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At least 1,189 records · Page 66
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

What drives the scatter of local star-forming galaxies in the BPT diagrams? A Machine Learning based analysis

ABSTRACT We investigate which physical properties are most predictive of the position of local star forming galaxies on the BPT diagrams, by means of different Machine Learning (ML) algorithms. Exploiting the large statistics from the Sloan Digital Sky Survey (SDSS), we define a framework in which the deviation of star-forming galaxies from their median sequence can be described in terms of the relative variations in a variety of observational parameters. We train artificial neural networks (ANN) and random forest (RF) trees to predict whether galaxies are offset above or below the sequence (via classification), and to estimate the exact magnitude of the offset itself (via regression). We find, with high significance, that parameters primarily associated to variations in the nitrogen-over-oxygen abundance ratio (N/O) are the most predictive for the [N ii]-BPT diagram, whereas properties related to star formation (like variations in SFR or EW(H α)) perform better in the [S ii]-BPT diagram. We interpret the former as a reflection of the N/O–O/H relationship for local galaxies, while the latter as primarily tracing the variation in the effective size of the S+ emitting region, which directly impacts the [S ii] emission lines. This analysis paves the way to assess to what extent the physics shaping local BPT diagrams is also responsible for the offsets seen in high redshift galaxies or, instead, whether a different framework or even different mechanisms need to be invoked.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

Machine learning-enabled phenotyping for GWAS and TWAS of WUE traits in 869 field-grown sorghum accessions

Abstract Sorghum (Sorghum bicolor) is a model C4 crop made experimentally tractable by extensive genomic and genetic resources. Biomass sorghum is studied as a feedstock for biofuel and forage. Mechanistic modeling suggests that reducing stomatal conductance (gs) could improve sorghum intrinsic water use efficiency (iWUE) and biomass production. Phenotyping to discover genotype-to-phenotype associations remains a bottleneck in understanding the mechanistic basis for natural variation in gs and iWUE. This study addressed multiple methodological limitations. Optical tomography and a machine learning tool were combined to measure stomatal density (SD). This was combined with rapid measurements of leaf photosynthetic gas exchange and specific leaf area (SLA). These traits were the subject of genome-wide association study and transcriptome-wide association study across 869 field-grown biomass sorghum accessions. The ratio of intracellular to ambient CO2 was genetically correlated with SD, SLA, gs, and biomass production. Plasticity in SD and SLA was interrelated with each other and with productivity across wet and dry growing seasons. Moderate-to-high heritability of traits studied across the large mapping population validated associations between DNA sequence variation or RNA transcript abundance and trait variation. A total of 394 unique genes underpinning variation in WUE-related traits are described with higher confidence because they were identified in multiple independent tests. This list was enriched in genes whose Arabidopsis (Arabidopsis thaliana) putative orthologs have functions related to stomatal or leaf development and leaf gas exchange, as well as genes with nonsynonymous/missense variants. These advances in methodology and knowledge will facilitate improving C4 crop WUE.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

The entropy of galaxy spectra: how much information is encoded?

Abstract The inverse problem of extracting the stellar population content of galaxy spectra is analysed here from a basic standpoint based on information theory. By interpreting spectra as probability distribution functions, we find that galaxy spectra have high entropy, thus leading to a rather low effective information content. The highest variation in entropy is unsurprisingly found in regions that have been well studied for decades with the conventional approach. We target a set of six spectral regions that show the highest variation in entropy – the 4000 Å break being the most informative one. As a test case with real data, we measure the entropy of a set of high-quality spectra from the Sloan Digital Sky Survey, and contrast entropy-based results with the traditional method based on line strengths. The data are classified into star-forming (SF), quiescent (Q), and active galactic nucleus (AGN) galaxies, and show – independently of any physical model – that AGN spectra can be interpreted as a transition between SF and Q galaxies, with SF galaxies featuring a more diverse variation in entropy. The high level of entanglement complicates the determination of population parameters in a robust, unbiased way, and affects traditional methods that compare models with observations, as well as machine learning (especially deep learning) algorithms that rely on the statistical properties of the data to assess the variations among spectra. Entropy provides a new avenue to improve population synthesis models so that they give a more faithful representation of real galaxy spectra.

