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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Evaluating Radiation Impact on Transmon Qubits in Above and Underground Facilities

Superconducting qubits can be sensitive to abrupt energy deposits caused by cosmic rays and ambient radioactivity. Previous studies have focused on understanding possible correlated effects over time and distance due to cosmic rays. In this study, for the first time, we directly compare the response of a transmon qubit measured initially at the Fermilab SQMS above-ground facilities and then at the deep underground Gran Sasso Laboratory (INFN-LNGS, Italy). We observe same average qubit lifetime T$_1$ of roughly 80 microseconds at above and underground facilities. We then apply a fast decay detection protocol and investigate the time structure, sensitivity and relative rates of triggered events due to radiation versus intrinsic noise, comparing above and underground performance of several high-coherence qubits. Using gamma sources of variable activity we calibrate the response of the qubit to different levels of radiation in an environment with minimal background radiation. Results indicate that qubits respond to a strong gamma source and it is possible to detect particle impacts. However, when comparing above and underground results, we do not observe a difference in radiation induced-like events for these sapphire and niobium-based transmon qubits. We conclude that the majority of these events are not radiation related and to be attributed to other noise sources which by far dominate single qubit errors in modern transmon qubits.

Roy, Tanay↗
DOE OSTI2021

Methods, media, and systems for detecting attack on a digital processing device

Methods, media, and systems for detecting attack are provided. In some embodiments, the methods include: comparing at least part of a document to a static detection model; determining whether attacking code is included in the document based on the comparison of the document to the static detection model; executing at least part of the document; determining whether attacking code is included in the document based on the execution of the at least part of the document; and if attacking code is determined to be included in the document based on at least one of the comparison of the document to the static detection model and the execution of the at least part of the document, reporting the presence of an attack. In some embodiments, the methods include: selecting a data segment in at least one portion of an electronic document; determining whether the arbitrarily selected data segment can be altered without causing the electronic document to result in an error when processed by a corresponding program; in response to determining that the arbitrarily selected data segment can be altered, arbitrarily altering the data segment in the at least one portion of the electronic document to produce an altered electronic document; and determining whether the corresponding program produces an error state when the altered electronic document is processed by the corresponding program.

Stolfo, Salvatore J.↗
DOE OSTI2023

Count Every Trip: Finding the Uncertainty in Energy Estimates Made from Inferred Travel Modes

To properly inform transport policy and infrastructure changes, transportation related metrics need both measured values and uncertainties of those values. Travel monitoring smartphone apps can record people's travel behavior, but trip data quality is limited by sensor errors, user labeling rates and the accuracy of inference algorithms used for travel diary creation. We discuss the use of phone app recorded travel diary data to estimate energy consumption, and propose the use of propagation of variance to find error bars for such estimates. We define energy consumption for one trip as trip length times the energy intensity per distance unit of the travel mode used. We characterize trip length errors with relative error and inferred trip mode errors with confusion matrix columns. The resulting variances of each measurement are then propagated to the final calculated energy consumption. We tested our uncertainty methods on a dataset that used phone app data combined with prompted recall, consisting of 92,234 labeled trips for over 500,000 miles. Accounting for uncertainty using expected energy intensities and variance propagation gives a dataset-wide aggregate energy consumption percent error of about 9%, within one standard deviation from the truth. Future work could involve applying similar methods to other travel diary based metrics.

ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2021

Neutrinos in N -body simulations

In the next decade, cosmological surveys will have the statistical power to detect the absolute neutrino mass scale. N-body simulations of large-scale structure formation play a central role in interpreting data from such surveys. Yet these simulations are Newtonian in nature. We provide a quantitative study of the limitations to treating neutrinos, implemented as N-body particles, in N-body codes, focusing on the error introduced by neglecting special relativistic effects. Special relativistic effects are potentially important due to the large thermal velocities of neutrino particles in the simulation box. We derive a self-consistent theory of linear perturbations in Newtonian and nonrelativistic neutrinos and use this to demonstrate that N-body simulations overestimate the neutrino free-streaming scale, and cause errors in the matter power spectrum that depend on the initial redshift of the simulations. For z i ≲ 100, and neutrino masses within the currently allowed range, this error is ≲ 0.5%, though represents an up to ~10 % correction to the shape of the neutrino-induced suppression to the cold dark matter power spectrum. Here, we argue that the simulations accurately model nonlinear clustering of neutrinos so that the error is confined to linear scales.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2023

Photogrammetry: Develop methods for detecting deviations from expected geometry of components in a glovebox environment (Lawrence Livermore National Laboratory Aging and Lifetimes Program FY23, Milestone 8651, Grading Criterion #4)

Measurements in glovebox environments are challenging to conduct. Currently deployed techniques are time consuming and provide limited information. These limitations make current disposition and future assessments of products challenging. In FY23 we developed methods for detecting deviations of components in a glovebox environment that address these shortcomings by rapidly collecting information-dense measurements. Conducted tests demonstrate that camera pose (position and orientation) can be derived for images utilized in the photogrammetry process. This information, in conjunction with common image processing techniques, enables automated detection and size estimation of surface features. First, image processing is used to identify marks, or localized regions, that standout from the surrounding area. Then, the physical size of the region can be estimated because scale can be determined for photogrammetry images. Development of this methods shows that photogrammetry can identify measurable deviations from expected geometries. Although measurement error when using this technique through a glovebox window still needs to be reduced, this capability shows promise for eliminating or reducing the use of tedious manual measurements.

42 ENGINEERING↗
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 OSTI2020

Planck 2018 results - VIII. Gravitational lensing

We present measurements of the cosmic microwave background (CMB) lensing potential using the final Planck 2018 temperature and polarization data. Using polarization maps filtered to account for the noise anisotropy, we increase the significance of the detection of lensing in the polarization maps from 5σ to 9σ. Combined with temperature, lensing is detected at 40σ. We present an extensive set of tests of the robustness of the lensing-potential power spectrum, and construct a minimum-variance estimator likelihood over lensing multipoles 8 ≤ L ≤ 400 (extending the range to lower L compared to 2015), which we use to constrain cosmological parameters. We find good consistency between lensing constraints and the results from the Planck CMB power spectra within the ΛCDM model. Combined with baryon density and other weak priors, the lensing analysis alone constrains σ 8 Ωm 0.25 = 0.589 ± 0.020 (1σ errors). Also combining with baryon acoustic oscillation data, we find tight individual parameter constraints, σ8 = 0.811 ± 0.019, H0 = 67.9-1.3+1.2 km s -1 Mpc -1 , and Ω m = 0.303 -0.018 +0.016 . Combining with Planck CMB power spectrum data, we measure σ 8 to better than 1% precision, finding σ 8 = 0.811 ± 0.006. CMB lensing reconstruction data are complementary to galaxy lensing data at lower redshift, having a different degeneracy direction in σ 8 - Ω m space; we find consistency with the lensing results from the Dark Energy Survey, and give combined lensing-only parameter constraints that are tighter than joint results using galaxy clustering. Using the Planck cosmic infrared background (CIB) maps as an additional tracer of high-redshift matter, we make a combined Planck-only estimate of the lensing potential over 60% of the sky with considerably more small-scale signal. We additionally demonstrate delensing of the Planck power spectra using the joint and individual lensing potential estimates, detecting a maximum removal of 40% of the lensing-induced power in all spectra. The improvement in the sharpening of the acoustic peaks by including both CIB and the quadratic lensing reconstruction is detected at high significance.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

Performance of methods for SARS-CoV-2 variant detection and abundance estimation within mixed population samples

