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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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At least 1,099 records · Page 61
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

The metabolic origins of non-photorespiratory CO 2 release during photosynthesis: a metabolic flux analysis

Respiration in the light (R L ) releases CO 2 in photosynthesizing leaves and is a phenomenon that occurs independently from photorespiration. Since R L lowers net carbon fixation, understanding R L could help improve plant carbon-use efficiency and models of crop photosynthesis. Although R L was identified more than 75 years ago, its biochemical mechanisms remain unclear. To identify reactions contributing to R L , we mapped metabolic fluxes in photosynthesizing source leaves of the oilseed crop and model plant camelina (Camelina sativa). We performed a flux analysis using isotopic labeling patterns of central metabolites during 13 CO 2 labeling time course, gas exchange, and carbohydrate production rate experiments. To quantify the contributions of multiple potential CO 2 sources with statistical and biological confidence, we increased the number of metabolites measured and reduced biological and technical heterogeneity by using single mature source leaves and quickly quenching metabolism by directly injecting liquid N 2 ; we then compared the goodness-of-fit between these data and data from models with alternative metabolic network structures and constraints. Our analysis predicted that R L releases 5.2 μmol CO2 g –1 FW h –1 of CO 2 , which is relatively consistent with a value of 9.3 μmol CO 2 g –1 FW h –1 measured by CO 2 gas exchange. The results indicated that ≤10% of R L results from TCA cycle reactions, which are widely considered to dominate R L . Further analysis of the results indicated that oxidation of glucose-6-phosphate to pentose phosphate via 6-phosphogluconate (the G6P/OPP shunt) can account for >% of CO 2 released by R L .

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2019

Modeling Present and Future Permafrost Distribution at Seward Peninsula, Alaska: Modeling Archive

This dataset contains results from two GIPL runs for RCP4.5 and RCP8.5 across the Seward Peninsula. The model was run on 500 m by 500 m resolution for the period 1901-2100. Meteo variables include forcing air temperature and precipitation as well as modeled maxim and average snow height. This dataset also includes ground temperature at 10 different depths for every grid cell, active layer depth and talik thickness. Auxillary variables include ecotype map and projected latitude and longitude. Ground temperature borehole data that is used for model calibration and validation of GIPL2 for Seward Peninsula 500 m resolution model runs for 1901-2100 time period. Dataset also includes calibrated model parameters for future use. The main purpose of this dataset is the reproducibility of research. Data were produced for regional permafrost distribution assessment for present and future, in addition, model parameters can be used for future model runs on adjacent territories. This dataset contains *.pdf, *.txt, *.csv, and *.nc. files. The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), was a research effort to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy's Office of Biological and Environmental Research. The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska. Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy's Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Phase Field Modeling of Pressure Induced Densification in Solid Electrolytes

Adoption of dense and homogeneous solid electrolytes can possibly mitigate the propagation of lithium dendrites and enable lithium metal anodes. Application of external pressure helps to minimize the sintering temperature in oxide ceramics and can potentially densify softer sulfide electrolytes even under room temperature conditions. Here, a previously developed phase field-based computational scheme for predicting the high-temperature sintering-induced densification of oxide ceramic solid electrolytes is extended in the present context to capture the influence of external pressure for densifying solid electrolytes. Two different bulk deformation mechanisms, namely, "reorganization" and "creep deformation," are dominant under external pressure, which is different from the surface and grain-boundary diffusion-induced densification of solid electrolytes that occurs during high temperature sintering. External pressure also increases the points of contact between the particles, which further enhances the propensity of diffusion-induced sintering process. Results obtained from simulations indicate that densification under external pressure is independent of the solid electrolyte particle morphology. Finally, a phase map is generated between applied pressure and temperature for achieving complete densification of oxide ceramics, which can possibly guide the synthesis of thin and dense solid electrolyte separators.