Ferreras, Ignacio (ORCID:0000000345843127)↗
DOE OSTI2020

Intraspecific variability in root traits and edaphic conditions influence soil microbiomes across 12 switchgrass cultivars

Microbial communities help plants access nutrients and tolerate stress. Some microbiomes are specific to plant genotypes and, therefore, may contribute to intraspecific differences in plant growth and be a promising target for plant breeding. Switchgrass (Panicum virgatum L.) is a potential bioenergy crop with broad variation in yields and environmental responses; recent studies suggest that associations with distinct microbiomes may contribute to variation in cultivar yields. We used a common garden experiment to investigate variation in 12 mature switchgrass cultivar soil microbiomes and, further, to examine how root traits and soil conditions influence microbiome structure. Here, we found that average root diameter varied up to 33% among cultivars and that they associated with distinct soil microbiomes. Cultivar had a larger effect on the soil bacterial than fungal community, but both were strongly influenced by soil properties. Root traits had a weaker effect on microbiome structure, but root length contributed to variation in the fungal community. Unlike the soil communities, the root bacterial communities did not group by cultivar, based on a subset of samples. Microbial biomass carbon and nitrogen and the abundance of several dominant bacterial phyla varied between ecotypes, but overall the differences in soil microbiomes were greater among cultivars than between ecotypes. Our findings show that there is not one soil microbiome that applies to all switchgrass cultivars, or even to each ecotype. These subtle but significant differences in root traits, microbial biomass, and the abundance of certain soil bacteria could explain differences in cultivar yields and environmental responses.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Analysis of Long-Term Quality Control Data for a 137 Cs Dosimetry Calibration Source

Strict quality assurance programs are required for many radiological applications, but these seldom exist for verifying dosimetry calibration sources. After initial characterization of a dosimetry calibration facility, quality control procedures are recommended to ensure the early detection of any changes or malfunctions. These also result in refined knowledge about average dose rate and experimental variations in dose delivery. This paper describes the implementation of a phase I quality control protocol for a 137 Cs dosimetry calibration source and includes an analysis of the resulting data collected over a 24-mo period. During this time, substantial data was collected to establish trial control limits. Air kerma rate measurements were obtained using an ion chamber and were adjusted for decay, corrected for ambient temperature, pressure and humidity, and then analyzed using quality control charts. Three variations of rational subgrouping methods were used in order to find assignable causes of error, and Nelson's Rules were followed to detect any non-random statistical variations. Measurements were subgrouped according to same-day measurements in order to detect positional errors as well as atmospheric correction errors. Additionally, measurements were subgrouped according to analogous experimental setups in order to detect failure in equipment or incorrect settings. Both were analyzed using the X-bar and R chart method. Similarly, individuals and moving ranges charts were used to carefully examine each position in order to observe any situational errors that may occur which include timing, positional, or interference errors. Each method was successful in identifying unique out-of-control data points that occurred during the phase I application of forming control limits. Furthermore, over the 24-mo period, enough data points were deemed in-control to establish reliable trial limits. Future experiments will include the phase II application of gaining more reliable measurements in order to fine-tune the limits, as well as performing a designed experiment, where variables are purposefully changed in order to test the variation of the data.

