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DOE OSTI2014

Supersymmetric B – L inflation near the conformal coupling

We investigate a novel scenario of cosmological inflation in a gauged B-L extended minimal supersymmetric Standard Model with R-symmetry. We use a noncanonical Kähler potential and a superpotential, both preserving the R-symmetry to construct a model of slow-roll inflation. The model is controlled by two real parameters: the nonminimal coupling ξ that originates from the Kähler potential, and the breaking scale v of the U (1)B-L symmetry. We compute the spectrum of the cosmological microwave background radiation and show that the prediction of the model fits well the recent Planck satellite observation for a wide range of the parameter space. We also find that the typical reheating temperature of the model is low enough to avoid the gravitino problem but nevertheless allows sufficient production of the baryon asymmetry if we take into account the effect of resonance enhancement. The model is free from cosmic strings that impose stringent constraints on generic U (1)B-L based scenarios, as in our scenario the U (1)B-L symmetry is broken from the onset.

Arai, Masato↗

DOE OSTI2018

Empirical modeling of dopability in diamond-like semiconductors

Carrier concentration optimization has been an enduring challenge when developing newly discovered semiconductors for applications (e.g., thermoelectrics, transparent conductors, photovoltaics). This barrier has been particularly pernicious in the realm of high-throughput property prediction, where the carrier concentration is often assumed to be a free parameter and the limits are not predicted due to the high computational cost. In this work, we explore the application of machine learning for high-throughput carrier concentration range prediction. Bounding the model within diamond-like semiconductors, the learning set was developed from experimental carrier concentration data on 127 compounds ranging from unary to quaternary. The data were analyzed using various statistical and machine learning methods. Accurate predictions of carrier concentration ranges in diamond-like semiconductors are made within approximately one order of magnitude on average across both p- and n-type dopability. The model fit to empirical data is analyzed to understand what drives trends in carrier concentration and compared with previous computational efforts. Lastly, dopability predictions from this model are combined with high-throughput quality factor predictions to identify promising thermoelectric materials.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2017

Alfvén eigenmode stability and critical gradient energetic particle transport using the Trapped-Gyro-Landau-Fluid model

The Trapped-Gyro-Landau-Fluid (TGLF) transport model is a physically realistic and comprehensive theory based on a local quasilinear transport model fitted to linear and nonlinear GYRO gyrokinetic simulations [Staebler et al., Phys. Plasmas 14, 55909 (2007)]. This work presents the first use of the TGLF model to treat low-n Alfvén eigenmode (AE) stability and energetic particle (EP) transport. TGLF accurately recovers the local GYRO toroidicity-induced AE (TAE) and energetic particle mode (EPM) linear growth and frequency rates for a fusion alpha case. With a very high grid resolution, TGLF can quickly find the critical EP pressure gradient profile for stiff EP transport based on an AE linear threshold given the background thermal plasma profiles in DIII-D. The TGLF critical gradient profile using the recipe γ AE =0, that is the linear AE growth rate without additional driving rates from the background plasma gradients, matches the more expensive linear GYRO results with a single worst toroidal mode number n. TGLF can easily find the minimum critical gradient profile with testing multiple ns. From a database of runs using a newly developed TGLFEP code, a rough but insightful parametric “power law” scaling for critical EP beta is demonstrated. An important toroidal stabilization condition on the EP pressure gradient p EP /L$EP\atop{p}$ drive is isolated: R/L$EP\atop{p}$ > C R ~ 3, where L$EP\atop{p}$ is the EP pressure gradient length and R is the tokamak major radius. This paper also demonstrates that relaxation of the fixed slowing down EP profile shape approximation often used to find the critical EP density profile has little effect on the resulting EP transport. The single EP species critical gradient model is generalized to handle two EP species.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2023

What drives the variance of galaxy spectra?

