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All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

An updated estimate of the Mu2e experiment sensitivity

The Mu2e experiment at Fermilab will search for the conversion of a negative muon into an electron inside the field of a nucleus. This process does not conserve charged-lepton flavour and is heavily suppressed in the Standard Model (SM), with a branching ratio < 10-50. Any evidence of it would be an undeniable evidence of new physics beyond the SM. The project sets out to achieve a single event sensitivity of $\sim 3 × 10^{-17}$ on the ratio between the probability for a conversion of a negative muon into an electron and the one for a muon capture by the nucleus. Such a sensitivity would represent a 4 orders of magnitude improvement on the previous upper limit for the process, making possible to test predictions of different extensions of the SM. Mu2e uses three superconducting solenoids to produce and measure the muon conversions. In the first solenoid, the Production Solenoid, pions and kaons are produced, together with other secondary products, by 8 GeV kinetic energy proton interactions in a tungsten target. A gradient magnetic field is specifically designed to direct low momentum particles into the Transport Solenoid, an S-shaped magnet that filters out particles with unwanted charge and momentum. Muons, produced by pion and kaon decays, finally reach the aluminum Stopping Target in the Detector Solenoid, where they eventually stop and convert. The result of the conversion process is a monochromatic electron of ~105 MeV/c momentum. The Detector Solenoid also hosts the two main detectors: a straw tube tracker and two CsI calorimeter disks, both providing measurement of event kinematics. A germanium detector and a LaBr crystal are located downstream of the Detector Solenoid to measure the X and gamma rays produced by the muon captures in the Stopping Target. A veto system of scintillators covering the Detector Solenoid and half of the Transport Solenoid is used to identify and reject cosmic rays interactions. With respect to the initial project, the Mu2e running plan has evolved to a staged configuration with 2 years at reduced intensity before the 2025 accelerator shutdown for the neutrino beam upgrade and 2 or 3 years at full intensity after that. This, together with geometry changes and a better knowledge of detector performances obtained by the first slice tests, has required a full revision of the signal over background selection that is the subject of this thesis. In order to achieve a new estimate of Mu2e sensitivity, the simulation of the data corresponding to the first 2 years of data acquisition has been performed. This includes both conversion electrons (CE) and the main sources of background: cosmics, decay-in orbits (DIO), radiative pion captures (RPC) and antiprotons. The characteristics of the signal and of the main backgrounds have been studied to define the best selection variables for CE. A special effort has been devoted to the evaluation of the antiproton background. The lack of experimental data for antiproton production cross section makes the systematic uncertainty on this background significant. A new parameterization of the cross section has been developed to fit the existing data and to provide a more reliable estimate of the systematic uncertainty by comparing the results of the old and the new model. A special effort has been devoted to the optimization of the antiprotons Monte Carlo generator. This study has also revealed that the dominant component within this background is represented by antiprotons produced in the opposite direction with respect to the Transport Solenoid entrance and then redirected to it by back-scattering processes in the Production Target. This ultimately highlights the sensitivity of the background estimate to the G4 handling of antiproton interactions in the 1 to 3 GeV/c momentum range. Finally, the final experimental sensitivity has been studied. The momentum and time selection have been optimized to obtain the 5-sigma discovery reach or, in case of no signal, the upper limit on conversion probability. The results confirm that, in the firs t two years of data taking, Mu2e will be able to improve the current experimental sensitivity for muon-to-electron conversion in an atomic field by more than 3 order of magnitudes.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2020

Economic Model for Estimation of GDP Losses in the MACCS Offsite Consequence Analysis Code