The accurate identification of SARS-CoV-2 (SC2) variants and estimation of their abundance in mixed population samples (e.g., air or wastewater) is imperative for successful surveillance of community level trends. Assessing the performance of SC2 variant composition estimators (VCEs) should improve our confidence in public health decision making. Here, we introduce a linear regression based VCE and compare its performance to four other VCEs: two re-purposed DNA sequence read classifiers (Kallisto and Kraken2), a maximum-likelihood based method (Lineage deComposition for Sars-Cov-2 pooled samples (LCS)), and a regression based method (Freyja). We simulated DNA sequence datasets of known variant composition from both Illumina and Oxford Nanopore Technologies (ONT) platforms and assessed the performance of each VCE. We also evaluated VCEs performance using publicly available empirical wastewater samples collected for SC2 surveillance efforts. Bioinformatic analyses were performed with a custom NextFlow workflow (C-WAP, CFSAN Wastewater Analysis Pipeline). Relative root mean squared error (RRMSE) was used as a measure of performance with respect to the known abundance and concordance correlation coefficient (CCC) was used to measure agreement between pairs of estimators. Based on our results from simulated data, Kallisto was the most accurate estimator as it had the lowest RRMSE, followed by Freyja. Kallisto and Freyja had the most similar predictions, reflected by the highest CCC metrics. We also found that accuracy was platform and amplicon panel dependent. For example, the accuracy of Freyja was significantly higher with Illumina data compared to ONT data; performance of Kallisto was best with ARTICv4. However, when analyzing empirical data there was poor agreement among methods and variations in the number of variants detected (e.g., Freyja ARTICv4 had a mean of 2.2 variants while Kallisto ARTICv4 had a mean of 10.1 variants). This work provides an understanding of the differences in performance of a number of VCEs and how accurate they are in capturing the relative abundance of SC2 variants within a mixed sample (e.g., wastewater). Such information should help officials gauge the confidence they can have in such data for informing public health decisions.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2023

ADoSPSiQS (Automated Detection of Symmetry-Protected Subspaces in Quantum Simulations ) [SWR-23-55]

Symmetry-protected subspaces in quantum systems are subspaces demarcated by a conserved quantity of that quantum system. We introduce two classical algorithms, which efficiently compute and elucidate features of these subspaces. The first algorithm explores the entire symmetry-protected subspace of an initial state in time complexity linear to the size of the subspace by closing local basis state-to-basis state transitions. The second algorithm determines, with bounded error, if a given measurement outcome of a dynamically-generated state is within the symmetry-protected subspace of the state in which the dynamical system is initialized. This software is paired with the paper of the same name at https://arxiv.org/pdf/2302.08586.pdf.

Rotello, Caleb↗
DOE OSTI2025

Efficient screening of rare large pit anomalies on polished surfaces using a minimalist sampling scheme

Lawrence Livermore National Laboratory (LLNL) has made significant strides in generating clean energy through its inertial confinement fusion (ICF) experiments. These experiments rely on high-density carbon (HDC) coated shells to encapsulate the fusion fuel. The success of these experiments is heavily dependent on the surface quality of these shells, as even minor imperfections, such as deep pits, can negatively impact fusion yield. Ensuring the required smoothness involves an extensive surface-finishing process that spans approximately 20 stages, making it both time-intensive and resource-demanding. A critical challenge in this process is the need for high-resolution scans to detect rare deep pits, which can be costly and impractical if performed on every shell. This highlights the necessity of developing more efficient scanning methods to optimize time and cost without compromising accuracy. To address these challenges, we introduce a novel approach that employs the multivariate Dvoretzky–Kiefer–Wolfowitz (DKW) inequality to provide a probabilistic upper bound on the error in estimating pit distribution characteristics via a Kernel Density Estimator (KDE). This error bound enables efficient and reliable estimation of pit distribution characteristics at a specified statistical confidence level using a minimal number of surface scans. The integrated DKW-KDE approach was validated through surface-finishing experiments across two batches of HDC-coated shells, demonstrating consistent and robust performance across multiple stages of the surface-finishing experiments. The validation studies suggest that the integrated DKW-KDE approach achieves comparable accuracy in estimating the risk of deleterious large pits with six scans, thus conserving time and resources. Further evaluations show that performance remains consistent across batches and over multiple polishing stages. In conclusion, based on these findings, one can leverage the minimal-scan insights to strategically improve the bottleneck inspection process, thus enhancing the productivity and quality of shell polishing and similar challenging manufacturing processes.

