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DOE OSTI2021

Non-growing season plant nutrient uptake controls Arctic tundra vegetation composition under future climate

Abstract Plant growth and distribution in high-latitude tundra ecosystems is strongly limited by nutrient availability and is critical for quantifying centennial-scale carbon-climate interactions. However, land model representations of plant–nutrient interactions are uncertain, leading to poor comparisons with high-latitude observations. Although it has been recognized for decades in the observational community that plants continue to acquire nutrients well past when aboveground activity has ceased, most large-scale land models ignore this process. Here we address the role tundra plant nutrient acquisition during the non-growing season (NGS) has on centennial-scale vegetation growth and dynamics, with a focus on shrub expansion. We apply a well-tested mechanistic model of coupled plant, microbial, hydrological, and thermal dynamics that explicitly represents nutrient acquisition based on plant and microbial traits, thereby allowing a prognostic assessment of NGS nutrient uptake. We first show that the model accurately represents observed seasonality of NGS plant nutrient uptake in a northern Alaskan tundra site. Applying the model across the North America tundra indicates that NGS nutrient uptake is consistent with observations and ranges between ∼5% and 50% of annual uptake, with large spatial variability and dependence on plant functional type. We show that NGS plant nutrient acquisition strongly enhances modeled 21 st century tundra shrub growth and expansion rates. Our results suggest that without NGS nutrient uptake, total shrub aboveground dominance would be ∼50% lower, limited primarily by their inability to grow tall enough to maximize their inherent capacity for light competition. Evergreen shrubs would be more strongly affected because of their relatively lower capacity for nutrient remobilization and acquisition compared to deciduous shrubs. Our results highlight the importance of NGS plant and soil processes on high-latitude biogeochemistry and vegetation dynamics and motivate new observations and model structures to represent these dynamics.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2021

The impact of void environment on AGN

ABSTRACT We study the population of active galaxies in void environment in the Sloan Digital Sky Survey. We use optical spectroscopic information to analyse characteristics of the emission lines of galaxies, accomplished by WHAN and BPT diagrams. Also, we study Wide-field Infrared Survey Explorer(WISE) mid-IR colours to assess active galactic nucleus (AGN) activity. We investigate these different AGN classification schemes, both optical and mid-IR, and their dependence on the spatial location with respect to the void centres. To this end, we define three regions: void, the spherical region defined by voidcentric distance relative to void radius (distance/rvoid) smaller than 0.8, comprising overdensities lesser than −0.9, an intermediate/transition shell region (namely, void-wall) 0.8 < distance/rvoid < 1.2, and a region sufficiently distant from voids, the field: distance/rvoid > 2. We find statistical evidence for a larger fraction of AGN and star-forming galaxies in the void region, regardless of the classification scheme addressed (either BPT, WHAN, or WISE). Moreover, we obtain a significantly stronger nuclear activity in voids compared to the field. We find an unusually large fraction of the most massive black holes undergoing strong accretion when their host galaxies reside in voids. Our results suggest a strong influence of the void environment on AGN mechanisms associated with galaxy evolution.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2020

Deep medical image analysis with representation learning and neuromorphic computing

Deep learning is increasingly used in medical imaging, improving many steps of the processing chain, from acquisition to segmentation and anomaly detection to outcome prediction. Yet significant challenges remain: (i) image-based diagnosis depends on the spatial relationships between local patterns, something convolution and pooling often do not capture adequately; (ii) data augmentation, the de facto method for learning three-dimensional pose invariance, requires exponentially many points to achieve robust improvement; (iii) labelled medical images are much less abundant than unlabelled ones, especially for heterogeneous pathological cases; and (iv) scanning technologies such as magnetic resonance imaging can be slow and costly, generally without online learning abilities to focus on regions of clinical interest. To address these challenges, novel algorithmic and hardware approaches are needed for deep learning to reach its full potential in medical imaging.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2026