25 ENERGY STORAGE↗
DOE OSTI2021

$\rm {H\,\small {I}}$ constraints from the cross-correlation of eBOSS galaxies and Green Bank Telescope intensity maps

We present the joint analysis of Neutral Hydrogen ($\rm {H\,\small {I}}$) Intensity Mapping observations with three galaxy samples: the Luminous Red Galaxy (LRG) and Emission Line Galaxy (ELG) samples from the eBOSS survey, and the WiggleZ Dark Energy Survey sample. The $\rm {H\,\small {I}}$ intensity maps are Green Bank Telescope observations of the redshifted $21\rm cm$ emission on $100 \, {\rm deg}^2$ covering the redshift range 0.6 < z < 1.0. We process the data by separating and removing the foregrounds present in the radio frequencies with FastI ICA. We verify the quality of the foreground separation with mock realizations, and construct a transfer function to correct for the effects of foreground removal on the $\rm {H\,\small {I}}$ signal. We cross-correlate the cleaned $\rm {H\,\small {I}}$ data with the galaxy samples and study the overall amplitude as well as the scale dependence of the power spectrum. We also qualitatively compare our findings with the predictions by a semianalytical galaxy evolution simulation. The cross-correlations constrain the quantity $\Omega _{\rm {H\,\small {I}}} b_{\rm {H\,\small {I}}} r_{\rm {H\,\small {I}},{\rm opt}}$ at an effective scale k eff , where $\Omega _\rm {H\,\small {I}}$ is the $\rm {H\,\small {I}}$ density fraction, $b_\rm {H\,\small {I}}$ is the $\rm {H\,\small {I}}$ bias, and $r_{\rm {H\,\small {I}},{\rm opt}}$ the galaxy–hydrogen correlation coefficient, which is dependent on the $\rm {H\,\small {I}}$ content of the optical galaxy sample. At $k_{\rm eff}=0.31 \, h\,{\rm Mpc^{-1}}$ we find $\Omega _{\rm {H\,\small {I}}} b_{\rm {H\,\small {I}}} r_{\rm {H\,\small {I}},{\rm Wig}} = [0.58 \pm 0.09 \, {\rm (stat) \pm 0.05 \, {\rm (sys)}}] \times 10^{-3}$ for GBT-WiggleZ, $\Omega _{\rm {H\,\small {I}}} b_{\rm {H\,\small {I}}} r_{\rm {H\,\small {I}},{\rm ELG}} = [0.40 \pm 0.09 \, {\rm (stat) \pm 0.04 \, {\rm (sys)}}] \times 10^{-3}$ for GBT-ELG, and $\Omega _{\rm {H\,\small {I}}} b_{\rm {H\,\small {I}}} r_{\rm {H\,\small {I}},{\rm LRG}} = [0.35 \pm 0.08 \, {\rm (stat) \pm 0.03 \, {\rm (sys)}}] \times 10^{-3}$ for GBT-LRG, at z ≃ 0.8. We also report results at $k_{\rm eff}=0.24$ and $k_{\rm eff}=0.48 \, h\,{\rm Mpc^{-1}}$. With little information on $\rm {H\,\small {I}}$ parameters beyond our local Universe, these are amongst the most precise constraints on neutral hydrogen density fluctuations in an underexplored redshift range.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

X-ray fluorescence mapping of brain tissue reveals the profound extent of trace element dysregulation in stroke pathophysiology

