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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 OSTI2021

Deep Search for Decaying Dark Matter with XMM-Newton Blank-Sky Observations

Sterile neutrinos with masses in the keV range are well-motivated extensions to the Standard Model that could explain the observed neutrino masses while also making up the dark matter (DM) of the universe. If sterile neutrinos are DM then they may slowly decay into active neutrinos and photons, giving rise to the possibility of their detection through narrow spectral features in astrophysical x-ray data sets. Here, we perform the most sensitive search to date for this and other decaying DM scenarios across the mass range from 5 to 16 keV using archival XMM-Newton data. We reduce 547 Ms of data from both the MOS and PN instruments using observations taken across the full sky and then use this data to search for evidence of DM decay in the ambient halo of the Milky Way. We determine the instrumental and astrophysical baselines with data taken far away from the Galactic Center, and use Gaussian process modeling to capture additional continuum background contributions. No evidence is found for unassociated x-ray lines, leading us to produce the strongest constraints to date on decaying DM in this mass range.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

UAS Edge Computing of Energy Infrastructure Damage Assessment

Energy infrastructure assessments are needed within 72 hours of natural disasters, and previous data collection methods have proven too slow. We demonstrate a scalable end-to-end solution using a prototype unmanned aerial system that performs on-the-edge detection, classification (i.e., damaged or undamaged), and geo-location of utility poles. The prototype is suitable for disaster response because it requires no local communication infrastructure and is capable of autonomous missions. Collections before, during, and after Hurricane Ida in 2021 were used to test the system. The system delivered an F1 score of 0.65 operating with a 2.7 s/frame processing speed with the YOLOv5 large model and an F1 score of 0.55 with a 0.48 s/frame with the YOLOv5 small model. Geo-location uncertainty in the bottom half of the frame was ~8 m, mostly driven by error in camera pointing measurement. With additional training data to improve performance and detect additional types of features, a fleet of similar drones could autonomously collect actionable post-disaster data.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

A Strategy for NACS investment in Machine Learning

The Nuclear and Chemical Sciences (NACS) Division furnishes the expertise in the scientific areas of chemical, nuclear and isotopic sciences that are foundational in the Laboratory’s national security missions. This expertise is maintained and advanced through identification, development and application of state-of-the-art theoretical, computational and experimental methods and tools. Recent developments in artificial intelligence and machine learning (AI/ML) techniques enabled by advances in computing capabilities and widespread availability of powerful software implementations have made use of these techniques ubiquitous across both science and industry. While the scope of AI/ML applications is incredibly large and evolves very rapidly, the topics most relevant to NACS missions fall into the general category of detecting, categorizing or identifying features in large, complex datasets using either supervised or unsupervised learning. This covers both basic scientific data analysis and the development of efficient surrogate models of real-life technological systems, experimental detectors, or theoretical models. To remain at the forefront of its core scientific disciplines, NACS must both cultivate ML expertise as well as continuously explore applying this expertise to new problems or utilizing new methods. This document identifies the key areas where this support is critical and provides a strategy for investing in them.

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗
DOE OSTI2021

The Ultra-High-Energy Source MGRO J1908+06

The TeV gamma-ray source MGRO J1908+06 is one of the highest-energy sources known, with observed emission by the High Altitude Water Cherenkov (HAWC) Observatory extending well past 100 TeV. The source exhibits both energy-dependent morphology and a spatially-dependent spectral index. The emission is likely to be dominantly leptonic, and associated with the radio-quiet PSR J1907+0602. However, one-population models do not describe the data well; a second particle population is needed to explain the shape of the spectral energy distribution at the highest energies. This component can be well-described by either leptonic or hadronic hypotheses. We discuss this feature and implications for detection by multi-wavelength and multi-messenger experiments.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

Metabolome patterns identify active dechlorination in bioaugmentation consortium SDC-9™