We present a study aimed at understanding the physical phenomena underlying the formation and evolution of galaxies following a data-driven analysis of spectroscopic data based on the variance in a carefully selected sample. We apply principal component analysis (PCA) independently to three subsets of continuum-subtracted optical spectra, segregated into their nebular emission activity as quiescent, star-forming, and active galactic nuclei (AGNs). We emphasize that the variance of the input data in this work only relates to the absorption lines in the photospheres of the stellar populations. The sample is taken from the Sloan Digital Sky Survey (SDSS) in the stellar velocity dispersion range 100–150 km s −1 , to minimize the ‘blurring’ effect of the stellar motion. We restrict the analysis to the first three principal components (PCs) and find that PCA segregates the three types with the highest variance mapping SSP-equivalent age, along with an inextricable degeneracy with metallicity, even when all three PCs are included. Spectral fitting shows that stellar age dominates PC1, whereas PC2 and PC3 have a mixed dependence of age and metallicity. The trends support – independently of any model fitting – the hypothesis of an evolutionary sequence from star formation to AGN to quiescence. As a further test of the consistency of the analysis, we apply the same methodology in different spectral windows, finding similar trends, but the variance is maximal in the blue wavelength range, roughly around the 4000 Å break.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2025

X-Ray Polarization of the Magnetar 1E 1841−045

We report on IXPE and NuSTAR observations beginning 40 days after the 2024 outburst onset of magnetar 1E 1841−045, marking the first IXPE observation of a magnetar in an enhanced state. Our spectropolarimetric analysis indicates that both a blackbody (BB) plus double power-law (PL) and a double blackbody plus power-law spectral model fit the phase-averaged intensity data well, with a hard PL tail (Γ = 1.19 and 1.35, respectively) dominating above ≈5 keV. For the former model, we find the soft PL (the dominant component at soft energies) exhibits a polarization degree (PD) of ≈30% while the hard PL displays a PD of ≈40%. Similarly, the cool BB of the 2BB+PL model possesses a PD of ≈15% and a hard PL PD of ≈57%. For both models, each component has a polarization angle (PA) compatible with celestial north. Model-independent polarization analysis supports these results, wherein the PD increases from ≈15% to ≈70% in the 2–3 keV and 6–8 keV ranges, respectively, while the PA remains nearly constant. We find marginal evidence for phase-dependent variability of the polarization properties, namely a higher PD at phases coinciding with the hard X-ray pulse peak. We compare the hard X-ray PL to the expectation from resonant inverse Compton scattering (RICS) and secondary pair cascade synchrotron radiation from primary high-energy RICS photons; both present reasonable spectropolarimetric agreement with the data, albeit the latter does so more naturally. We suggest that the soft PL X-ray component may originate from a Comptonized corona in the inner magnetosphere.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2025

Solvation Structure of 237 Np 4+ in a Noncomplexing Environment

Here, the solvation structure of an Np 4+ ion in an aqueous, noncomplexing and nonoxidizing environment of trifluoromethanesulfonic (triflic) acid was investigated with X-ray absorption spectroscopy (XAS) combined with ab initio molecular dynamics (AIMD) and time-dependent density functional theory (TDDFT) calculations. Np L III -edge X-ray absorption near-edge structure (XANES) and extended X-ray absorption fine structure (EXAFS) data were collected for Np 4+ in 1, 3, and 7 M triflic acid using a laboratory-scale spectrometer and separately at a synchrotron facility, producing data sets in excellent agreement. TDDFT calculations revealed a weak pre-edge feature not previously reported for Np L III -edge XANES. AIMD modeling results showed differences in the hydration shell of the Np 4+ ion at different concentrations of triflic acid; these results are supported by the experiment. EXAFS fit models to the experiment resulted in similar coordination of Np 4+ in noncomplexing aqueous media as reported in the literature for 1 M perchloric acid but, together with calculations, revealed more than one distance between Np and O atoms in 7 M triflic acid. These results imply monodentate coordination with sulfonate groups in 7 M triflic acid and suggest the possibility of proto-neptunyl species in relatively low-concentration Np 4+ acid solutions.

Boglaienko, Daria V. [Pacific Northwest National L↗

DOE OSTI2015

Kinetic modelling for zinc (II) ions biosorption onto Luffa cylindrica

The biosorption of Zinc (II) ions onto a biomaterial - Luffa cylindrica has been studied. This biomaterial was characterized by elemental analysis, surface area, pore size distribution, scanning electron microscopy, and the biomaterial before and after sorption, was characterized by Fourier Transform Infra Red (FTIR) spectrometer. The kinetic nonlinear models fitted were Pseudo-first order, Pseudo-second order and Intra-particle diffusion. A comparison of non-linear regression method in selecting the kinetic model was made. Four error functions, namely coefficient of determination (R{sup 2}), hybrid fractional error function (HYBRID), average relative error (ARE), and sum of the errors squared (ERRSQ), were used to predict the parameters of the kinetic models. The strength of this study is that a biomaterial with wide distribution particularly in the tropical world and which occurs as waste material could be put into effective utilization as a biosorbent to address a crucial environmental problem.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2023

