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All sourcesNASA NTRSDOE OSTI
DOE OSTI2018

Lepton Pair Production in Neutrino–Nucleus Scattering

The impact of the emission of new light gauge bosons in neutrino–nucleus scattering on the observed signal is studied. As an example, a model with a neutral vector boson Z {sup ′} with a mass of a few tens of MeV, interacting with neutrinos via the V−A current, is considered. An equivalent Z {sup ′} boson spectrum for neutrinos is introduced. The corresponding correction to the total neutrino–nucleus scattering cross section as a function of the collision energy is calculated. This correction for several values of the model parameters allowed by the existing experimental bounds is presented.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2019

Function Preserving Projection

With function preserving projection (FPP), we aim to address high-dimensional data exploration challenges common in many scientific applications. Instead of preserving the global or local geometric structure of a dataset like traditional dimensionality reduction methods (e.g., principal component analysis, t-stochastic neighborhood embedding), the proposed approach finds a linear projection that reveal interpretable (yet potentially nonlinear) pattern of the properties of interests defined on a highdimensional domain (e.g., simulation yield defined on the input parameter space). The key insight driving FPP is the fact that while non-linear embeddings are difficult to interpret, humans are highly skilled in understanding complex visual patterns. FPP lets users select the complexity of a pattern they consider interpretable, allows to find a joint projection that explains multiple functions, and provides a confidence statistic (akin to a p-value) indicating how salient the resulting pattern is. We demonstrate the efficacy of the proposed method on several synthetic and scientific datasets for both continuous (regression) and discrete (classification) problems of interests and illustrate the scalability of FPP by finding an optimal projection for close to 40 gigabytes in around 10 minutes.

Liu, SHUSEN↗
DOE OSTI2026

Data for "Depth of nutrient uptake by deep-rooted plants is regulated by water availability"

The data set consists of strontium (Sr) isotope ratios (87Sr/86Sr), water isotopes, soil cation concentrations, soil water potential sensor data, and results of 87Sr/86Sr mixing model. The plant canopy size files include the dataset of canopy dimension of sagebrush, lupine, and sunflower. The soil and plant ICPMS (Inductively Coupled Plasma Mass Spectrometry) data file includes both of 87Sr/86Sr, and cation concentration dataset from soil exchangeable pool, apatite pool, silicate extract, atmospheric rain deposition, and plant leaf and stem tissues. The plant dendrochronology file includes the dendrochronogical ring width of several sagebrush, and dendrochemical sample data includes the 87Sr/86Sr for each separated growth ring. The modeling result gives the proportion of nutrient sources of each plants (based on their 87Sr/86Sr in leaf tissues and growth rings) from atmospheric deposition and mineral weathering. Soil water potential data includes continuous collection of soil water potential dataset at 2 depths (30 cm and 60 cm, from Nov 24 - Jun 25) of the sampling site. All the samples were collected from 2 sampling campaign June and July 2023, and rain water is a separate sampling from Aug - Sept 2023, at north-facing hillslope near pumphouse site. The data showed that the depth of cation nutrient acquisition is thus tightly coupled with, and likely determined by, water availability in soil, saprolite and bedrock. The enhanced uptake of cations and water from regions of mineral weathering could confer plant and ecosystem resilience during low water years and may impact the rate of bedrock weathering and watershed chemistry during drought. This dataset includes a file-level metadata (flmd.csv) file that lists each file contained in the dataset with associated metadata; a data dictionary (dd.csv) file that contains column/row headers used throughout the files along with a definition, units, and data type; a location metadata file (locations.csv); and a samples metadata file (samples.csv). All files are provided as comma-separated values (CSV) files (.csv). This work was supported by the Watershed Function Science Focus Area at Lawrence Berkeley National Laboratory funded by the US Department of Energy, Office of Science, Biological and Environmental Research under Contract No. DE-AC02-05CH11231.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2018

Overview of distributed energy storage for demand charge reduction

Utilities bill customers not only on energy use but peak power use since transmission costs are a function of power and not energy. Energy storage (ES) can deliver value to utility customers by leveling building demand and reducing demand charges. With increasing distributed energy generation and greater building demand variability, utilities have raised demand charges and are even including them in residential electricity bills. This paper will present a comprehensive overview of electrical and thermal energy storage technologies but will focus on mid-size energy storage technologies for demand charge avoidance in commercial and industrial applications. Of the ES technologies surveyed, lithium ion batteries deliver the highest value for demand charge reduction especially with systems that have larger power to energy ratios. Current lithium ion ES systems have payback periods below 5 years when deployed in markets with high demand charges.