Ultra-high performance liquid chromatography–high-resolution mass spectrometry (UPHLC–HRMS) is used to discover and monitor single or sets of biomarkers informing about metabolic processes of interest. The technique can detect 1000’s of molecules (i.e., metabolites) in a single instrument run and provide a measurement of the global metabolome, which could be a fingerprint of activity. Despite the power of this approach, technical challenges have hindered the effective use of metabolomics to interrogate microbial communities implicated in the removal of priority contaminants. Herein, our efforts to circumvent these challenges and apply this emerging systems biology technique to microbiomes relevant for contaminant biodegradation will be discussed. Chlorinated ethenes impact many contaminated sites, and detoxification can be achieved by organohalide-respiring bacteria, a process currently assessed by quantitative gene-centric tools (e.g., quantitative PCR). This laboratory study monitored the metabolome of the SDC-9™ bioaugmentation consortium during cis-1,2-dichloroethene (cDCE) conversion to vinyl chloride (VC) and nontoxic ethene. Untargeted metabolomics using an UHPLC-Orbitrap mass spectrometer and performed on SDC-9™ cultures at different stages of the reductive dechlorination process detected ~10,000 spectral features per sample arising from water-soluble molecules with both known and unknown structures. Multivariate statistical techniques including partial least squares-discriminate analysis (PLSDA) identified patterns of measurable spectral features (peak patterns) that correlated with dechlorination (in)activity, and ANOVA analyses identified 18 potential biomarkers for this process. Statistical clustering of samples with these 18 features identified dechlorination activity more reliably than clustering of samples based only on chlorinated ethene concentration and Dhc 16S rRNA gene abundance data, highlighting the potential value of metabolomic workflows as an innovative site assessment and bioremediation monitoring tool.

environmental monitoring↗
DOE OSTI2025

Potential Role of Malassezia restricta in Pterygium Development

Pterygium is a condition affecting the ocular surface, marked by a triangular-shaped growth of fibrotic tissue extending from the nasal conjunctiva toward the corneal center, potentially causing visual impairment. While ultraviolet (UV )light exposure is the primary risk factor for pterygium, its underlying cause remains unclear. In order to better understand the true genesis of pterygium development, we investigated pterygium tissue and compared it with healthy conjunctiva controls. Given the eye’s direct environmental exposure, we analyzed the microbiota composition using metagenomic sequencing of pterygium tissue to identify microbes potentially associated with this condition. Metagenomic sequencing revealed a higher prevalence of the fungus Malassezia restricta in five pterygium samples, confirmed by in situ hybridization. The CHIT1 gene, which plays a role in antifungal defenses, displayed the highest expression in five pterygium tissue samples compared to healthy conjunctiva controls, suggesting the potential involvement of Malassezia restricta in pterygium development. Gene expression profiling of pterygium highlighted an IL-33 and IL-4 gene expression signature, along with an increased presence of M2 macrophages, emphasizing their role in promoting fibrosis—a hallmark feature of pterygium. The detection of Malassezia restricta in the pterygium samples and associated molecular changes provides novel insights into the ocular microbiome and raises the possibility of Malassezia’s involvement in pterygium pathology.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2020

A Featureless Infrared Transmission Spectrum for the Super-puff Planet Kepler-79d

Extremely low-density planets (“super-puffs”) are a small but intriguing subset of the transiting planet population. With masses in the super-Earth range (1 − 10 M{sub ⊕}) and radii akin to those of giant planets (> 4 R{sub ⊕}), their large envelopes may have been accreted beyond the water snow line and many appear to be susceptible to catastrophic mass loss. Both the presence of water and the importance of mass loss can be explored using transmission spectroscopy. Here, we present new Hubble space telescope WFC3 spectroscopy and updated Kepler transit depth measurements for the super-puff Kepler-79d. We do not detect any molecular absorption features in the 1.1 − 1.7 μm WFC3 bandpass, and the combined Kepler and WFC3 data are consistent with a flat-line model, indicating the presence of aerosols in the atmosphere. We compare the shape of Kepler-79d’s transmission spectrum to predictions from a microphysical haze model that incorporates an outward particle flux due to ongoing mass loss. We find that photochemical hazes offer an attractive explanation for the observed properties of super-puffs like Kepler-79d, as they simultaneously render the near-infrared spectrum featureless and reduce the inferred envelope mass-loss rate by moving the measured radius (optical depth unity surface during transit) to lower pressures. We revisit the broader question of mass-loss rates for super-puffs and find that the age estimates and mass-loss rates for the majority of super-puffs can be reconciled if hazes move the photosphere from the typically assumed pressure of ∼10 mbar to ∼10 μbar.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