Inertial confinement fusion↗
DOE OSTI2024

Evaluating Radiation Impact on Transmon Qubits Using a Fast Decay Protocol in Above and Underground Laboratories

Superconducting qubits can be sensitive to sudden energy deposits caused by ambient radioactivity and cosmic rays. Previous studies have focused on understanding possible correlated effects over time and distance due to this radiation. In this study‚ for the first time‚ we directly compare the response of a transmon qubit measured initially at the SQMS above-ground facility (Fermilab‚ Illinois‚ USA) and then at the deep underground Gran Sasso Laboratory (INFN-LNGS‚ Italy). We observe the same average qubit lifetime of roughly 80 microseconds at both facilities. We then apply a fast decay detection protocol and investigate the time structure and relative rates of triggered events due to radiation versus intrinsic noise‚ comparing the above and underground performance of several high-coherence qubits. Using gamma sources of variable intensity we calibrate the response of the qubit to different levels of radiation in an environment with minimal background radiation. Results indicate that qubits respond to a strong gamma source‚ and it is possible to detect particle impacts. However‚ we do not observe a difference in radiation-induced-like events when comparing the above and underground results for niobium-based transmon qubits with sapphire substrates. We conclude that the majority of these events are not radiation-related and are attributed to other noise sources‚ which by far dominate single-qubit errors in modern transmon qubits. [1] Dominicis‚ Roy et al. arXiv:2405.18355

Roy, Tanay↗
DOE OSTI2021

Extreme metrics from large ensembles: investigating the effects of ensemble size on their estimates

Abstract. We consider the problem of estimating the ensemble sizes required to characterize the forced component and the internal variability of a number of extreme metrics. While we exploit existing large ensembles, our perspective is that of a modeling center wanting to estimate a priori such sizes on the basis of an existing small ensemble (we assume the availability of only five members here). We therefore ask if such a small-size ensemble is sufficient to estimate accurately the population variance (i.e., the ensemble internal variability) and then apply a well-established formula that quantifies the expected error in the estimation of the population mean (i.e., the forced component) as a function of the sample size n, here taken to mean the ensemble size. We find that indeed we can anticipate errors in the estimation of the forced component for temperature and precipitation extremes as a function of n by plugging into the formula an estimate of the population variance derived on the basis of five members. For a range of spatial and temporal scales, forcing levels (we use simulations under Representative Concentration Pathway 8.5) and two models considered here as our proof of concept, it appears that an ensemble size of 20 or 25 members can provide estimates of the forced component for the extreme metrics considered that remain within small absolute and percentage errors. Additional members beyond 20 or 25 add only marginal precision to the estimate, and this remains true when statistical inference through extreme value analysis is used. We then ask about the ensemble size required to estimate the ensemble variance (a measure of internal variability) along the length of the simulation and – importantly – about the ensemble size required to detect significant changes in such variance along the simulation with increased external forcings. Using the F test, we find that estimates on the basis of only 5 or 10 ensemble members accurately represent the full ensemble variance even when the analysis is conducted at the grid-point scale. The detection of changes in the variance when comparing different times along the simulation, especially for the precipitation-based metrics, requires larger sizes but not larger than 15 or 20 members. While we recognize that there will always exist applications and metric definitions requiring larger statistical power and therefore ensemble sizes, our results suggest that for a wide range of analysis targets and scales an effective estimate of both forced component and internal variability can be achieved with sizes below 30 members. This invites consideration of the possibility of exploring additional sources of uncertainty, such as physics parameter settings, when designing ensemble simulations.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Assessing Historical Variability of South Asian Monsoon Lows and Depressions With an Optimized Tracking Algorithm