25 ENERGY STORAGE↗
DOE OSTI2022

Revisiting Theoretical Tools and Approaches for the Valorization of Recalcitrant Lignocellulosic Biomass to Value-Added Chemicals

Biorefinery processes for converting lignocellulosic biomass to fuels and chemicals proceed via an integrated series of steps. Biomass is first pretreated and deconstructed using chemical catalysts and/or enzymes to liberate sugar monomers and lignin fragments. Deconstruction is followed by a conversion step in which engineered host organisms assimilate the released sugar monomers and lignin fragments, and produce value-added fuels and chemicals. Over the past couple of decades, a significant amount of work has been done to develop innovative biomass deconstruction and conversion processes that efficiently solubilize biomass, separate lignin from the biomass, maximize yields of bioavailable sugars and lignin fragments and convert the majority of these carbon sources into fuels, commodity chemicals, and materials. Herein, we advocate that advanced in silico approaches provide a theoretical framework for developing efficient processes for lignocellulosic biomass valorization and maximizing yields of sugars and lignin fragments during deconstruction and fuel and chemical titers during conversion. This manuscript surveys the latest developments in lignocellulosic biomass valorization with special attention given to highlighting computational approaches used in process optimization for lignocellulose pretreatment; enzyme engineering for enhanced saccharification and delignification; and prediction of the genome modification necessary for desired pathway fine-tuning to upgrade products from biomass deconstruction into value-added products. Physics-based modeling approaches such as density functional theory calculations and molecular dynamics simulations have been most impactful in studies aimed at exploring the molecular level details of solvent-biomass interactions, reaction mechanisms occurring in biomass-solvent systems, and the catalytic mechanisms and engineering of enzymes involved in biomass degradation. More recently, with ever increasing amounts of data from, for example, advanced mutli-omics experiments, machine learning approaches have begun to make important contributions in synthetic biology and optimization of metabolic pathways for production of biofuels and chemicals.

09 BIOMASS FUELS↗
DOE OSTI2018

Optimal Matched Filter in the Low-number Count Poisson Noise Regime and Implications for X-Ray Source Detection

Detection of templates (e.g., sources) embedded in low-number count Poisson noise is a common problem in astrophysics. Examples include source detection in X-ray images, γ-rays, UV, neutrinos, and search for clusters of galaxies and stellar streams. However, the solutions in the X-ray-related literature are sub-optimal in some cases by considerable factors. Using the lemma of Neyman–Pearson, we derive the optimal statistics for template detection in the presence of Poisson noise. We demonstrate that, for known template shape (e.g., point sources), this method provides higher completeness, for a fixed false-alarm probability value, compared with filtering the image with the point-spread function (PSF). In turn, we find that filtering by the PSF is better than filtering the image using the Mexican-hat wavelet (used by wavdetect). For some background levels, our method improves the sensitivity of source detection by more than a factor of two over the popular Mexican-hat wavelet filtering. This filtering technique can also be used for fast PSF photometry and flare detection; it is efficient and straightforward to implement. We provide an implementation in MATLAB. The development of a complete code that works on real data, including the complexities of background subtraction and PSF variations, is deferred for future publication.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2019

Time and Phase Resolved Optical Spectra of Potentially Hazardous Asteroid 2014 JO25