Abstract The brain is a privileged organ with regard to its trace element composition and maintains a robust barrier system to sequester this specialized environment from the rest of the body and the vascular system. Stroke is caused by loss of adequate blood flow to a region of the brain. Without adequate blood flow ischaemic changes begin almost immediately, triggering an ischaemic cascade, characterized by ion dysregulation, loss of function, oxidative damage, cellular degradation, and breakdown of the barrier that helps maintain this environment. Ion dysregulation is a hallmark of stroke pathophysiology and we observe that most elements in the brain are dysregulated after stroke. X-ray fluorescence-based detection of physiological changes in the neurometallome after stroke reveals profound ion dysregulation within the lesion and surrounding tissue. Not only are most elements significantly dysregulated after stroke, but the level of dysregulation cannot be predicted from a cell-level description of dysregulation. X-ray fluorescence imaging reveals that the stroke lesion retains <25% of essential K+ after stroke, but this element is not concomitantly elevated elsewhere in the organ. Moreover, elements like Na+, Ca2+, and Cl− are vastly elevated above levels available in normal brain tissue (>400%, >200%, and >150%, respectively). We hypothesize that weakening of the blood–brain barrier after stroke allows elements to freely diffuse down their concentration gradient so that the stroke lesion is in equilibrium with blood (and the compartments containing brain interstitial fluid and cerebrospinal fluid). The change observed for the neurometallome likely has consequences for the potential to rescue infarcted tissue, but also presents specific targets for treatment.

Biochemistry & Molecular Biology↗
DOE OSTI2021

Quantitative Detection of Products and Radical Intermediates in Low-Temperature Oxidation of Cyclopentane

We present a combined experimental and theoretical investigation of the autoignition chemistry of a prototypical cyclic hydrocarbon, cyclopentane. Experiments using a high-pressure photolysis reactor coupled to time-resolved synchrotron VUV photoionization mass spectrometry directly probe the short-lived radical intermediates and products in cyclopentane oxidation reactions. We detect key peroxy radical intermediates ROO and OOQOOH, as well as several hydroperoxides, formed by second O 2 addition. Automated quantum chemical calculations map out the R + O 2 + O 2 reaction channels and demonstrate that the detected intermediates belong to the dominant radical chain-branching pathway: ROO (+ O 2 ) → γ-QOOH + O 2 → γ-OOQOOH → products. ROO, OOQOOH, and hydroperoxide products of second-O 2 addition undergo extensive dissociative ionization, making their experimental assignment challenging. We use photoionization dynamics calculations to aid in their characterization and report the absolute photoionization spectra of isomerically pure ROO and γ-OOQOOH. Here, a global statistical fit of the observed kinetics enables reliable quantification of the time-resolved concentrations of these elusive, yet critical species, paving the way for detailed comparisons with theoretical predictions from master-equation-based models.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

First-Principles Elucidation of Initial Dehydrogenation Pathways in Mg(BH 4 ) 2

Complex borohydrides such as Mg(BH 4 ) 2 offer one of highest capacities to chemically store hydrogen for onboard applications; however, it suffers greatly from kinetic constraints that prevent realization of full capacity and reversibility. Understanding these kinetic limitations solely from experiments is extremely challenging due to the unusual complexity of various competing elemental reaction steps involved during the de/rehydrogenation reaction. This work aims to map out the energetics associated with initial dehydrogenation of Mg(BH 4 ) 2 from first-principles simulations and to identify the preferred reaction pathways. Our calculations suggest the rate-limiting step during BH 4 – –B 3 H 8 – conversion is the formation of the B 2 H 7 – intermediate. We further emphasize and clarify that the B 3 H 8 – and H – intermediates, formed during initial Mg(BH 4 ) 2 decomposition, appear as molecular species that are embedded in the Mg–BH 4 –Mg matrix as evidenced in the nuclear magnetic resonance measurements and not as bulk MgH 2 and Mg(B 3 H 8 ) 2 as previously assumed in theoretical predictions of the thermodynamics.