The MACCS (MELCOR Accident Consequence Code System) code is the U.S. Nuclear Regulatory Commission (NRC) tool used to perform probabilistic health and economic consequence assessments for atmospheric releases of radionuclides. It is also used by international organizations, both reactor owners and regulators. It is intended and most commonly used for hypothetical accidents that could potentially occur in the future rather than to evaluate past accidents or to provide emergency response during an ongoing accident. It is designed to support probabilistic risk and consequence analyses and is used by the NRC, U.S. nuclear licensees, the Department of Energy, and international vendors, licensees, and regulators. This report describes the modeling framework, implementation, verification, and benchmarking of a GDP-based model for economic losses that has recently been developed as an alternative to the original cost-based economic loss model in MACCS. The GDP-based model has its roots in a code developed by Sandia National Laboratories for the Department of Homeland Security to estimate short-term losses from natural and manmade accidents, called the Regional Economic Accounting analysis tool (REAcct). This model was adapted and modified for MACCS and is now called the Regional Disruption Economic Impact Model (RDEIM). It is based on input-output theory, which is widely used in economic modeling. It accounts for direct losses to a disrupted region affected by an accident, indirect losses to the national economy due to disruption of the supply chain, and induced losses from reduced spending by displaced workers. RDEIM differs from REAcct in its treatment and estimation of indirect loss multipliers, elimination of double counting associated with inter-industry trade in the affected area, and that it is designed to be used to estimate impacts for extended periods that can occur from a major nuclear reactor accident, such as the one that occurred at the Fukushima Daiichi site in Japan. Most input-output models do not account for economic adaptation and recovery, and in this regard RDEIM differs from its parent, REAcct, because it allows for a user-definable national recovery period. Implementation of a recovery period was one of several recommendations made by an independent peer review panel to ensure that RDEIM is state-of-practice. For this and several other reasons, RDEIM differs from REAcct. Both the original and the RDEIM economic loss models account for costs from evacuation and relocation, decontamination, depreciation, and condemnation. Where the original model accounts for an expected rate of return, based on the value of property, that is lost during interdiction, the RDEIM model instead accounts for losses of GDP based on the industrial sectors located within a county. The original model includes costs for disposal of crops and milk that the RDEIM model currently does not, but these costs tend to contribute insignificantly to the overall losses. This document discusses three verification exercises to demonstrate that the RDEIM model is implemented correctly in MACCS. It also describes a benchmark study at five nuclear power plants chosen to represent the spectrum of U.S. commercial sites. The benchmarks provide perspective on the expected differences between the RDEIM and the original cost-based economic loss models. The RDEIM model is shown to consistently predict larger losses than the original model, probably in part because it accounts for national losses by including indirect and induced losses; whereas, the original model only accounts for regional losses. Nonetheless, the RDEIM model predicts losses that are remarkably consistent with the original cost-based model, differing by 16% at most for the five sites combined with three source terms considered in this benchmark.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2020

Performance Analysis and Radiation Damage Estimation of the Stopping Target Monitor Detectors in Mu2e

The Mu2e experiment aims to search for the charged lepton flavour violating (CLFV) process of a coherent, neutrinoless, conversion of a muon into an electron within the proximity of an aluminium nucleus. Mu2e seeks to measure the ratio (R μe ) of the rate of this conversion process, relative to that of ordinary muon capture. Mu2e will achieve world-leading sensitivity, improving the current limit of Rμe < 7 ×10 -13 (90% C.L.),set by the SINDRUM-II experiment, by an order of 104. This corresponds to a single-event sensitivity of Rμe < 2.87 × 10 -17 (90% C.L.). Many beyond Standard Model (BSM) theories require CLFV to occur at a rate accessible by Mu2e. Any observation of CLFV at Mu2e would have profound implications on particle physics. The Stopping Target Monitor (STM) will be comprised of both a HPGe and a LaBr3 detector which will monitor the signals of photons produced in stopped-muon processes to a required accuracy of 10%. To achieve the desired sensitivity, it is imperative that the detectors perform at their optimal level. The STM detectors are placed within a harsh radiation environment in the form of a high energy ‘flash’ of gamma radiation as well as a flux of fast neutrons. Estimations of radiation damage to HPGe in literature is limited. This thesis reports an approach which utilizes the Kinetic-Energy Released in Matter (KERMA) parameter that considers the energy dependence of the incident flux and the cross-section information for the incident particle. Resampling schemes have been adopted to give a realistic rate over the experiment run time. The results have then been normalized to the radiation damage caused by a 252 Cf source. The estimated time before annealing of the detector is necessary is found to be 75 and 78 months in the case of neutron damage and τ ~ 24 years, 11 months in the case of electron/positron induced damage. This shows significant improvement on the previous estimations.