The asteroid 2014 JO25, considered to be potentially hazardous by the Minor Planet Center, was spectroscopically followed during its close-Earth encounter on 2017 April 19 and 20. The spectra of the asteroid were taken with the low-resolution spectrograph (LISA), mounted on the 1.2 m telescope at the Mount Abu Infrared Observatory, India. Coming from a region close to the Hungaria population of asteroids, this asteroid follows a comet-like orbit with a relatively high inclination and large eccentricity. Hence, we carried out optical spectroscopic observations of the asteroid to look for comet-like molecular emissions or outbursts. However, the asteroid showed a featureless spectrum, devoid of any comet-like features. The light curve of the asteroid was analyzed using V-band magnitudes derived from the spectra and the most likely solution for the rotation of the asteroid was obtained. The absolute magnitude H and the slope parameter G were determined for the asteroid in the V filter band using the IAU accepted standard two-parameter H–G model. A peculiar, rarely found result from these observations is its phase bluing trend. The relative B–V color index seems to decrease with increasing phase angle, which indicates a phase bluing trend. Such trends have seldom been reported in the literature. However, phase reddening in asteroids is very common. The asymmetry parameter g and the single-scattering albedo w were estimated for the asteroid by fitting the Hapke phase function to the observed data. The asteroid shows a relatively large value for the single-scattering albedo and a highly back-scattering surface.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

The High-energy Spectrum of the Nearby Planet-hosting Inactive Mid-M Dwarf LHS 3844

To fully characterize the atmospheres, or lack thereof, of terrestrial exoplanets, we must include the high-energy environments provided by their host stars. The nearby mid-M dwarf LHS 3844 hosts a terrestrial world that lacks a substantial atmosphere. We present a time-series UV spectrum of LHS 3844 from 1131 to 3215 Å captured by HST/COS. We detect one flare in the FUV that has an absolute energy of 8.96 ± 0.77 × 10{sup 28} erg and an equivalent duration of 355 ± 31 s. We extract the flare and quiescent UV spectra separately. For each spectrum, we estimate the Lyα flux using correlations between UV line strengths. We use Swift-XRT to place an upper limit on the soft X-ray flux and construct a differential emission model to estimate flux that is obscured by the interstellar medium. We compare the differential emission model flux estimates in the XUV to other methods that rely on scaling from the Lyα, Si iv, and N v lines in the UV. The XUV, FUV, and NUV flux of LHS 3844 relative to its bolometric luminosity is log{sub 10}(L {sub band}/L {sub Bol}) =−3.65, −4.16, and −4.48, respectively, for the quiescent state. These values agree with trends in high-energy flux as a function of stellar effective temperature found by the MUSCLES survey for a sample of early-M dwarfs. Many of the most spectroscopically accessible terrestrial exoplanets orbit inactive mid-to-late M dwarfs like LHS 3844. Measurements of M dwarf high-energy spectra are preferable for exoplanet characterization but are not always possible. The spectrum of LHS 3844 is a useful proxy for the current radiation environment for these worlds.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2017

Non-LTE Stellar Population Synthesis of Globular Clusters Using Synthetic Integrated Light Spectra. I. Constructing the IL Spectra

We present an investigation of the globular cluster population synthesis method of McWilliam and Bernstein, focusing on the impact of non-LTE (NLTE) modeling effects and color–magnitude diagram (CMD) discretization. Johnson–Cousins–Bessel U – B, B−V, V−I, and J−K colors are produced for 96 synthetic integrated light (IL) spectra with two different discretization prescriptions and three degrees of NLTE treatment. These color values are used to compare NLTE- and LTE-derived population ages. Relative contributions of different spectral types to the IL spectra for different wavebands are measured. IL NLTE spectra are shown to be more luminous in the UV and optical than LTE spectra, but show stronger absorption features in the IR. The main features showing discrepancies between NLTE and LTE IL spectra may be attributed to light metals, primarily Fe i, Ca i, and Ti i, as well as TiO molecular bands. Main-sequence stars are shown to have negligible NLTE effects at IR wavelengths compared to more evolved stars. Photometric color values are shown to vary at the millimagnitude level as a function of CMD discretization. Finer CMD sampling for the upper main sequence and turnoff, base of the red giant branch, and the horizontal branch minimizes this variation. Differences in ages derived from LTE and NLTE IL spectra are found to range from 0.55 to 2.54 Gyr, comparable to the uncertainty in GC ages derived from color indices with observational uncertainties of 0.01 mag, the limiting precision of the Harris catalog.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