Production of Very Light Elements and Strontium in the Early Ejecta of Neutron Star Mergers

Abstract We study the production of very light elements ( Z < 20) in the dynamical and spiral-wave wind ejecta of binary neutron star mergers by combining detailed nucleosynthesis calculations with the outcome of numerical relativity merger simulations. All our models are targeted to GW170817 and include neutrino radiation. We explore different finite-temperature, composition-dependent nuclear equations of state, and binary mass ratios, and find that hydrogen and helium are the most abundant light elements. For both elements, the decay of free neutrons is the driving nuclear reaction. In particular, ∼0.5–2 × 10 −6 M ⊙ of hydrogen are produced in the fast expanding tail of the dynamical ejecta, while ∼1.5–11 × 10 −6 M ⊙ of helium are synthesized in the bulk of the dynamical ejecta, usually in association with heavy r -process elements. By computing synthetic spectra, we find that the possibility of detecting hydrogen and helium features in kilonova spectra is very unlikely for fiducial masses and luminosities, even when including nonlocal thermodynamic equilibrium effects. The latter could be crucial to observe helium lines a few days after merger for faint kilonovae or for luminous kilonovae ejecting large masses of helium. Finally, we compute the amount of strontium synthesized in the dynamical and spiral-wave wind ejecta, and find that it is consistent with (or even larger than, in the case of a long-lived remnant) the one required to explain early spectral features in the kilonova of GW170817.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2026

A Novel Segmentation Algorithm for the ARM User Facility All-Sky Imagers Using Machine Learning Applications

Cloud cover plays a pivotal role in modulating the Earth's energy budget through the reflection of incoming solar radiation and the trapping of outgoing longwave radiation. Ground-based all-sky imagers offer an objective assessment of cloud cover that can be used to estimate solar irradiance, classify cloud types, track cloud movement, and serve as a benchmark 10 for the evaluation of satellite and reanalysis data products. The Atmospheric Radiation Measurement (ARM) user facility has utilized all-sky imagers for more than 25 years to monitor cloud cover and augment its comprehensive suite of atmospheric measurements. Following the retirement of its Total Sky Imager (TSI), ARM recently deployed the TSI’s successor, the All Sky Imager (ASI-16 camera systems). To provide a smooth transition and continuity to the vast amount of knowledge gathered by the TSI over the years, while addressing typical deployment issues, we developed a novel pixel segmentation algorithm, 15 the ASI Sky Cover (ASISKYCOVER). ASISKYCOVER builds on the different strengths and properties of the TSI processing algorithm while integrating machine learning techniques, ensuring data validity and accuracy across diverse atmospheric conditions. It enhances cloud cover characterization with new features such as artifact detection and uncertainty quantification. ASISKYCOVER also includes cloud cover estimates for near-zenith (narrow field-of-view) and reduces susceptibility to false detections. This study introduces ASISKYCOVER, details its algorithm framework, and demonstrates its capabilities using a 20 year-long dataset from the ARM Southern Great Plains site. Comparisons with co-located TSI data and other ARM measurements, such as zenith-pointing radars and lidars, are presented, underscoring the ASISKYCOVER’s potential to improve cloud cover analyses and data evaluation efforts, as well as to be integrated into higher-level data products that synergize instrument suites to generate new and insightful information