36 MATERIALS SCIENCE↗
DOE OSTI2022

E-Area Low Level Waste Generator Inventory Uncertainty Estimation

This report describes the method used to estimate the uncertainty and bias in the low-level waste inventory, as reported by Waste Generators, for waste disposed in the E-Area Low-Level Waste Facility at SRS. The waste cut uncertainty and bias is summed to calculate the current total uncertainty and bias in each disposal unit in the E-Area Low-Level Waste Facility. This information is used to estimate the future uncertainty and bias when all disposal units are closed. For most waste cuts, there are two areas of inventory uncertainty and bias: in the isotopic characterization of the waste stream and in the measurement of total waste cut activity. Waste stream characterization is accomplished through a combination of process knowledge and analytical measurements. This characterization is documented by a technical baseline that describes the relative quantity of each isotope present in the waste stream. The total activity of each individual waste cut is usually measured directly or is calculated through a ratio of activity per weight or activity per volume. The waste cut total activity is multiplied by the waste stream isotopic fraction to calculate the total isotopic activity present in the waste cut. The waste cut uncertainty and bias is calculated by analyzing the uncertainties and biases in the waste stream characterization and waste cut measurements and adding the uncertainties in quadrature and multiplying the reported isotopic activities by the bias factors. The current isotopic inventory, uncertainty, and bias of a disposal unit is the sum of the currently disposed individual waste cut isotopic activities, uncertainties, and biases. The future uncertainty and bias is estimated based on the average “worst-case” (largest) uncertainties and biases of currently closed and operational disposal units. This report details the method, data sources, and calculation techniques used to calculate these values. It also presents some of the difficulties in working with the reported data and gives examples of the calculations and analysis. The software used to carryout the analysis is included and the location of the complete individual analyses is given. After discussion with waste generators, examination of multiple waste characterization documents, and a review of SRS Manual 1S, the reported ELLWF radionuclide activities are clearly conservative and likely represent reasonable upper bounds for the true activities. However, quantifying the bias and uncertainty in each waste cut requires detailed analysis of the documentation for each waste cut measurement and waste stream characterization.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2022

Support Vector Machines for Estimating Decision Boundaries with Numerical Simulations

Many engineering design problems can be formulated as decisions between two possible options. This is the case, for example, when a quantity of interest must be maintained below or above some threshold. The threshold thereby determines which input parameters lead to which option, and creates a boundary between the two options known as the decision boundary. This report details a machine learning approach for estimating decision boundaries, based on support vector machines (SVMs), that is amenable to large scale computational simulations. Because it is computationally expensive to evaluate each training sample, the approach iteratively estimates the decision boundary in a manner that requires relatively few training samples to glean useful estimates. The approach is then demonstrated on three example problems from structural mechanics and heat transport.

25 ENERGY STORAGE↗
DOE OSTI2022

Deep learning-based spatio-temporal estimate of greenhouse gas emissions using satellite data

Accurate estimation of greenhouse gases (GHGs) emissions is very important for developing mitigation strategies to climate change by controlling and reducing GHG emissions. This project aims to develop multiple deep learning approaches to estimate anthropogenic greenhouse gas emissions using multiple types of satellite data. NO2 concentration is chosen as an example of GHGs to evaluate the proposed approach. Two sentinel satellites (sentinel-2 and sentinel-5P) provide multiscale observations of GHGs from 10-60m resolution (sentinel-2) to ~kilometer scale resolution (sentinel-5P). Among multiple deep learning (DL) architectures evaluated, two best DL models demonstrate that key features of spatio-temporal satellite data and additional information (e.g., observation times and/or coordinates of ground stations) can be extracted using convolutional neural networks and feed forward neural networks, respectively. In particular, irregular time series data from different NO 2 observation stations limit the flexibility of long short-term memory architecture, requiring zero-padding to fill in missing data. However, deep neural operator (DNO) architecture can stack time-series data as input, providing the flexibility of input structure without zero-padding. As a result, the DNO outperformed other deep learning architectures to account for time-varying features. Overall, temporal patterns with smooth seasonal variations were predicted very well, while frequent fluctuation patterns were not predicted well. In addition, uncertainty quantification using conformal inference method is performed to account for prediction ranges. Overall, this research will lead to a new groundwork for estimating greenhouse gas concentrations using multiple satellite data to enhance our capability of tracking the cause of climate change and developing mitigation strategies.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Electrodynamic Shaker Capability Estimation Through Experimental Dynamic Substructuring [Thesis]