61 RADIATION PROTECTION AND DOSIMETRY↗
DOE OSTI2025

Constraints on the finite volume two-nucleon spectrum at 𝑚𝜋 ≈806 MeV

The low-energy, finite-volume spectrum of the two-nucleon system at a quark mass corresponding to a pion mass of 𝑚𝜋≈806 MeV is studied with lattice quantum chromodynamics (LQCD) using variational methods. The interpolating-operator sets used in [Variational study of two-nucleon systems with lattice QCD, Phys. Rev. D 107, 094508 (2023).] are extended by including a complete basis of local hexaquark operators, as well as plane-wave dibaryon operators built from products of both positive- and negative-parity nucleon operators. Results are presented for the isosinglet and isotriplet two-nucleon channels. In both channels, noticeably weaker variational bounds on the lowest few energy eigenvalues are obtained from operator sets which contain only hexaquark operators or operators constructed from the product of two negative-parity nucleons, while other operator sets produce low-energy variational bounds which are consistent within statistical uncertainties. The consequences of these studies for the LQCD understanding of the two-nucleon spectrum are investigated.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2024

TETRIS-ADAPT-VQE: An adaptive algorithm that yields shallower, denser circuit Ansätze

Adaptive quantum variational algorithms are particularly promising for simulating strongly correlated systems on near-term quantum hardware, but they are not yet viable due, in large part, to the severe coherence time limitations on current devices. In this paper, we introduce an algorithm called TETRIS-ADAPT-VQE (tiling efficient trial circuits with rotations implemented simultaneously adaptive derivative-assembled problem-tailored variational quantum eigensolver), which iteratively builds up variational a few operators at a time in a way dictated by the problem being simulated. This algorithm is a modified version of the ADAPT-VQE algorithm, in which the one-operator-at-a-time rule is lifted to allow for the addition of multiple operators with disjoint supports in each iteration. TETRIS-ADAPT-VQE results in denser but significantly shallower circuits, without increasing the number of controlled- gates or variational parameters. Its advantage over the original algorithm in terms of circuit depths increases with the system size. Moreover, the expensive step of measuring the energy gradient with respect to each candidate unitary at each iteration is performed only a fraction of the time compared with ADAPT-VQE. These improvements bring us closer to the goal of demonstrating a practical quantum advantage on quantum hardware. Published by the American Physical Society 2024

Anastasiou, Panagiotis G. (ORCID:0000000256601791)↗
DOE OSTI2024

Device Feasibility Analysis of Multi-level FeFETs for Neuromorphic Computing

As an emerging non-volatile memory device technology, Ferroelectric Field-Effect Transistors (FeFETs) can enable low-power, adaptive intelligent system design. However, device dimension and operating voltage dependent reliability issues of scaled FeFETs can ultimately lead to degraded performance in solving machine learning tasks. In this article, detailed experimental characterization of FeFET devices of different dimensions have been carried out to explicitly evaluate the non-ideal behavior in device conductance programming properties like number of programming states, cycle-to-cycle (C2C) variations, device-to-device (D2D) variations, and state retention. A hardware-aware software simulation approach has been adopted to capture the adversarial effects of the non-idealities on recognition accuracy through algorithm-level performance assessment by including them in NeuroSim, a popular neural network hardware simulator, to execute a neural network model considering all other hardware constraints. With the added non-idealities, significant accuracy degradation has been observed compared to the ideal scenarios where D2D variations play the most critical role. Thereafter, feasibility of a variation-aware training method has been evaluated to tackle the accuracy drop.

42 ENGINEERING↗
DOE OSTI2023

Solving Unit Commitment Problems with Demand Responsive Loads

This work focuses on using variations of the Frank-Wolfe (FW) algorithm for solving unit commitment problems with high volumes of demand responsive loads on the power grid. We present a formulation of the unit commitment problem with demand responsive loads. We then show through reformulation and relaxations of the problem that variations of the Frank-Wolfe algorithm can be used to determine the time series decisions for the demand responsive loads. We show through computational experiments on the IEEE Reliability Test System that the timeseries of demand responsive load decisions obtained through our approach are near optimal and describe how large-scale parallel implementations of our approach can be highly computationally efficient.

demand response↗
DOE OSTI2023

A Novel Design for Improving the Control on the Stainless-Steel Vessel Welding Process for Superconducting Magnets