Sampling off-axis neutrino fluxes with the short-baseline near detector

The short-baseline near detector (SBND), the near detector in the short-baseline neutrino program at Fermi National Accelerator Laboratory, is located just 110 m from the booster neutrino beam target. Thanks to this close proximity, relative to its 4 m × 4 m front face, neutrinos enter SBND over a range of angles from 0° to approximately 1.6°, enabling the detector to sample variations in the neutrino flux as a function of the angle—a technique known as precision reaction-independent spectrum measurement (PRISM), referred to here as SBND-PRISM. In this paper, we show how muon- and electron-neutrino fluxes vary as a function of the neutrino beam axis angle and how this can be exploited to expand the physics potential of SBND. We make use of a model that predicts an angle-dependent electron-neutrino excess signal to illustrate this effect, such as 𝜈 𝜇 → 𝜈 𝑒 oscillations. We present how SBND-PRISM provides a method to add robustness against uncertainties in cross-section modeling and, more generally, uncertainties that do not depend on the spatial position of neutrino interaction inside the detector. The fluxes, along with their associated covariance matrices, are made publicly available with this publication.

Abratenko, P. [Tufts University]↗

DOE OSTI2024

Order-parameter evolution in the Fulde-Ferrell-Larkin-Ovchinnikov phase

Here, we report on the temperature dependence of the spatially modulated spin-polarization amplitude ΔK spin , which is a hallmark of the superconducting Fulde-Ferrell-Larkin-Ovchinnikov (FFLO) state. For that, we use 13 C nuclear magnetic resonance (NMR) spectroscopy performed on the organic conductor β" -(ET) 2 SF 5 CH 2 CF 2 SO 3 . From a comparison of our experimental results to a comprehensive modeling of the 13 C NMR spectra, we determine the evolution of ΔK spin upon condensation of the FFLO state. Further, the modeling of the spectra in the superconducting phase allows to quantify the decrease of the average spin susceptibility, stemming from the spin-singlet coupling of the superconducting electron pairs in the FFLO state of β" -(ET) 2 SF 5 CH 2 CF 2 SO 3 .

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

DOE OSTI2020

Black hole echoes

In this work, we consider a very simple model for gravitational wave echoes from black hole merger ringdowns which may arise from local Lorentz symmetry violations that modify graviton dispersion relations. If the corrections are sufficiently soft so they do not remove the horizon, the reflection of the infalling waves which trigger the echoes is very weak. As an example, we look at the dispersion relation of a test scalar field corrected by rotonlike operators depending only on spatial momenta, in Gullstrand-Painlevé coordinates. The near-horizon regions of a black hole do become reflective, but only very weakly. The resulting “bounces” of infalling waves can yield repetitive gravity wave emissions but their power is very small. This implies that to see any echoes from black holes we really need an egregious departure from either standard GR or effective field theory, or both. One possibility to realize such strong echoes is the recently proposed classical firewalls which replace black hole horizons with material shells surrounding timelike singularities.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2024

Data-driven background model for the CUORE experiment

Here, we present the model we developed to reconstruct the CUORE radioactive background based on the analysis of an experimental exposure of 1038.4 kg yr. The data reconstruction relies on a simultaneous Bayesian fit applied to energy spectra over a broad energy range. The high granularity of the CUORE detector, together with the large exposure and extended stable operations, allow for an in-depth exploration of both spatial and time dependence of backgrounds. We achieve high sensitivity to both bulk and surface activities of the materials of the setup, detecting levels as low as 10 nBq kg −1 and 0.1 nBq cm −2 , respectively. We compare the contamination levels we extract from the background model with prior radio-assay data, which informs future background risk mitigation strategies. The results of this background model play a crucial role in constructing the background budget for the CUPID experiment as it will exploit the same CUORE infrastructure.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2020

Operator Lévy Flight: Light Cones in Chaotic Long-Range Interacting Systems

We argue that chaotic power-law interacting systems have emergent limits on information propagation, analogous to relativistic light cones, which depend on the spatial dimension d and the exponent α governing the decay of interactions. Using the dephasing nature of quantum chaos, we map the problem to a stochastic model with a known phase diagram. A linear light cone results for α ≥ $\textit{d}$ + 1/2. We also provide a Lévy flight (long-range random walk) interpretation of the results and show consistent numerical data for 1D long-range spin models with 200 sites.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2023