Impedance-Based State-of-Health Estimation for Lithium-Ion Battery Management Systems

This paper discusses an impedance-based state-of-health (SOH) estimation for lithium-ion battery management systems. Features obtained from the Nyquist plot of the electrochemical impedance spectroscopy (EIS) are utilized to estimate the SOH. Two commercial lithium-ion batteries were aged (cycled) in the laboratory to collect aging and impedance data. An algorithm for SOH estimation is presented and evaluated. The performance evaluation results show that the SOH features extracted from the Nyquist plot can be utilized for SOH estimation. Estimated SOH values can be then utilized in various battery management systems (BMS) functions such as for calibrating the available capacity, adjusting charging/discharging strategies, and protection.

Al-Smadi, Mohammad↗
DOE OSTI2017

Localizing Holes as Polarons and Predicting Band Gaps, Defect Levels, and Delithiation Energies of Solid-State Materials with a Local Exchange-Correlation Functional

Here, we assess the performance of seven exchange–correlation functionals (some with and some without a Hubbard U correction) for their ability (i) to predict band gaps of silicon, diamond, and Li-ion battery cathode materials, (ii) to localize hole polarons and predict delithiation energies in Li-ion battery cathode materials, and (iii) to predict transition levels of charge carriers of doped silicon and diamond. Both local and hybrid exchange–correlation functionals were tested. The local functionals tend to underestimate band gaps and delocalize polarons. The hybrid functionals very often give a good description of both properties, but they may not be practical for calculations involving large unit cells, large ensembles, or dynamics, and therefore a local functional with a Hubbard U correction is often used (giving the method called DFT+U), where the value of a parameter U is adjusted according to the system and the property being investigated. Keeping in mind the importance of computational cost and the undesirability of having to adjust an empirical parameter for each system or property of interest, we recently developed a local functional, namely HLE17, to try to accurately predict band gaps and excitation energies, and we validated it using mostly main-group solids and molecules. Here we test the performance of HLE17 for its ability to predict band gaps and localize polarons in other solid-state materials, and we compare its performance to that of popular local functionals (PBE, PBEsol, and TPSS), a range-separated hybrid functional with screened exchange (HSE06), and DFT+U. We find that HLE17 predicts more accurate band gaps than other local functionals and can localize holes as polarons, which other local functionals usually fail to do, and for a number of cases it is comparable in performance quality to Hubbard-corrected functionals without the need for system-specific parametrization and to hybrid functionals without the high cost. Because HLE17 does not predict accurate lattice constants, we use the single-point method of quantum chemistry, where the geometry is optimized with one functional and the band gap is calculated with HLE17, or we perform calculations with the lattice constants obtained by TPSS and both the fractional intracell coordinates and the electronic structure obtained by HLE17 (a new method denoted HLE17\\TPSS). In particular, we performed calculations by HLE17//TPSS, HLE17//HSE06, HLE17//DFT+U, and HLE17\\TPSS, and these methods usually agree well with each other and give values similar to experiment.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2017

Emergent Electronic and Dielectric Properties of Interacting Nanoparticles at Finite Temperature

Lead chalcogenide nanoparticle solids have been successfully integrated into certified solar cells and represent promising platforms for the design of novel photoabsorbers for photoelectrochemical cells. While much attention has been drawn to improving efficiency and device performance through altering the character of the individual nanoparticles, the role of interactions between nanoparticles is not yet well-understood. Using first-principles molecular dynamics and electronic structure calculations, we investigated the combined effect of temperature and interaction on functionalized lead chalcogenide nanoparticles (NPs). Here, we show that at finite temperature, interacting NPs are dynamical dipolar systems, with the average values of dipole moments and polarizabilities substantially increased with respect to those of the isolated building blocks. In addition, we show that the interacting NPs exhibit slightly smaller fundamental gaps that decrease as a function of temperature and that the radiative lifetimes of both the isolated NPs and the solids are greatly reduced at finite temperature compared to T = 0. Lastly, we present a critical discussion of various results reported in the literature for the values of dipole moments of nanoparticles.