Electrodynamic shaker systems are an essential tool in shock and vibration testing of dynamic environments. However, the specific performance capability of these systems is difficult to characterize. The dynamics of the shaker itself, the device under test and the specific test configuration used all couple to create a dynamic response unique to each test. Poorly predicted limitations in shaker capability affect the ability to achieve test specifications, delay testing schedules, and create difficulties for choosing test equipment. To predict shaker capability for a specific test configuration prior to setup, a lumped parameter model of the shaker system and a modal model of a device under test was developed. These models were then analytically coupled using LaGrange multiplier frequency based substructuring to estimate their coupled frequency response functions. The coupled frequency response functions were used to predict electrical inputs required to meet a given test specification. These input requirements were finally compared to a validation test using the specification and setup. Input requirements estimated using the substructuring estimated frequency response functions showed significant error. However, results using an ideal frequency response function showed very little error. These results indicate that with a better method of experimental dynamic substructuring employed it would be possible to accurately predict shaker capability for a given test configuration prior to setup.

42 ENGINEERING↗
DOE OSTI2024

Graph Neural Networks for Parameterized Quantum Circuits Expressibility Estimation (Rev.1)

Parameterized quantum circuits (PQCs) are fundamental to quantum machine learning (QML), quantum optimization, and variational quantum algorithms (VQAs). The expressibility of PQCs is a measure that determines their capability to harness the full potential of the quantum state space. It is thus a crucial guidepost to know when selecting a particular PQC ansatz. However, the existing technique for expressibility computation through statistical estimation requires a large number of samples, which poses significant challenges due to time and computational resource constraints. This paper introduces a novel approach for expressibility estimation of PQCs using Graph Neural Networks (GNNs). We demonstrate the predictive power of our GNN model with a dataset consisting of 25,000 samples from the noiseless IBM QASM Simulator and 12,000 samples from three distinct noisy quantum backends. The model accurately estimates expressibility, with root mean square errors (RMSE) of 0.05 and 0.06 for the noiseless and noisy backends, respectively. We compare our model’s predictions with reference circuits from Sim et al. and IBM Qiskit’s hardwareefficient ansatz sets to further evaluate our model’s performance. Our experimental evaluation in noiseless and noisy scenarios reveals a close alignment with ground truth expressibility values, highlighting the model’s efficacy. Moreover, our model exhibits promising extrapolation capabilities, predicting expressibility values with low RMSE for out-of-range qubit circuits trained solely on only up to 5-qubit circuit sets. This work thus provides a reliable means of efficiently evaluating the expressibility of diverse PQCs on noiseless simulators and hardware.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

A Refined Method to Translate Solar Data Quality Assessment Flags to Estimated Measurement Uncertainty

Integrating solar resource uncertainties due to radiometer measurement performance and operational data quality assessment can provide improved estimates of economic bankability, system design performance, and compliance of solar energy conversion systems. Estimating radiometer measurement uncertainty is an established procedure consistent with recognized best practices and international guidelines. SERI QC is a robust solar data quality assessment software tool that has been in continuous use for more than three decades. This report, the fourth of six for the Data Quality and Uncertainty Integration Project, presents a refined algorithm description for software to translate solar resource data quality assessment results into estimated uncertainty values in a Solar Resource Operational Uncertainty Integrator (SROUI) application. This algorithm requires three-component solar irradiance measurements - global horizontal irradiance, direct normal irradiance, and diffuse horizontal irradiance - collected at 1- to 60-minute intervals, as described in the previous deliverables. The development of this report as Deliverable 6.4 was an iterative process that included reviews and feedback from the project team on initial drafts designed to refine how the new software could best support determining solar resource data uncertainty. The results of this effort will contribute to the final software system development by National Renewable Energy Laboratory staff.