08 HYDROGEN↗
DOE OSTI2026

Physics-informed machine learning exploration of Na storage mechanisms in disordered carbon

Sodium-ion batteries are a cost-effective, sustainable alternative to lithium-ion systems for large-scale energy storage. However, optimizing sodium storage in carbon-based anodes with microstructural complexity and atomic disorder remains a major challenge. The intrinsic inhomogeneity of these materials produces diverse local environments, making it difficult for conventional methods to predict and control ion dynamics. Hard carbon (HC) anodes, composed of ranges of ordered-to-disordered graphitic and amorphous nanodomains, offer tunable ion storage and rate capacity, yet rationale design remains a challenge due to poorly understood correlation between local atomic feature and ion transport mechanism. Here, to address this challenge, we introduce a data-driven framework that integrates validated machine-learned interatomic potentials, large-scale molecular dynamics simulations, and machine learning to elucidate sodium transport mechanisms as a function of carbon and sodium loading densities. By computing per-ion structural descriptors and applying unsupervised learning, we identify distinct diffusion modes governed by microscopic features. Supervised analysis and correlation mapping then establish quantitative links between these transport regimes and processing variables such as bulk carbon density and sodium content. This physics-informed approach establishes quantitative structure–transport relationships and offers actionable design principles for engineering high-performance HC anodes.

Data-driven framework↗
DOE OSTI2021

Sample environment effects on synchrotron-measured temperature profiles in an approximant of optical floating zone crystal growth

Even though the growth of crystals using optical floating zone furnaces has had an immense scientific impact, the implementation of this method remains more of an art than a science due to the difficulty of obtaining quantitative information about the sample thermal profile during crystal growth. Building on recent work demonstrating that in situ synchrotron studies can be used to map sample rod temperatures during heating, investigations were carried out to better understand how the sample environment affects the sample temperature profile. Through a combination of experimental studies and modeling efforts, it is shown that the environment in the furnace can strongly influence the sample temperature at the lamp focus, the steepness of the vertical temperature gradient, and the timescale required for sample heating and cooling - effects which can combine to produce a strong history-dependence to sample temperature profiles. It is demonstrated that the furnace effects can be effectively captured in thermal models, allowing both the steady-state and time-dependent behavior of the sample to be accurately reproduced with predictive models, and providing a launching point for improved furnace designs that can more readily deliver desired thermal profiles.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Emerging anomaly detection techniques for electronic health records: A survey

Background Anomaly detection in electronic health records (EHRs) is a cornerstone of biomedical informatics, with direct implications for patient safety, clinical decision-making, and the prevention of healthcare fraud. Once guided primarily by simple rule-based methods, the field has advanced rapidly, driven by increased computing power, richer and more detailed health data, and the rise of machine learning and deep learning techniques. The objective of this paper is to provide a comprehensive overview of modern approaches to detecting anomalies in EHRs, outlining their strengths, limitations, and relevance to key healthcare challenges. We review traditional statistical methods alongside newer ML- and DL-based strategies and hybrid models, with particular attention to how these techniques support transparency and build clinical trust. Methods This paper presents a thorough and critical survey through systematic review (PRISMA-based) of the latest anomaly detection strategies in time-sequence data domains within electronic health record systems. Results We explore a broad spectrum of methodologies, including statistical models, supervised and unsupervised learning approaches, hybrid frameworks, and state-of-the-art ML-based techniques that collectively advance the precision and scalability of detecting anomalies in complex clinical datasets. In addition to mapping current capabilities, we address the enduring challenges that hinder widespread implementation and provide a forward-looking perspective on the future of anomaly detection in the data-rich landscape of modern healthcare. Summary The advancement in AI-based approaches is reported along with the basic principles of the individual approaches and their applicability. The increased availability of high-quality data, advancements in DL approaches, and enhanced computation power are leading to more frequent adaptation of DL-based approaches. Emerging DL-based approaches that have been adapted in other domains or recently applied in the EHR domain are also discussed in detail. Although DL-based approaches can improve model predictions by incorporating comorbidities, their application is limited in low-frequency data domains (e.g., when the total available data remains in the single digits). Therefore, the user must carefully consider the application based on data availability.