Bayesian Spatial Models for Projecting Corn Yields

Climate change is predicted to impact corn yields. Previous studies analyzing these impacts differ in data and modeling approaches and, consequently, corn yield projections. We analyze the impacts of climate change on corn yields using two statistical models with different approaches for dealing with county-level effects. The first model, which is novel to modeling corn yields, uses a computationally efficient spatial basis function approach. We use a Bayesian framework to incorporate both parametric and climate model structural uncertainty. We find that the statistical models have similar predictive abilities, but the spatial basis function model is faster and hence potentially a useful tool for crop yield projections. We also explore how different gridded temperature datasets affect the statistical model fit and performance. Compared to the dataset with only weather station data, we find that the dataset composed of satellite and weather station data results in a model with a magnified relationship between temperature and corn yields. For all statistical models, we observe a relationship between temperature and corn yields that is broadly similar to previous studies. We use downscaled and bias-corrected CMIP5 climate model projections to obtain detrended corn yield projections for 2020–2049 and 2069–2098. In both periods, we project a decrease in the mean corn yield production, reinforcing the findings of other studies. However, the magnitude of the decrease and the associated uncertainties we obtain differ from previous studies.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2017

Comparison of Two Coronal Magnetic Field Models to Reconstruct a Sigmoidal Solar Active Region with Coronal Loops

Magnetic field extrapolation is an important tool to study the three-dimensional (3D) solar coronal magnetic field, which is difficult to directly measure. Various analytic models and numerical codes exist, but their results often drastically differ. Thus, a critical comparison of the modeled magnetic field lines with the observed coronal loops is strongly required to establish the credibility of the model. Here we compare two different non-potential extrapolation codes, a nonlinear force-free field code (CESE–MHD–NLFFF) and a non-force-free field (NFFF) code, in modeling a solar active region (AR) that has a sigmoidal configuration just before a major flare erupted from the region. A 2D coronal-loop tracing and fitting method is employed to study the 3D misalignment angles between the extrapolated magnetic field lines and the EUV loops as imaged by SDO /AIA. It is found that the CESE–MHD–NLFFF code with preprocessed magnetogram performs the best, outputting a field that matches the coronal loops in the AR core imaged in AIA 94 Å with a misalignment angle of ∼10°. This suggests that the CESE–MHD–NLFFF code, even without using the information of the coronal loops in constraining the magnetic field, performs as good as some coronal-loop forward-fitting models. For the loops as imaged by AIA 171 Å in the outskirts of the AR, all the codes including the potential field give comparable results of the mean misalignment angle (∼30°). Thus, further improvement of the codes is needed for a better reconstruction of the long loops enveloping the core region.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2022

Modeling land use and land cover change: using a hindcast to estimate economic parameters in gcamland v2.0

Abstract. Future changes in land use and cover have important implications for agriculture, energy, water use, and climate. Estimates of future land use and land cover differ significantly across economic models as a result of differences in drivers, model structure, and model parameters; however, these models often rely on heuristics to determine model parameters. In this study, we demonstrate a more systematic and empirically based approach to estimating a few key parameters for an economic model of land use and land cover change, gcamland. Specifically, we generate a large set of model parameter perturbations for the selected parameters and run gcamland simulations with these parameter sets over the historical period in the United States to quantify land use and land cover, determine how well the model reproduces observations, and identify parameter combinations that best replicate observations, assuming other model parameters are fixed. We also test alternate methods for forming expectations about uncertain crop yields and prices, including adaptive, perfect, linear, and hybrid approaches. In particular, we estimate parameters for six parameters used in the formation of expectations and three of seven logit exponents for the USA only. We find that an adaptive expectation approach minimizes the error between simulated outputs and observations, with parameters that suggest that for most crops, landowners put a significant weight on previous information. Interestingly, for corn, where ethanol policies have led to a rapid growth in demand, the resulting parameters show that a larger weight is placed on more recent information. We examine the change in model parameters as the metric of model error changes, finding that the measure of model fitness affects the choice of parameter sets. Finally, we discuss how the methodology and results used in this study could be used for other regions or economic models to improve projections of future land use and land cover change.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2015