36 MATERIALS SCIENCE↗
DOE OSTI2025

On the minimum number of radiation field parameters to specify gas cooling and heating functions

Fast and accurate approximations of gas cooling and heating functions are needed for hydrodynamic galaxy simulations. We use machine learning to analyze atomic gas cooling and heating functions in the presence of a generalized incident local radiation field computed by Cloudy. We characterize the radiation field through binned radiation field intensities instead of the photoionization rates used in our previous work. We find a set of 6 energy bins whose intensities exhibit relatively low correlation. We use these bins as features to train machine learning models to predict Cloudy cooling and heating functions at fixed metallicity. We compare the relative SHapley Additive exPlanation (SHAP) value importance of the features. From the SHAP analysis, we identify a feature subset of 3 energy bins (0.5-1, 1-4, and 13-16Ry) with the largest importance and train additional models on this subset. We compare the mean squared errors and distribution of errors on both the entire training data table and a randomly selected 20% test set withheld from model training. The machine learning models trained with 3 and 6 bins, as well as 3 and 4 photoionization rates, have comparable accuracy everywhere, with errors ≳10 times smaller than for the interpolation table of Gnedin and Hollon (2012). We conclude that 3 energy bins (or 3 analogous photoionization rates: molecular hydrogen photodissociation, neutral hydrogen HI, and fully ionized carbon CVI) are sufficient to characterize the dependence of the gas cooling and heating functions on our assumed incident radiation field model.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2018

Correction Scheme for Comparison of Computed and Experimental Optical Transition Energies in Functionalized Single-Walled Carbon Nanotubes

We present that covalent functionalization of single-walled carbon nanotubes (SWCNTs) introduces red-shifted emission features in the near-infrared spectral range due to exciton localization around the defect site. Such chemical modifications increase their potential use as near-infrared emitters and single-photon sources in telecommunications applications. Density functional theory (DFT) studies using finite-length tube models have been used to calculate their optical transition energies. Predicted energies are typically blue-shifted compared to experiment due to methodology errors including imprecise self-interaction corrections in the density functional and finite-size basis sets. Furthermore, artificial quantum confinement in finite models cannot be corrected by a constant-energy shift since they depend on the degree of exciton localization. Herein, we present a method that corrects the emission energies predicted by time-dependent DFT. Confinement and methodology errors are separately estimated using experimental data for unmodified tubes. Finally, corrected emission energies are in remarkable agreement with experiment, suggesting the value of this straightforward method toward predicting and interpreting the optical features of functionalized SWCNTs.

36 MATERIALS SCIENCE↗
DOE OSTI2025

On the minimum number of radiation field parameters to specify gas cooling and heating functions

Fast and accurate approximations of gas cooling and heating functions are needed for hydrodynamic galaxy simulations. We use machine learning to analyze atomic gas cooling and heating functions computed by Cloudy in the presence of a generalized incident local radiation field. We characterize the radiation field through binned radiation field intensities instead of the photoionization rates used in our previous work. We find a set of 6 energy bins whose intensities exhibit relatively low correlation. We use these bins as features to train machine learning models to predict Cloudy cooling and heating functions at fixed metallicity. We compare the relative SHapley Additive exPlanation (SHAP) value importance of the features. From the SHAP analysis, we identify a feature subset of 3 energy bins ($0.5-1, 1-4$, and $13-16 \, \mathrm{Ry}$) with the largest importance and train additional models on this subset. We compare the mean squared errors and distribution of errors on both the entire training data table and a randomly selected 20% test set withheld from model training. The machine learning models trained with 3 and 6 bins, as well as 3 and 4 photoionization rates, have comparable accuracy everywhere, with errors $\gtrsim 10$ times smaller than for the interpolation table of Gnedin and Hollon (2012). We conclude that 3 energy bins (or 3 analogous photoionization rates: molecular hydrogen photodissociation, neutral hydrogen HI, and fully ionized carbon CVI) are sufficient to characterize the dependence of the gas cooling and heating functions on our assumed incident radiation field model.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

Predictor-corrector models for lightweight massive machine-type communications in Industry 4.0