Anomaly detection↗
DOE OSTI2024

NGEE Arctic 2019 Alder Ground Truth Survey, Seward Peninsula AK

In July 2019 we made traveled the road system outside of Nome, AK and detailed the GPS coordinates of alder shrublands for the purpose of ground-truthing alder maps of the region. Both visual and ground-based observations were made for patches of alder shrublands greater 5x5m and larger, ideally 10x10m. Visual observations were made from the car and GPS coordinates are approximate, placed by dropping pins on georeferenced pdfs using the Avenza app. Visual observations included positive identified alder shrublands as well as thickets of non-alder shrubs. Ground Observations were made at a subset of locations where we were able to hike to alders shrubland areas. Ground observations include GPS points (made with Garmin InReach) as well as relevant features of a centrally located, representative alder shrub in the patch (max height, basal diameter of all ramets, soil depth). Aboveground biomass (weight dry mass) of the surveyed shrub was calculated based on alder-specific allometric equations in Berner et al 2015 which our team checked for accuracy for the Seward Peninsula as part of Salmon et al 2019. This dataset contains three data files, three data dictionaries, and one file-level metadata file all in*.csv format plus one *.txt README file. The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), was a research effort to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy's Office of Biological and Environmental Research.The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska.Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy's Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Reconstruction of Six-Dimensional Phase Space

A phase space is a mathematical representation of all possible physical states of a system. Particle beams at Fermilab exist within a six-dimensional (6D) phase space defined by three positional components, (x, y, z) and three momentum components, (px, py, pz). To reconstruct this space implies taking measurement data from detectors and mapping out particle behavior using computational methods. The beam detectors, however, are only able to detect spatial distribution among the events of the beam, therefore being limited to positional data. Also, due to the vast number of events in a particle beam, it is extremely difficult to analyze and differentiate every single one’s behavior. However, with Machine Learning (ML), which can distinguish between patterns and map out particle behavior more efficiently. We first used the particle beam software, G4beamline, to simulate a 10,000-event muon beam, adjusting parameters such as initial momentum magnitude (p¬0) and virtual detector position. Using ten virtual detectors, we analyzed p0 values such that minimum 9,990 events were analyzed by every detector. We then input the data from these beam simulations to a C++ program, that randomly selects 100 events, and creates a 2D histogram based on spatial distribution, detector position, and event intensity. This process is repeated 100 times to create 100 histograms per p0 value. These images were then input to a modified ResNet18 Convolutional Neural Network (CNN) for training, and to predict p0 from some unseen set of histograms. The model was accurate when trained on momentum increments of 5 MeV/c and provided with denser training samples around highly variable test values. These results displayed machine learning being able to accurately predict p0 from being trained on different particle behaviors.

Shirlee, Jermain [Fermilab]↗
DOE OSTI2022

UAS remote sensing (DJI Phantom 4 RTK platform): RGB orthomosaic, digital surface and canopy height models, plant functional type map, Seward Peninsula, Alaska, 2019

Airborne remote sensing data collected using a DJI Phantom 4 RTK unoccupied aerial system (UAS) ? operated by the University of Maine Wheatland Geospatial Lab (https://wheatlandlab.org/). This package includes data from 17 flights flown over the NGEE-Arctic Council, Kougarok, Kougarok Mile80, Teller, and Teller Mile32 sites in July 2019. The Phantom 4 RTK is a drone platform that collects very high spatial resolution optical red/green/blue (RGB) imagery. Derived image products include point cloud, ortho-mosaiced RGB, a digital surface model (DSM) using the structure from motion (SfM) technique, and a canopy height model (CHM). Unprocessed and processed data products (1,000+ files) are included in this package (processing levels 0-3). Data and metadata are provided as text (*.txt, *.json, *hdr,), ENVI image file (*.dat), point cloud (*.laz) and image (*.jpg, *.tif, *png) formats. The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), was a research effort to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy's Office of Biological and Environmental Research. The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska. Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy's Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Scaling CFB risers: Maintaining microstructure dynamics