A General Spatiotemporal Imputation Framework for Missing Sensor Data

Many applications from precision agriculture, environmental monitoring and transportation networks rely on data collected across space and time over a large geographic area. Missing data poses a significant challenge for any data-driven inference and control tasks. Data imputation or the estimation of missing data can help fill these gaps by utilizing inherent spatial relationships and temporal patterns. A variety of spatiotemporal imputation models have been developed to address missing data in spatiotemporal datasets. However, these classical methods rely on the assumption that the underlying data follows a smooth trend and fail to provide accurate estimates when there is a large number of missing points in the data. Even though there are machine learning driven tensor completion approaches such as convolutional neural network based tensor completion (CoSTCo) that capture the non-linear relationships in the dataset, the transductive nature makes the algorithm less scalable. Thus, existing approaches for estimating the missing information do not effectively capture all dimensions of the spatiotemporal data structure, resulting in erroneous predictions and poor performance. The main contributions of this paper are: (1) We propose a novel inductive framework (G-LSTM) for missing data imputation that integrates a graph neural network with LSTMs to effectively capture both spatial and temporal dependencies. (2) Experimental results on a traffic dataset demonstrate that the proposed GNN integrated with an LSTM framework achieves improved imputation and maintains steady performance even when there are extreme missing conditions in comparison with the state-of-the-art imputation framework (i.e, CoSTCo). (3) The simulation results on a traffic network show up to 69% reduction in mean absolute error and 61% reduction in root mean square error when compared to CoSTCo.

Tharzeen, Aabila↗

DOE OSTI2025

Graph-Based Attention Mechanisms for Solving the AC Optimal Power Flow Problem in Electrical Power Networks

With the increasing complexity and data availability in modern power systems, learning-based approaches to AC Optimal Power Flow (AC OPF) have garnered significant attention. In particular, the structure of smart grids lends itself naturally to graph-based representations, where Graph Neural Networks (GNNs) can capture spatial and relational dependencies. This paper investigates attention-based GNN architectures tailored to heterogeneous graph representations of electric grids. We evaluate two major paradigms: relational attention, which distinguishes between edge types during message passing, and meta-path attention, which captures high-level semantics through multi-hop, typed paths. Using a large corpus of public AC OPF scenarios, we benchmark representative models of each type of attention. Our results demonstrate the benefits of heterogeneous attention-based models in accurately capturing grid dynamics; heterogeneous attention models achieve superior performance in both standard and perturbed settings. The findings highlight the importance of semantic-aware architectures for improving prediction robustness and interpretability in power system applications.

Trigui, Ali [Qubit Engineering Inc.]↗

DOE OSTI2024

PANDA-FES: Portable and Adaptable Neutron Diagnostics for Advancing Fusion Energy Science

Nuclear fusion is a potential source of carbon-free electricity with many concepts in development. The Portable and Adaptable Neutron Diagnostics for Advancing Fusion Energy Science (PANDA-FES) suite has been deployed since 2021 to measure neutron yield, energy, and spatiotemporal source location at two different Z-pinch fusion devices. This diagnostic can be used at a variety of facilities pursuing fusion in the magnetic, inertial, and magneto-inertial regimes. These different regimes have a wide range of time scales from less than 100 ns to a few μ s, neutron yields from 10 6 to 10 11 , and noise environments. Neutron yield is measured through activation of 79 Br and 89 Y with calibrated detectors. Temporal, spatial, and energy dependence of neutrons is measured with scintillators coupled to photomultiplier tubes (PMTs). Experimental setups and data analysis methods have been developed for these conditions. Finally, neutron yield, neutron energy anisotropy, and spatiotemporal evolution of the source have been measured.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2025

Characterizing Near-Surface Moisture Increase during the Clear-Sky Afternoon-to-Evening Transition Using a Single-Column Model