Silber, Israel↗
DOE OSTI2026

Baryon Acoustic Oscillations from the C IV Forest with DESI DR2

We present a measurement of Baryon Acoustic Oscillations (BAO) in the cross-correlation of triply ionized carbon C IV absorption with the positions of quasars (QSO) and Emission Line Galaxies (ELG). We use quasars and ELGs from the second data release (DR2) of the Dark Energy Spectroscopic Instrument (DESI) survey. Our data sample consists of 2.5 million quasars, 3.1 million ELGs, and the C IV absorption is measured along the line of sight of 1.5 million high redshift quasars with $z > 1.3$. We measure the isotropic BAO signal at 4.2$σ$ for the CIV$\times$QSO cross-correlation. This translates into a 3.0% precision measurement of the ratio of the isotropic distance scale, $D_{\rm V}$, and the sound horizon at the drag epoch, $r_{\rm d}$, with $D_{\rm V}/r_{\rm d}(z_{\rm eff} = 1.92) = 30.3 \pm 0.9$. We make the first detection of the BAO feature in the CIV$\times$ELG cross-correlation at a significance of 2.5$σ$ and find $D_{\rm V}/r_{\rm d}(z_{\rm eff} = 1.47) = 24.6 \pm 1.0$.

Bault, Abby [LBL, Berkeley] (ORCID:000000029964100↗
DOE OSTI2025

Damping Wing-Like Features in the Spectra of High Redshift Quasars: a Challenge for Fully-Coupled Simulations

Recently, several observational detections of damping-wing-like features at the edges of “dark gaps” in the spectra of distant quasars (the “Malloy-Lidz effect”) have been reported, rendering strong support for the existence of “neutral islands” in the universe at redshifts as low as z<5.5. We apply the procedure from one of these works, Zhu et al 2024, to the outputs of fully coupled cosmological simulations from two recent large projects, “Cosmic Reionization On Computers” (CROC) and “Thesan”. Synthetic spectra in both simulations have statistics of dark gaps similar to observations, but do not exhibit the damping wing features. Moreover, a toy model with neutral islands added “by hand” only reproduces the observational results when the fraction of neutral islands among all dark gaps approaches 90%. I.e., simulations and observations appear to produce two distinct “populations” of dark gaps. In addition, in the simulations, the neutral islands at z=5.9 should be short-lived and should not extend to z<5.5. A plausible explanation for this discrepancy is that both simulations underestimate the fluctuations in the photoionization rate and, hence, are missing a population of long-lived neutral islands, located in the large downward fluctuations of the photoionization rate.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

ARCH: Large-scale knowledge graph via aggregated narrative codified health records analysis

Objective: Electronic health record (EHR) systems contain a wealth of clinical data stored as both codified data and free-text narrative notes (NLP). The complexity of EHR presents challenges in feature representation, information extraction, and uncertainty quantification. Here, to address these challenges, we proposed an efficient Aggregated naRrative Codified Health (ARCH) records analysis to generate a large-scale knowledge graph (KG) for a comprehensive set of EHR codified and narrative features. Methods: Using data from 12.5 million Veterans Affairs patients, ARCH first derives embedding vectors and generates similarities along with associated p-values to measure the strength of relatedness between clinical features with statistical certainty quantification. Next, ARCH performs a sparse embedding regression to remove indirect linkage between features to build a sparse KG. Finally, ARCH was validated on various clinical tasks, including detecting known relationships between entity pairs, predicting drug side effects, disease phenotyping, as well as sub-typing Alzheimer’s disease patients. Results: ARCH produces high-quality clinical embeddings and KG for over 60,000 codified and narrative EHR concepts. The KG and embeddings are visualized in the R-shiny powered web-API.3 ARCH achieved high accuracy in detecting EHR concept relationships, with AUCs of 0.926 (codified) and 0.861 (NLP) for similar EHR concepts, and 0.810 (codified) and 0.843 (NLP) for related pairs. It detected drug side effects with a 0.723 AUC, which improved to 0.826 after fine-tuning. Using both codified and NLP features, the detection power increased significantly. Compared to other methods, ARCH has superior accuracy and enhances weakly supervised phenotyping algorithms’ performance. Notably, it successfully categorized Alzheimer’s patients into two subgroups with varying mortality rates. Conclusion: The proposed ARCH algorithm generates large-scale high-quality semantic representations and knowledge graph for both codified and NLP EHR features, useful for a wide range of predictive modeling tasks.