A COMPARATIVE ANALYSIS OF THE SUPERNOVA LEGACY SURVEY SAMPLE WITH ΛCDM AND THE R{sub h}=ct UNIVERSE

The use of Type Ia supernovae (SNe Ia) has thus far produced the most reliable measurement of the expansion history of the universe, suggesting that ΛCDM offers the best explanation for the redshift–luminosity distribution observed in these events. However, analysis of other kinds of sources, such as cosmic chronometers, gamma-ray bursts, and high-z quasars, conflicts with this conclusion, indicating instead that the constant expansion rate implied by the R{sub h} = ct universe is a better fit to the data. The central difficulty with the use of SNe Ia as standard candles is that one must optimize three or four nuisance parameters characterizing supernova (SN) luminosities simultaneously with the parameters of an expansion model. Hence, in comparing competing models, one must reduce the data independently for each. We carry out such a comparison of ΛCDM and the R{sub h} = ct universe using the SN Legacy Survey sample of 252 SN events, and show that each model fits its individually reduced data very well. However, since R{sub h} = ct has only one free parameter (the Hubble constant), it follows from a standard model selection technique that it is to be preferred over ΛCDM, the minimalist version of which has three (the Hubble constant, the scaled matter density, and either the spatial curvature constant or the dark energy equation-of-state parameter). We estimate using the Bayes Information Criterion that in a pairwise comparison, the likelihood of R{sub h} = ct is ∼90%, compared with only ∼10% for a minimalist form of ΛCDM, in which dark energy is simply a cosmological constant. Compared to R{sub h} = ct, versions of the standard model with more elaborate parametrizations of dark energy are judged to be even less likely.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2017

A Modified Johnson–Cook Model for Flow Behavior of Alloy 800H at Intermediate Strain Rates and High Temperatures

A modified Johnson–Cook model for the flow behavior of alloy 800H at intermediate strain rates and high temperatures is presented. The modification is based on a study of the relation between strain hardening and both strain rate and softening parameters. The predicted stresses obtained using the modified model are compared to those obtained using the original Johnson–Cook model. The parameters constitute the two models are determined using the inverse method, Kalman filter. The results show that the modified model fits the experimental data very well for different combinations of strain rates and temperatures, with a mean value of R-squared regression of 0.90 for the modified model and 0.74 for the original Johnson–Cook model.

36 MATERIALS SCIENCE↗

DOE OSTI2014

Quantifying (dis)agreement between direct detection experiments in a halo-independent way

We propose an improved method to study recent and near-future dark matter direct detection experiments with small numbers of observed events. Our method determines in a quantitative and halo-independent way whether the experiments point towards a consistent dark matter signal and identifies the best-fit dark matter parameters. To achieve true halo independence, we apply a recently developed method based on finding the velocity distribution that best describes a given set of data. For a quantitative global analysis we construct a likelihood function suitable for small numbers of events, which allows us to determine the best-fit particle physics properties of dark matter considering all experiments simultaneously. Based on this likelihood function we propose a new test statistic that quantifies how well the proposed model fits the data and how large the tension between different direct detection experiments is. We perform Monte Carlo simulations in order to determine the probability distribution function of this test statistic and to calculate the p-value for both the dark matter hypothesis and the background-only hypothesis.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2025

Statistical estimates of the binary properties of rotational variables

ABSTRACT We present a model to estimate the average primary masses, companion mass ranges, the inclination limit for recognizing a rotational variable, and the primary mass spreads for populations of binary stars. The model fits a population’s binary mass function distribution and allows for a probability that some mass functions are incorrectly estimated. Using tests with synthetic data, we assess the model’s sensitivity to each parameter, finding that we are most sensitive to the average primary mass and the minimum companion mass, with less sensitivity to the inclination limit and little to no sensitivity to the primary mass spread. We apply the model to five populations of binary spotted rotational variables identified in ASAS-SN, computing their binary mass functions using RV data from APOGEE. Their average primary mass estimates are consistent with our expectations based on their CMD locations ($\sim 0.75 \, {\rm M}_{\odot }$ for lower main sequence primaries and $\sim 0.9$–$1.2 \, {\rm M}_{\odot }$ for RS CVn and sub-subgiants). Their companion mass range estimates allow companion masses down to $M_2/M_1\simeq 0.1$, although the main sequence population may have a higher minimum mass fraction ($\sim 0.4$). We see weak evidence of an inclination limit $\gtrsim 50^{\circ }$ for the main sequence and sub-subgiant groups and no evidence of an inclination limit in the other groups. No groups show strong evidence for a preferred primary mass spread. We conclude by demonstrating that the approach will provide significantly better estimates of the primary mass and the minimum mass ratio and reasonable sensitivity to the inclination limit with 10 times as many systems.