The afternoon-to-evening transition is the period when the atmospheric boundary layer transitions from convective to stable conditions. One noticeable feature during this transition period is the rapid increase in water vapor concentration near the surface. However, the mechanism behind the increase in water vapor remains poorly understood. This study investigated the processes contributing to the water vapor increase and the impacts of the land cover and horizontal advection on the water vapor increase using a single-column model for the clear-sky condition. Numerical experiments were conducted on three cases at the Atmospheric Radiation Measurement (ARM) Southern Great Plains (SGP) site. Evapotranspiration was found to be the main moisture source to the water vapor increase, and that change of the near-surface vertical temperature gradient from negative to positive is the trigger for this increase during the afternoon-to-evening transition. This is because the near-surface turbulence divergence term decreases due to the change in the buoyancy profile caused by vertical temperature gradient change. The impact of horizontal advection on water vapor varies, and it can either lead to an increase or a decrease in water vapor, depending on the spatial horizontal water vapor gradient. This study also found that land cover can influence the timing of the water vapor increase since different land covers may have different Bowen ratios. Water vapor increases earlier under conditions with smaller Bowen ratios compared to those with larger values.

Atmosphere-land interaction↗

DOE OSTI2020

Intermodel Spread in the Pattern Effect and Its Contribution to Climate Sensitivity in CMIP5 and CMIP6 Models

Radiative feedbacks depend on the spatial patterns of sea surface temperature (SST) and thus can change over time as SST patterns evolve—the so-called pattern effect. In this study, we investigate intermodel differences in the magnitude of the pattern effect and how these differences contribute to the spread in effective equilibrium climate sensitivity (ECS) within CMIP5 and CMIP6 models. Effective ECS in CMIP5 estimated from 150-yr-long abrupt4×CO2 simulations is on average 10% higher than that estimated from the early portion (first 50 years) of those simulations, which serves as an analog for historical warming; this difference is reduced to 7% on average in CMIP6. The (negative) net radiative feedback weakens over the course of the abrupt4×CO2 simulations in the vast majority of CMIP5 and CMIP6 models, but this weakening is less dramatic on average in CMIP6. For both ensembles, the total variance in the effective ECS is found to be dominated by the spread in radiative response on fast time scales, rather than the spread in feedback changes. Using Green’s functions derived from two AGCMs shows that the spread in feedbacks on fast time scales may be primarily due to differences in atmospheric model physics, whereas the spread in feedback evolution is primarily governed by differences in SST patterns. Intermodel spread in feedback evolution is well explained by differences in the relative warming in the West Pacific warm-pool regions for the CMIP5 models, but this relation fails to explain differences across the CMIP6 models, suggesting that a stronger sensitivity of extratropical clouds to surface warming may also contribute to feedback changes in CMIP6.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2022

On the Diffusivity of Moist Static Energy and Implications for the Polar Amplification Response to Climate Warming

Energy balance models (EBMs) have been widely used in a range of climate problems, but the assumption of constant diffusivity in the parameterization of the moist static energy (MSE) flux can be hardly justified. We demonstrate in this study that the diffusive MSE flux can be derived from the basic energy balance equation with a few tolerable assumptions. The estimated diffusivity is both spatially and seasonally dependent, and its midlatitude average is then tested against several scaling theories for the midlatitude eddy diffusivity. The result supports the diffusivity theory of Held and Larichev (1996) modified for the moist atmosphere, affording a dynamics-based parameterization of MSE diffusivity. The implementation of the parameterization in an EBM leads to an interactive MSE diffusivity that accounts for the midlatitude eddy response to climate forcing perturbations. Under a uniform radiative forcing, the EBM with a diffusivity so parameterized produces a weakening of the midlatitude diffusivity and a modestly polar-amplified surface temperature response as an inevitable outcome under the dual constraints of the nonlinear Clausius-Clapeyron relation and the temperature gradient-dependent diffusivity, even in the absence of any poleward amplifying radiative feedbacks. As the consequence of more isothermal temperature and reduced diffusivity, the variance of the midlatitude surface temperature also decreases with warming.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2020

Characterization of Extreme Wave Conditions for Wave Energy Converter Design and Project Risk Assessment