Stainless steel vessels see widespread use in superconducting magnets for particle accelerator applications. Their function varies in different magnet designs: they always provide the necessary liquid helium containment, but in some cases are also used to provide azimuthal prestress and can also be welded to the magnet end plate to provide additional longitudinal stiffness. A magnet designed with the bladder and key technology does not rely on the structural role of the vessel. They are structurally supported using azimuthally prestressed aluminum shells, and the longitudinal constraint by rods. In this case, the magnet designer would generally like to minimize the interaction between the magnet and the stainless-steel vessel and to minimize the coil stress variation due to the vessel. The stress state in the vessel and in the coil is a function of the circumferential interference, defined as the vessel azimuthal length minus the magnet circumference. The vessel and the magnet azimuthal length machining tolerances are relatively large resulting in significant stress variations in the superconducting coils. Here in this paper we introduce an interference-control shim, which can significantly limit the stress variation of the coils for a given variation of the interference. The effectiveness of the interference-control shim is evaluated numerically on the MQXF, the low-β quadrupole for the High Luminosity LHC.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2021

Clutter Distributions for Tomographic Image Standardization in Ground-Penetrating Radar

Multistatic ground-penetrating radar (GPR) signals can be imaged tomographically to produce 3-D distributions of image intensities. In the absence of objects of interest, these intensities can be considered to be estimates of clutter. These clutter intensities spatially vary over several orders of magnitude and vary across different arrays, which makes a direct comparison of these raw intensities difficult. However, by gathering statistics on these intensities and their spatial variation, a variety of metrics can be determined. In this study, the clutter distribution is found to fit better to a two-parameter Weibull distribution than Gaussian or log-normal distributions. Based on the spatial variation of the two Weibull parameters, scale and shape, more information may be gleaned from these data. How well the GPR array is illuminating various parts of the ground, in depth and cross track, may be determined from the spatial variation of the Weibull scale parameter, which may in turn be used to estimate an effective attenuation coefficient in the soil. The transition in depth from clutter- to noise-limited conditions (which is one possible definition of GPR penetration depth) can be estimated from the spatial variation of the Weibull shape parameter. Lastly, the underlying clutter distributions also provide an opportunity to standardize image intensities to determine when a statistically significant deviation from background (clutter) has occurred, which is convenient for buried threat detection algorithm development that needs to be robust across multiple different arrays.

42 ENGINEERING↗
DOE OSTI2021

Above-ground net primary productivity in regenerating seasonally dry tropical forest: Contributions of rainfall, forest age and soil

Identifying factors controlling forest productivity is critical to understanding forest-climate change feedbacks, modelling vegetation dynamics and carbon finance schemes. However, little research has focused on productivity in regenerating tropical forests which are expanding in their fraction of global area have an order of magnitude larger carbon uptake rates relative to older forest. Here We examined above-ground net primary productivity (ANPP) and its components (wood production and litterfall) over 10 years in forest plots that vary in successional age, soil characteristics and species composition using band dendrometers and litterfall traps in regenerating seasonally dry tropical forests in northwestern Costa Rica. We show that the components of ANPP are differentially driven by age and annual rainfall and that local soil variation is important. Total ANPP was explained by a combination of age, annual rainfall and soil variation. Wood production comprised 35% of ANPP on average across sites and years, and was explained by annual rainfall but not forest age. Conversely, litterfall increased with forest age and soil fertility yet was not affected by annual rainfall. In this region, edaphic variability is highly correlated with plant community composition. Thus, variation in ecosystem processes explained by soil may also be partially explained by species composition. These results suggest that future changes in annual rainfall can alter the secondary forest carbon sink, but this effect will be buffered by the litterfall flux which varies little among years. In determining the long-term strength of the secondary forest carbon sink, both rainfall and forest age will be critical variables to track. We also conclude that detailed understanding of local site variation in soils and plant community may be required to accurately predict the impact of changing rainfall on forest carbon uptake. Synthesis. We show that in seasonally dry tropical forest, annual rainfall has a positive relationship with the growth of above-ground woody tissues of trees and that droughts lead to significant reductions in above-ground productivity. These results provide evidence for climate change—carbon cycle feedbacks in the seasonal tropics and highlight the value of longitudinal data on forest regeneration.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Topological Characterization and Uncertainty Visualization of Atmospheric Rivers