Abstract Cyclonic low‐pressure systems (LPS) produce abundant rainfall in South Asia, where they are traditionally categorized as monsoon lows, monsoon depressions, and more intense cyclonic storms. The India Meteorological Department (IMD) has tracked monsoon depressions for over a century, finding a large decline in their number in recent decades, but their methods have changed over time and do not include monsoon lows. This study presents a fast, objective algorithm for identifying monsoon LPS and uses it to assess interannual variability and trends in reanalyses. Variables and thresholds used in the algorithm are selected to best match a subjectively analyzed LPS data set while minimizing disagreement between four reanalyses in a training period. The stream function of 850 hPa horizontal wind is found to be optimal in this sense; it is less noisy than vorticity and represents the complete nondivergent wind, even when flow is not geostrophic. Using this algorithm, LPS statistics are computed for five reanalyses, and none show a detectable trend in monsoon depression counts since 1979. Both the Japanese 55‐year Reanalysis (JRA‐55) and the IMD data set show a step‐like reduction in depression counts when they began using geostationary satellite data, in 1979 and 1982, respectively; the 1958–2018 linear trend in JRA‐55, however, is smaller than in the IMD data set, and its error bar includes 0. There are more LPS in seasons with above‐average monsoon rainfall and in La Niña years, but few other large‐scale modes of interannual variability are found to modulate LPS counts, lifetimes, or track length consistently across reanalyses.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Statistics and sensitivity of axion wind detection with the homogeneous precession domain of superfluid helium-3

The homogeneous precession domain (HPD) of superfluid He 3 has recently been identified as a detection medium which might provide sensitivity to the axion-nucleon coupling g a N N competitive with, or surpassing, existing experimental proposals. In this work, we make a detailed study of the statistical and dynamical properties of the HPD system in order to make realistic projections for a full-fledged experimental program. We include the effects of clock error and measurement error in a concrete readout scheme using superconducting qubits and quantum metrology. This work also provides a more general framework to describe the statistics associated with the axion gradient coupling through the treatment of a transient resonance with a nonstationary background in a time-series analysis. Incorporating an optimal data-taking and analysis strategy, we project a sensitivity approaching g a N N ∼ 10 − 12 GeV − 1 across a decade in axion mass. Published by the American Physical Society 2024

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2020

Detectability of standing accretion shock instabilities activity in supernova neutrino signals

We introduce a novel methodology for establishing the presence of standing accretion shock instabilities (SASI) in the dynamics of a core collapse supernova from the observed neutrino event rate at water- or ice- based neutrino detectors. The methodology uses a likelihood ratio in the frequency domain as a test- statistics; it is also employed to assess the potential to estimate the frequency and the amplitude of the SASI modulations of the neutrino signal. The parameter estimation errors are consistent with the minimum possible errors as evaluated from the inverse of the Fisher information matrix, and close to the theoretical minimum for the SASI amplitude. Using results from a core-collapse simulation of a 15 solar-mass star by Kuroda et al. [Astrophys. J. 851, 62 (2017).] as a test bed for the method, we find that SASI can be identified with high confidence for a distance to the supernova of up to ~6 kpc for IceCube and up to ~3 kpc for a 0.4 Mt mass water Cherenkov detector. This methodology will aid the investigation of a future galactic supernova.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2026

Maximizing efficiency of dataset compression for machine learning potentials with information theory