14 SOLAR ENERGY↗
DOE OSTI2023

Notes on Regression Analysis for Radar Parameter Estimation

A fundamental task of radar, beyond merely detecting a target, is to estimate some parameters associated with it. For example, this might include range, direction, velocity, etc. In any case, multiple measurements, often noisy, need to be processed to yield a ‘best estimate’ of the parameter. A common mathematical method for doing so is called “Regression” analysis. The goal is to minimize the expected squared error in the estimate. Even when alternate algorithms are considered, the least squared-error regression analysis is the benchmark against which alternatives are compared.

47 OTHER INSTRUMENTATION↗
DOE OSTI2024

Technoeconomic Evaluation of Microreactor Using Detailed Bottom-up Estimate (Rev.1)

Microreactors are a novel class of nuclear reactors that are expected to be factory produced, transportable, and self-regulating. They are expected to be several orders of magnitude smaller in size than traditional reactors (with power outputs in the 1–20 MWe range typically). They are primarily envisaged to target niche, remote markets that are difficult to access and where energy costs are high. There has been a scarcity of technoeconomic assessment for these types of reactors due to the scarcity of designs without proprietary restraints (e.g., those that include balance of plants and building layouts) and cost estimates. The primary objective of this report was to develop a transparent, detailed, bottom-up cost estimate for a microreactor. While there is a high degree of uncertainty associated with the projected costs, this work provides a foundation that can built upon and improved to better model the economics of microreactors. The Microreactor Applications Research, Validation, and Evaluation (MARVEL) microreactor was selected for this analysis. This Department of Energy–sponsored demonstration was chosen because (1) its final design was recently completed and (2) a detailed class-3 cost-and-schedule estimation was conducted for it. This provided a strong technical basis for further analysis.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2025

Analysis of Warped April Tag Impacts on Detection and Pose Estimation

This report evaluates the impact of geometric deformation on an April Tag, particularly when warped due to attachment on a curved surface, on its detectability and pose estimation performance. A comparative analysis was conducted using a flat April Tag as a control under identical experimental conditions, which involved recording video sequences with varying viewing angles. For detectability, the warped tag exhibited consistent detection failures at viewing angles beyond 40° and complete failures beyond 60°, whereas the flat tag maintained reliable detection across all angles. For pose estimation, measured by pose jitter (variation in rotation and translation), differences between the warped and flat tags were minimal and statistically insignificant, indicating robust performance even for the deformed tag. These findings suggest that while geometric warping reduces an April Tag’s detectability, its pose estimation accuracy remains relatively unaffected under the tested conditions.

42 ENGINEERING↗
DOE OSTI2026

Energy Cost Estimate Tool v2.0: Methodology and Usage

Information about home energy costs is essential for appraisers, lenders, and buyers, but reliable data are often unavailable in real estate transactions. To fill this gap, the National Laboratory of the Rockies developed the Energy Cost Estimate (ECE) tool. The tool provides flexible, data-driven estimates of annual household energy use and costs across the contiguous United States, using only a small number of inputs typically found in mortgage appraisals. This report presents the methodology behind version 2.0 of the ECE tool, demonstrates how users can generate estimates through its interface, and discusses its applications and limitations.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2026

Myriad World Baseline: Global Geodemographic Estimates

The LandScan Myriad World Baseline (MWB) method produces global, residential (nighttime/home-location) gridded geodemographic estimates based on 5-year age/gender cohorts—at 30-arcsecond (≈1 km) resolution. MWB is designed to fill gaps where detailed, georeferenced survey data (e.g., Demographic and Health Surveys (DHS)) are missing or outdated, and to provide a baseline that can support human security analysis, including consequence assessment, “patterns of life” modeling, and scenario-based population futures. MWB’s workflow spatializes household-level age/gender characteristics from the GLOPOP-S dataset by conflating household and gridded expected relative wealth adapted from Global Gridded Relative Deprivation Index (GRDI), then adjusts them to a target year of interest. Age/gender estimates are then applied to harmonize lowest-administrative-level statistics with LandScan residential counts, yielding final geodemographic estimates. Two validation case studies are presented: Ghana (2021) and Tokyo/Kanagawa, Japan (2020), illustrating spatial variability in demographic cohorts and comparing MWB outputs to official gridded statistics. Results show close overall alignment relative to validation criteria including population pyramids and age-dependency ratios.