Atmospheric rivers (ARs) are long, narrow regions of water vapor in the Earth's atmosphere that transport heat and moisture from the tropics to the mid-latitudes. ARs are often associated with extreme weather events in North America and contribute significantly to water supply and flood risk. However, characterizing ARs has been a major challenge due to the lack of a universal definition and their structural variations. Existing AR detection tools (ARDTs) produce distinct AR boundaries for the same event, making the risk assessment of ARs a difficult task. Understanding these uncertainties is crucial to improving the predictability of AR impacts, including their landfall areas and associated precipitation, which could cause catastrophic flooding and landslides over the coastal regions. In this work, we develop an uncertainty visualization framework that captures boundary and interior uncertainties, i.e., structural variations, of an ensemble of ARs that arise from a set of ARDTs. We first provide a statistical overview of the AR boundaries using the contour boxplots of Whitaker et al. that highlight the structural variations of AR boundaries based on their nesting relationships. We then introduce the topological skeletons of ARs based on Morse complexes that characterize the interior variation of an ensemble of ARs. We propose an uncertainty visualization of these topological skeletons, inspired by MetroSets of Jacobson et al. that emphasizes the agreements and disagreements across the ensemble members. Through case studies and expert feedback, we demonstrate that the two approaches complement each other, and together they could facilitate an effective comparative analysis process and provide a more confident outlook on an AR's shape, area, and onshore impact.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

The response of stomatal conductance to seasonal drought in tropical forests

Stomata regulate CO2 uptake for photosynthesis and water loss through transpiration. The approaches used to represent stomatal conductance (gs) in models vary. In particular, current understanding of drivers of the variation in a key parameter in those models, the slope parameter (i.e. a measure of intrinsic plant water-use-efficiency), is still limited, particularly in the tropics. Here we collected diurnal measurements of leaf gas exchange and water potential (?leaf), and a suite of plant traits from the upper canopy of 15 tropical trees in two contrasting Panamanian forests throughout the dry season of the 2016 El Niño. The plant traits included wood density, leaf-mass-per-area (LMA), leaf carboxylation capacity (Vc,max25), leaf water content, the degree of isohydry, and predawn ?leaf. We first investigated how the choice of four commonly used leaf-level gs models with and without the inclusion of ?leaf as an additional predictor variable influence the ability to predict gs, and then explored the abiotic (i.e. month, site-month interaction) and biotic (i.e. tree-species-specific characteristics) drivers of slope parameter variation. Our results show that the inclusion of ?leaf did not improve model performance and that the models that represent the response of gs to vapor pressure deficit performed better than corresponding models that respond to relative humidity. Within each gs model, we found large variation in the slope parameter, and this variation was attributable to the biotic driver, rather than abiotic drivers. We further investigated potential relationships between the slope parameter and the six available plant traits mentioned above, and found that only one trait, LMA, had a significant correlation with the slope parameter (R2=0.66, n=15), highlighting a potential path towards improved model parameterization. This study advances understanding of gs dynamics over seasonal drought, and identifies a practical, trait-based approach to improve modeling of carbon and water exchange in tropical forests.

carbon and water exchange, stomatal conductance mo↗
DOE OSTI2021

Intrinsic and extrinsic factors interact during development to influence telomere length in a long‐lived reptile