Over the years there have been numerous approaches to develop a scaleup methodology for circulating fluidized beds. Many of these past approaches have relied on macroscopic definitions of similitude to define dimensionless groups such as Reynolds number, Froude number, density ratios, and dimensional ratios involving different length scales. These approaches have demonstrated various levels of success, but often fail when used outside the bounds of experimental datasets. The issue with these methods is that they rely on macroscopic similitude with the hopes that those conditions would provide microscopic similitude as it does in single phase systems where the Buckingham Pi techniques have given reasonable success. Two of the main reasons for failure are 1) changing fluidization regimes and 2) changing material properties that result in the material falling into a different Geldart group. Both are certain to change the microstructure (local instantaneous volume solids fraction) and changes to the microstructure affect the interphase transport properties of the reactor system, namely the mass transfer and heat transfer. Here, this work presents a new approach towards scaling based upon maintaining similitude at the microscale. It reviews a novel dimensionless flow regime map developed from analysis of the deterministic chaos parameters and higher order moments to ensure microstructure similitude. The work then examines four cases from the literature to assess the scaling approach used: (1) same particle-different scale risers for Geldart Group B particles, (2) Glicksman scaling of atmospheric circulating fluidized bed combustor, (3) same particle-different scale risers for Geldart Group A particles and (4) Glicksman scaling for pressurized gasification. General findings are that none of the scaling approaches produced directly applicable microstructure similar behavior based upon the chaotic and higher moment analysis. The positive take away is that the approach can define scale conditions that will provide similar microstructure that can be us to predict transport behavior.

42 ENGINEERING↗
DOE OSTI2022

Computational study of inertial migration of prolate particles in a straight rectangular channel

Inertial migration of spherical particles has been investigated extensively using experiments, theory, and computational modeling. Yet, a systematic investigation of the effect of particle shape on inertial migration is still lacking. Herein, we numerically mapped the migration dynamics of a prolate particle in a straight rectangular microchannel using smoothed particle hydrodynamics at moderate Reynolds number flows. After validation, we applied our model to 2:1 and 3:1 shape aspect ratio particles at multiple confinement ratios. Their effects on the final focusing position, rotational behavior, and transitional dynamics were studied. In addition to the commonly reported tumbling motion, for the first time, we identified a new logrolling behavior of a prolate ellipsoidal particle in the confined channel. This new behavior occurs when the confinement ratio is above an approximate threshold value of K = 0.72. Our microfluidic experiments using cell aggregates with similar shape aspect ratio and confinement ratio confirmed this new predicted logrolling motion. In this study, we also found that the same particle can undergo different rotational modes, including kayaking behavior, depending on its initial cross-sectional position and orientation. Furthermore, we examined the migration speed, angular velocity, and rotation period as well as their dependence on both particle shape aspect ratio and confinement ratio. Our findings are especially relevant to the applications where particle shape and alignment are used for sorting and analysis, such as the use of barcoded particles for biochemical assays through optical reading, or the shape-based enrichment of microalgae, bacteria, and chromosomes.

42 ENGINEERING↗
DOE OSTI2026

Optimizing Optical Searches for Supermassive Black Hole Binaries in Active Galactic Nuclei Light Curves: Fourier versus Bayesian Periodicity Detection