Tuccillo, Joe [ORNL] (ORCID:0000000259300943)↗
DOE OSTI2023

Simultaneous estimation of multiple eigenvalues with short-depth quantum circuit on early fault-tolerant quantum computers

We introduce a multi-modal, multi-level quantum complex exponential least squares (MM-QCELS) method to simultaneously estimate multiple eigenvalues of a quantum Hamiltonian on early fault-tolerant quantum computers. Our theoretical analysis demonstrates that the algorithm exhibits Heisenberg-limited scaling in terms of circuit depth and total cost. Notably, the proposed quantum circuit utilizes just one ancilla qubit, and with appropriate initial state conditions, it achieves significantly shorter circuit depths compared to circuits based on quantum phase estimation (QPE). Numerical results suggest that compared to QPE, the circuit depth can be reduced by around two orders of magnitude under several settings for estimating ground-state and excited-state energies of certain quantum systems.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Simultaneous Forward-Backward Prony Estimation

Power system dynamic stability can be evaluated through the analysis of transient oscillations that occur following significant system events. One of the earliest methods for this type of study is Prony analysis, which estimates the system's electromechanical modes. While previous studies have highlighted advantages of performing Prony analysis on data in the forward and backward directions, the proposed method does so simultaneously. As a result, signal poles corresponding to electromechanical modes can be distinguished from spurious poles more reliably. The method also produces a single mode estimate, where independent application in the forward and backward directions would produce two estimates for each mode. The method is validated using simulated and measured power system data.

Follum, James D.↗
DOE OSTI2025

Optimal intrinsic alignment estimators in the presence of redshift-space distortions

We present estimators for quantifying intrinsic alignments in large spectroscopic surveys that efficiently capture line-of-sight (LOS) information while being relatively insensitive to redshift-space distortions (RSD). We demonstrate that changing the LOS integration range, pimax, as a function of transverse separation outperforms the conventional choice of a single pimax value. This is further improved by replacing the flat pimax cut with a LOS weighting based on shape projection and RSD. Although these estimators incorporate additional LOS information, they are projected correlations that exhibit signal-to-noise ratios comparable to 3D correlation functions, such as the IA quadrupole. Using simulations from Abacus Summit, we evaluate these estimators and provide recommended pimax values and weights for projected separations of 1 - 100 Mpc/h. These will improve measurements of intrinsic alignments in large cosmological surveys and the constraints they provide for both weak lensing and direct cosmological applications.

cosmology↗
DOE OSTI2023

Automating methods for estimating metabolite volatility

The volatility of metabolites can influence their biological roles and inform optimal methods for their detection. Yet, volatility information is not readily available for the large number of described metabolites, limiting the exploration of volatility as a fundamental trait of metabolites. Here, we adapted methods to estimate vapor pressure from the functional group composition of individual molecules (SIMPOL.1) to predict the gas-phase partitioning of compounds in different environments. We implemented these methods in a new open pipeline called volcalc that uses chemoinformatic tools to automate these volatility estimates for all metabolites in an extensive and continuously updated pathway database: the Kyoto Encyclopedia of Genes and Genomes (KEGG) that connects metabolites, organisms, and reactions. We first benchmark the automated pipeline against a manually curated data set and show that the same category of volatility (e.g., nonvolatile, low, moderate, high) is predicted for 93% of compounds. We then demonstrate how volcalc might be used to generate and test hypotheses about the role of volatility in biological systems and organisms. Specifically, we estimate that 3.4 and 26.6% of compounds in KEGG have high volatility depending on the environment (soil vs. clean atmosphere, respectively) and that a core set of volatiles is shared among all domains of life (30%) with the largest proportion of kingdom-specific volatiles identified in bacteria. With volcalc , we lay a foundation for uncovering the role of the volatilome using an approach that is easily integrated with other bioinformatic pipelines and can be continually refined to consider additional dimensions to volatility. The volcalc package is an accessible tool to help design and test hypotheses on volatile metabolites and their unique roles in biological systems.

59 BASIC BIOLOGICAL SCIENCES↗