Abstract The mechanisms connecting environmental conditions to plasticity in biological aging trajectories are fundamental to understanding individual variation in functional traits and life history. Recent findings suggest that telomere biology is especially dynamic during early life stages and has long‐term consequences for subsequent reproduction and survival. However, our current understanding is mostly derived from studies investigating ecological and anthropogenic factors separately, leaving the effects of complex environmental interactions unresolved. American alligators ( Alligator mississippiensis ) are long‐lived apex predators that rely on incubation temperature during a discrete period during development and endocrine cues to determine sex, making them especially vulnerable to current climatic variability and exposure to anthropogenic contaminants interfering with hormone function. Here, we combine field studies with a factorial design to understand how the developmental environment, along with intrinsic biological variation contribute to persistent telomere variation. We found that exposure to a common endocrine disrupting contaminant, DDE, affects telomere length, but that the directionality is highly dependent upon incubation temperature. Variation in hatchling growth, underlies a strong clutch effect. We also assess concentrations of a panel of glucocorticoid hormones and find that contaminant exposure elicits an increase in circulating glucocorticoids. Consistent with emerging evidence linking stress and aging trajectories, GC levels also appear to trend with shorter telomere length. Thus, we add support for a mechanistic link between contaminants and glucocorticoid signalling, which interacts with ecological aspects of the developmental environment to alter telomere dynamics.

Bae, Junsoo↗
DOE OSTI2022

Combining GWAS and population genomic analyses to characterize coevolution in a legume‐rhizobia symbiosis

Abstract The mutualism between legumes and rhizobia is clearly the product of past coevolution. However, the nature of ongoing evolution between these partners is less clear. To characterize the nature of recent coevolution between legumes and rhizobia, we used population genomic analysis to characterize selection on functionally annotated symbiosis genes as well as on symbiosis gene candidates identified through a two‐species association analysis. For the association analysis, we inoculated each of 202 accessions of the legume host Medicago truncatula with a community of 88 Sinorhizobia (Ensifer) meliloti strains. Multistrain inoculation, which better reflects the ecological reality of rhizobial selection in nature than single‐strain inoculation, allows strains to compete for nodulation opportunities and host resources and for hosts to preferentially form nodules and provide resources to some strains. We found extensive host by symbiont, that is, genotype‐by‐genotype, effects on rhizobial fitness and some annotated rhizobial genes bear signatures of recent positive selection. However, neither genes responsible for this variation nor annotated host symbiosis genes are enriched for signatures of either positive or balancing selection. This result suggests that stabilizing selection dominates selection acting on symbiotic traits and that variation in these traits is under mutation‐selection balance. Consistent with the lack of positive selection acting on host genes, we found that among‐host variation in growth was similar whether plants were grown with rhizobia or N‐fertilizer, suggesting that the symbiosis may not be a major driver of variation in plant growth in multistrain contexts.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Phylogeography of the blacklegged tick ( Ixodes scapularis ) throughout the USA identifies candidate loci for differences in vectorial capacity

Abstract The blacklegged tick ( Ixodes scapularis ( Journal of the Academy of Natural Sciences of Philadelphia , 1821, 2 , 59)) is a vector of Borrelia burgdorferi sensu stricto ( s.s .) ( International Journal of Systematic Bacteriology , 1984, 34 , 496), the causative bacterial agent of Lyme disease, part of a slow‐moving epidemic of Lyme borreliosis spreading across the northern hemisphere. Well‐known geographical differences in the vectorial capacity of these ticks are associated with genetic variation. Despite the need for detailed genetic information in this disease system, previous phylogeographical studies of these ticks have been restricted to relatively few populations or few genetic loci. Here we present the most comprehensive phylogeographical study of genome‐wide markers in I. scapularis , conducted by using 3RAD (triple‐enzyme restriction‐site associated sequencing) and surveying 353 ticks from 33 counties throughout the species' range. We found limited genetic variation among populations from the Northeast and Upper Midwest, where Lyme disease is most common, and higher genetic variation among populations from the South. We identify five spatially associated genetic clusters of I. scapularis . In regions where Lyme disease is increasing in frequency, the I. scapularis populations genetically group with ticks from historically highly Lyme‐endemic regions. Finally, we identify 10 variable DNA sites that contribute the most to population differentiation. These variable sites cluster on one of the chromosome‐scale scaffolds for I. scapularis and are within identified genes. Our findings illuminate the need for additional research to identify loci causing variation in the vectorial capacity of I. scapularis and where additional tick sampling would be most valuable to further understand disease trends caused by pathogens transmitted by I. scapularis .

3RAD↗