Best practices and international standards for determining n-year return period extreme wave (sea states) conditions allow wave energy converter designers and project developers the option to apply simple univariate or more complex bivariate extreme value analysis methods. The present study compares extreme sea state estimates derived from univariate and bivariate methods and investigates the performance of spectral wave models for predicting extreme sea states at buoy locations within several regional wave climates along the US East and West Coasts. Two common third-generation spectral wave models are evaluated, a WAVEWATCH III® model with a grid resolution of 4 arc-minutes (6–7 km), and a Simulating WAves Nearshore model, with a coastal resolution of 200–300 m. Both models are used to generate multi-year hindcasts, from which extreme sea state statistics used for wave conditions characterization can be derived and compared to those based on in-situ observations at National Data Buoy Center stations. Comparison of results using different univariate and bivariate methods from the same data source indicates reasonable agreement on average. Discrepancies are predominantly random. Large discrepancies are common and increase with return period. There is a systematic underbias for extreme significant wave heights derived from model hindcasts compared to those derived from buoy measurements. This underbias is dependent on model spatial resolution. However, simple linear corrections can effectively compensate for this bias. A similar approach is not possible for correcting model-derived environmental contours, but other methods, e.g., machine learning, should be explored.

16 TIDAL AND WAVE POWER↗

NASA NTRS1972

The stability of motion of satellites with flexible appendages

The mathematical formulation associated with the problem of stability of motion of a satellite consisting of a main rigid body and three (or less) pairs of flexible rods is presented. The rods are capable of flexure in two orthogonal directions. Whereas the rotational motion of the body is described by generalized coordinates depending on time alone, the elastic displacements of the rods depend both on spatial position and time. Assuming no external torques, there exist motion integrals in the form of momentum integrals. These integrals can be regarded as constraint equations relating the system velocities, and used to reduce the number of variables describing the motion. The stability analysis has been carried out by means of an extension of the Liapunov direct method. Since the elastic vibrations result in energy dissipation, it is shown that the equilibrium position is asymptotically stable if the Hamiltonian is positive definite and unstable if it can take negative values in the neighborhood of the equilibrium. Determining the sign definiteness of the Hamiltonian is complicated by the fact that it contains spatial derivatives of the elastic displacements. Two methods are presented to cope with this problem. The first, the standard modal analysis in conjunction with series truncation, develops criteria in terms of infinite series associated with the natural modes and frequencies of the elastic rods. The second, the method of integral coordinates, yields closed-form stability criteria involving the system parameters, such as the body moments of inertia, the length and mass distribution of the rods, the lowest natural frequencies of the rods, and the satellite spin velocity.

Meirovitch, L.↗

NASA NTRS1975

Hot-cold plasma interactions in the earth's magnetosphere

The paper gives a synopsis of observations and results obtained from the Explorer-45 satellite which pertain directly to the interaction of the magnetospheric hot and cold plasma populations. The specific case discussed is the interaction of the hot ring current plasma with the cold plasmaspheric plasma in the evening to early morning local time sector during magnetic storm recovery phase. It was found that above the plasmapause region, the hot ring current plasma is stable with negligible losses due to pitch angle diffusion; the hot ring current plasma enters a moderate pitch angle regime in the plasmapause region, whereby the addition of cold plasma destabilizes the hot plasma. Analysis of the energy, spatial, and temporal dependence of the above destabilization along with the ion-cyclotron resonant energy equation, and comparison of this analysis with an in situ estimate of the plasma density strongly indicate that the mechanism responsible for destabilization of the hot plasma is the amplification of ion-cyclotron waves due to the interaction of the cold plasmaspheric plasma with the hot ring current plasma in a manner similar to that discussed by Cornwall et al. (1970).

Williams, D. J.↗

NASA NTRS1979

Microplume model of spatial-yield spectra

An analytic representation for the spatial (radial and longitudinal) yield spectra is developed in terms of a model containing three simple 'microplumes'. The model is applied to electron energy degradation in molecular nitrogen gas for 0.1 to 5 keV incident electrons. From the nature of the cross section input to this model it is expected that the scaled spatial yield spectra for other gases will be quite similar. The model indicates that each excitation, ionization, etc. plume should have its individual spatial and energy dependence. Extensions and aeronomical and radiological applications of the model are discussed.

Green, A. E. S.↗