Simulations predict that supermassive black hole binaries (SMBHBs) will exhibit periodic brightness variations that may exceed the stochastic variability intrinsic to active galactic nuclei (AGN). In this paper, we simulate SMBHBs with damped random walk (DRW) AGN variability and an added sinusoidal signal from the orbital motion, and test three methods—a generalized Lomb–Scargle periodogram (GLSP), a nested Bayesian sampler (NBS), and a weighted wavelet z-transform (or WWZ)—to determine which is best at recovering the periodicity. Our simulated light curves follow the properties of the Catalina Real-Time Transient Survey (or CRTS), Legacy Survey of Space and Time (LSST), and Zwicky Transient Facility (ZTF) to best inform current and future SMBHB searches. We map a broad range of parameter space and identify which DRW-only light curves best mimic periodicity and pass each method’s model selection. The NBS performs best at detecting periodicity and filtering out DRW-only light curves. Combined candidate selection with both the NBS and GLSP significantly reduces false-positive rates (FPRs) with marginal impact on true-positive rates (TPRs). With this joint model selection pipeline, we find the lowest FPRs in ZTF-like simulations and the highest detection rates in LSST-like simulations. Using a modified computation of the false-alarm probability with GLSP, we efficiently triage LSST AGN light curves (∼10 7 light curves in ∼10–30 hr) and achieve TPRs and FPRs of ∼40% and ∼0.5%, respectively.

Banaszak, Sebastian M. [Vanderbilt Univ., Nashvill↗
DOE OSTI2023

Winter connectivity and leapfrog migration in a migratory passerine

Abstract Technological advances in migratory tracking tools have revealed a remarkable diversity in migratory patterns. One such pattern is leapfrog migration, where individuals that breed further north migrate to locations further south. Here, we analyzed migration patterns in the Painted Bunting ( Passerina ciris ) using a genetic‐based approach. We started by mapping patterns of genetic variation across geographic space (called a genoscape) using 386 individuals from 25 populations across the breeding range. We then genotyped an additional 230 samples from 31 migration stopover locations and 178 samples from 16 wintering locations to map patterns of migratory connectivity. Our analyses of genetic variation across the breeding range show the existence of four genetically distinct groups within the species: Eastern, Southwestern, Louisiana, and Central groups. Subsequent assignment of migrating and wintering birds to genetic groups illustrated that birds from the Central group migrated during the fall via western Mexico or southern Texas, spent the winter from northeastern Mexico to Panama, and migrated north via the Gulf Coast of Mexico. While Louisiana birds overlapped with Central birds on their spring migratory routes along the Gulf Coast, we found that Louisiana birds had a more restricted wintering distribution in the Yucatan Peninsula and Central America. Further estimation of the straight‐line distance from the predicted breeding location to the wintering location revealed that individuals sampled at lower winter latitudes traveled to greater distances (i.e., the predicted breeding area was further north; p > .001), confirming that these species exhibit a leapfrog migration pattern. Overall, these results demonstrate the utility of a genoscape‐based approach for identifying range‐wide patterns of migratory connectivity such as leapfrog migration with a high degree of clarity.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Evaluation of Methods for Causal Discovery in Hydrometeorological Systems

Understanding causal relations is of utmost importance in hydrology and climate research for systems identification, prediction, and understanding systems behavior in a changing climate. Traditionally, researchers in hydrometeorology attempted to study causal questions by conducting controlled experiments using numerical models. This approach, however, in most cases of interest provides uncertain results because the models are approximate representation of the natural system. An alternative approach that has recently drawn significant attention in several fields is to infer causal relations from purely observational data. It possesses several traits to its utility particularly in hydrometeorology due to the rapid accumulation of in situ and remotely sensed data records. The first objective of this study is to present a brief description of four causal discovery methods (Granger causality, Transfer Entropy, graph-based algorithms, and Convergent Cross Mapping) with special emphasis on the assumptions on which they are built. Second, using synthetic data generated from a hydrological model, we assess their performance in retrieving causal information taking into account sensitivity to sample size and presence of noise. Last, we use causal analysis to examine and formulate hypotheses on causal drivers of evapotranspiration in a shrubland region during summer and winter seasons. An interpretation of the hypotheses based on canopy seasonal dynamics and evapotranspiration processes is presented. It is hoped that the results presented here can be useful in guiding researchers studying hydrometeorological systems as to which causal method is most appropriate to the characteristics of the system under study.

54 ENVIRONMENTAL SCIENCES↗