Machine learning interatomic potentials (MLIPs) balance high accuracy and lower costs compared to density functional theory calculations, but their performance often depends on the size and diversity of training datasets. Large datasets improve model accuracy and generalization but are computationally expensive to produce and train on, while smaller datasets risk discarding rare but important atomic environments and compromising MLIP accuracy/reliability. Here, we develop an information-theoretical framework to quantify the efficiency of dataset compression methods and propose an algorithm that maximizes this efficiency. By framing atomistic dataset compression as an instance of the minimum set cover (MSC) problem over atom-centered environments, our method identifies the smallest subset of structures that contains as much information as possible from the original dataset while pruning redundant information. The approach is extensively demonstrated on the GAP-20 and TM23 datasets and validated on 64 varied datasets from the ColabFit repository. Across all cases, MSC consistently retains outliers, preserves dataset diversity, and reproduces the long-tail distributions of forces even at high compression rates, outperforming other subsampling methods. Furthermore, MLIPs trained on MSC-compressed datasets exhibit reduced error for out-of-distribution data even in low-data regimes. We explain these results using an outlier analysis and show that such quantitative conclusions could not be achieved with conventional dimensionality reduction methods. The algorithm is implemented in the open-source QUESTS package and can be used for several tasks in atomistic modeling, from data subsampling, outlier detection, and training improved MLIPs at a lower cost.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Magnetostrictive Ultrasonic Waveguide Transducer for In-Pile Thermometry

Real-time and reliable temperature measurement on nuclear fuels is crucial for the safe operation of existing pressurized-water reactors and future advanced nuclear reactors. Magnetostrictive materials deform when subjected to a magnetic field or exhibit magnetization variation when stressed. Here, based on these properties, this study prototyped an ultrasonic thermometer (UT) consisting of a magnetostrictive waveguide, a dc coil providing appropriate magnetic biasing, and an ac coil generating an acoustic impulse and detecting the resulting acoustic echoes. By tracking the time of flight between the excitation pulse and the echoes, the UT can potentially detect nuclear fuel cladding temperature from a long distance. In this study, magnetostrictive iron–gallium alloys, or Galfenol, were selected as the waveguide due to their large magnetostriction, superior temperature survivability, excellent radiation resilience, and high mechanical robustness. A multiphysics finite-element model considering electrical, magnetic, and mechanical dynamics in the magnetostrictive UT was then developed. The model exhibited an error of 0.24% in time-of-flight simulation and, therefore, enabled computer-aided design and guided signal processing. Between room temperature and 120 °C, the new Galfenol-based UT exhibits a linear sensitivity of 162.8×10 ₋6 °C ₋1 , which is 51.7% higher than a previous magnetostrictive UT based on iron–cobalt–vanadium alloys.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2022

Extracting the Subhalo Mass Function from Strong Lens Images with Image Segmentation

Abstract Detecting substructure within strongly lensed images is a promising route to shed light on the nature of dark matter. However, it is a challenging task, which traditionally requires detailed lens modeling and source reconstruction, taking weeks to analyze each system. We use machine learning to circumvent the need for lens and source modeling and develop a neural network to both locate subhalos in an image as well as determine their mass using the technique of image segmentation. The network is trained on images with a single subhalo located near the Einstein ring across a wide range of apparent source magnitudes. The network is then able to resolve subhalos with masses m ≳ 10 8.5 M ⊙ . Training in this way allows the network to learn the gravitational lensing of light, and, remarkably, it is then able to detect entire populations of substructure, even for locations further away from the Einstein ring than those used in training. Over a wide range of the apparent source magnitude, the false-positive rate is around three false subhalos per 100 images, coming mostly from the lightest detectable subhalo for that signal-to-noise ratio. With good accuracy and a low false-positive rate, counting the number of pixels assigned to each subhalo class over multiple images allows for a measurement of the subhalo mass function (SMF). When measured over three mass bins from 10 9 –10 10 M ⊙ the SMF slope is recovered with an error of 36% for 50 images, and this improves to 10% for 1000 images with Hubble Space Telescope-like noise.

79 ASTRONOMY AND ASTROPHYSICS↗