Future Industry 4.0 scenarios are characterized by seamless integration between computational and physical processes. To achieve this objective, dense platforms made of small sensing nodes and other resource constraint devices are ubiquitously deployed. All these devices have a limited number of computational resources, just enough to perform the simple operation they are in charge of. The remaining operations are delegated to powerful gateways that manage sensing nodes, but resources are never unlimited, and as more and more devices are deployed on Industry 4.0 platforms, gateways present more problems to handle massive machine-type communications. Although the problems are diverse, those related to security are especially critical. To enable sensing nodes to establish secure communications, several semiconductor companies are currently promoting a new generation of devices based on Physical Unclonable Functions, whose usage grows every year in many real industrial scenarios. Those hardware devices do not consume any computational resource but force the gateway to keep large key-value catalogues for each individual node. In this context, memory usage is not scalable and processing delays increase exponentially with each new node on the platform. In this paper, we address this challenge through predictor-corrector models, representing the key-value catalogues. Models are mathematically complex, but we argue that they consume less computational resources than current approaches. The lightweight models are based on complex functions managed as Laurent series, cubic spline interpolations, and Boolean functions also developed as series. Unknown parameters in these models are predicted, and eventually corrected to calculate the output value for each given key. The initial parameters are based on the Kane Yee formula. An experimental analysis and a performance evaluation are provided in the experimental section, showing that the proposed approach causes a significant reduction in the resource consumption.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Exploring the dependence of gas cooling and heating functions on the incident radiation field with machine learning

ABSTRACT Gas cooling and heating functions play a crucial role in galaxy formation. But, it is computationally expensive to exactly compute these functions in the presence of an incident radiation field. These computations can be greatly sped up by using interpolation tables of pre-computed values, at the expense of making significant and sometimes even unjustified approximations. Here, we explore the capacity of machine learning to approximate cooling and heating functions with a generalized radiation field. Specifically, we use the machine learning algorithm XGBoost to predict cooling and heating functions calculated with the photoionization code cloudy at fixed metallicity, using different combinations of photoionization rates as features. We perform a constrained quadratic fit in metallicity to enable a fair comparison with traditional interpolation methods at arbitrary metallicity. We consider the relative importance of various photoionization rates through both a principal component analysis (PCA) and calculation of SHapley Additive exPlanation (shap) values for our XGBoost models. We use feature importance information to select different subsets of rates to use in model training. Our XGBoost models outperform a traditional interpolation approach at each fixed metallicity, regardless of feature selection. At arbitrary metallicity, we are able to reduce the frequency of the largest cooling and heating function errors compared to an interpolation table. We find that the primary bottleneck to increasing accuracy lies in accurately capturing the metallicity dependence. This study demonstrates the potential of machine learning methods such as XGBoost to capture the non-linear behaviour of cooling and heating functions.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

Testing Surrogate-Based Optimization with the Fortified Branin-Hoo Extended to Four Dimensions

Some popular functions used to test global optimization algorithms have multiple local optima, all with the same value, making them all global optima. It is easy to make them more challenging by fortifying them via adding a localized bump at the location of one of the optima. In previous work the authors illustrated this for the Branin-Hoo function and the popular differential evolution algorithm, showing that the fortified Branin-Hoo required an order of magnitude more function evaluations. This paper examines the effect of fortifying the Branin-Hoo function on surrogate- based optimization, which usually proceeds by adaptive sampling. Two algorithms are considered. The EGO algorithm, which is based on a Gaussian process (GP) and an algorithm based on radial basis functions (RBF). EGO is found to be more frugal in terms of the number of required function evaluations required to identify the correct basin, but it is expensive to run on a desktop, limiting the number of times the runs could be repeated to establish sound statistics on the number of required function evaluations. The RBF algorithm was cheaper to run, providing more sound statistics on performance. A four-dimensional version of the Branin-Hoo function was introduced in order to assess the effect of dimensionality. Furthermore, it was found that the difference between the ordinary function and the fortified one was much more pronounced for the four-dimensional function compared to the two dimensional one.

97 MATHEMATICS AND COMPUTING↗