrators, CUPID-Mo, has proved the potential of enriched Li$_{2}$$^{100}$MoO$_4$ crystals as suitable detectors for neutrinoless double beta decay search. In this work, we characterised cubic crystals that, compared to the cylindrical crystals used by CUPID-Mo, are more appealing for the construction of tightly packed arrays. We measured an average energy resolution of (6.7$\pm$0.6) keV FWHM in the region of interest, approaching the CUPID target of 5 keV FWHM. We assessed the identification of $\alpha$ particles with and without a reflecting foil that enhances the scintillation light collection efficiency, proving that the baseline design of CUPID already ensures a complete suppression of this $\alpha$-induced background contribution. We also used the collected data to validate a Monte Carlo simulation modelling the light collection efficiency, which will enable further optimisations of the detector.

100Mo↗