Electronic health records↗
DOE OSTI2021

Use of Traveling Wave Signatures in Medium-Voltage Distribution Systems for Fault Detection and Location

Protection systems are a critical feature for the proper operation of a power grid. Protection systems should be able to quickly detect, identify, locate, and isolate the faults within a time frame that prevents damage to grid components and reduces customer outages. As more inverter-based photovoltaics (PV) are integrated into the grid, phasor domain based protection schemes become more inaccurate due to low levels of fault current from inverters or respond too slowly to accurately identify fault conditions. This points to the need to identify and develop novel protection approaches for distribution systems with high penetration of inverter based PV. The main objective of the project is to develop new time domain-based protection schemes to enable more accurate and faster detection, identification and location of faults in distribution systems and/or microgrids with high penetration of inverter-based PV. Our approach aims to exploit the traveling waves that are generated during a fault or other disturbances in the grid.

14 SOLAR ENERGY↗
DOE OSTI2020

A Multi-Instance learning Framework for Seismic Detectors

In this report, we construct and test a framework for fusing the predictions of a ensemble of seismic wave detectors. The framework is drawn from multi-instance learning and is meant to improve the predictive skill of the ensemble beyond that of the individual detectors. We show how the framework allows the use of multiple features derived from the seismogram to detect seismic wave arrivals, as well as how it allows only the most informative features to be retained in the ensemble. The computational cost of the "ensembling" method is linear in the size of the ensemble, allowing a scalable method for monitoring multiple features/transformations of a seismogram. The framework is tested on teleseismic and regional p-wave arrivals at the IMS (International Monitoring System) station in Warramunga, NT, Australia and the PNSU station in University of Utah's monitoring network.

58 GEOSCIENCES↗
DOE OSTI2023

Model-based image change quantification

A system is provided for identifying and quantizing a change in a scene having objects based on comparison of features representing a target object derived from a first image and a second image that includes the target object. The system segments, based on a scene model of the scene, a second image that includes the target object to identify a second area representing the target object within the second image. The system extracts, from the second image, second features for the target object based on the second area. The system determines a second effectiveness of the second features indicating effectiveness of the second features in representing the target object. The system detects a change based on a difference between first features and the second features factoring in a first effectiveness and the second effectiveness.

Roberts, Randy S.↗
DOE OSTI2026

Virtual refrigerant charge sensing algorithm for residential CO₂ heat pumps

Natural refrigerants are increasingly adopted in next-generation heat pump systems, among which CO₂ heat pumps have attracted significant attention. However, due to their high operating pressures, the leakage risk is higher, resulting in undercharge conditions and degraded heat pump performance. Thus, developing an accurate refrigerant charge level detection technique is necessary to guarantee safe and efficient operation. Although virtual refrigerant charge (VRC) level calculation algorithms for CO₂ heat pumps exist, they typically rely on empirically selected features without a systematic selection framework, leading to multicollinearity and potential overfitting, which limit their prediction accuracy and generalizability. To address these issues, this study proposes a VRC algorithm framework with a systematic feature selection method that identifies physically meaningful and statistically significant features, and is applied using a residential CO₂ heat pump as a case study. The method is extended from previous work on conventional refrigerants to account for charge behavior in CO₂ gas coolers. The selected features include gas cooler outlet density, evaporator pressure, and superheat temperature. The results demonstrate that the proposed feature selection method significantly improves prediction accuracy compared to existing VRC approaches. A relatively small training dataset (∼30 samples) is sufficient for feature identification and model development. The developed algorithm achieves less than 3% prediction error under both undercharge and overcharge conditions, representing reductions of 46.7% and 35.3% compared to two recent reference VRC algorithms for transcritical CO₂ heat pumps reported in the literature. The proposed algorithm and feature selection method enhance leakage detection capability, facilitate the deployment of CO₂ heat pump systems, and contribute to reduced energy waste and maintenance costs.

Guo, Fangzhou [Lawrence Berkeley National Laborato↗