Phillips, Anya (ORCID:000900051914974X)↗

DOE OSTI2025

Dynamics-based halo model for large scale structure

Accurate modeling of the one-to-two halo transition has long been difficult to achieve. Here, we demonstrate that physically motivated halo definitions that respect the bimodal phase-space distribution of dark matter particles near halos resolves this difficulty. Specifically, the two phase-space components are overlapping and correspond to (1) particles orbiting the halo and (2) particles infalling into the halo for the first time. Motivated by this decomposition, García et al. [Mon. Not. R. Astron. Soc. 521, 2464 (2023)] advocated for defining halos as the collection of particles orbiting their self-generated potential. This definition identifies the traditional one-halo term of the halo-mass correlation function with the distribution of orbiting particles around a halo, while the two-halo term governs the distribution of infalling particles. We use dark matter simulations to demonstrate that the distribution of orbiting particles is finite and can be characterized by a single physical scale 𝑟 h , which we refer to as the halo radius. The two-halo term is described using a simple yet accurate empirical model based on the Zel’dovich correlation function. We further demonstrate that the halo radius imprints itself on the distribution of infalling particles at small scales. Our final model for the halo-mass correlation function is accurate at the ≈ 2% level for 𝑟∈ [0.1, 50] ℎ −1 Mpc. The Fourier transform of our best-fit model describes the halo-mass power spectrum with comparable accuracy for 𝑘 ∈ [0.06, 6.0] ℎ Mpc −1 .

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2016

Sci-Fri AM: Quality, Safety, and Professional Issues 04: Predicting waiting times in Radiation Oncology using machine learning

We describe a method for predicting waiting times in radiation oncology. Machine learning is a powerful predictive modelling tool that benefits from large, potentially complex, datasets. The essence of machine learning is to predict future outcomes by learning from previous experience. The patient waiting experience remains one of the most vexing challenges facing healthcare. Waiting time uncertainty can cause patients, who are already sick and in pain, to worry about when they will receive the care they need. In radiation oncology, patients typically experience three types of waiting: Waiting at home for their treatment plan to be prepared Waiting in the waiting room for daily radiotherapy Waiting in the waiting room to see a physician in consultation or follow-up These waiting periods are difficult for staff to predict and only rough estimates are typically provided, based on personal experience. In the present era of electronic health records, waiting times need not be so uncertain. At our centre, we have incorporated the electronic treatment records of all previously-treated patients into our machine learning model. We found that the Random Forest Regression model provides the best predictions for daily radiotherapy treatment waiting times (type 2). Using this model, we achieved a median residual (actual minus predicted value) of 0.25 minutes and a standard deviation residual of 6.5 minutes. The main features that generated the best fit model (from most to least significant) are: Allocated time, median past duration, fraction number and the number of treatment fields.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2017

Constraining the H i–Halo Mass Relation from Galaxy Clustering

We study the dependence of galaxy clustering on H i mass using ∼16,000 galaxies with redshift in the range of 0.0025 10{sup 8} M{sub ⊙}, drawn from the 70% complete sample of the Arecibo Legacy Fast ALFA survey. We construct subsamples of galaxies with M{sub HI} above different thresholds and make volume-limited clustering measurements in terms of three statistics: the projected two-point correlation function, the projected cross-correlation function with respect to a reference sample, and the redshift-space monopole moment. In contrast to previous studies, which found no/weak H i mass dependence, we find both the clustering amplitudes on scales above a few megaparsecs and the bias factors to increase significantly with increasing H i mass for M{sub HI}>10{sup 9} M{sub ⊙}. For H i mass thresholds below ∼10{sup 9} M{sub ⊙}, the inferred galaxy bias factors are systematically lower than the minimum halo bias from mass-selected halo samples. We extend the simple halo model, in which the galaxy content is only determined by halo mass, by including the halo formation time as an additional parameter. A model that puts H i-rich galaxies into halos that formed late can reproduce the clustering measurements reasonably well. We present the implications of our best-fitting model on the correlation of H i mass with halo mass and formation time, as well as the halo occupation distributions and H i mass functions for central and satellite galaxies. These results are compared with the predictions from semianalytic galaxy formation models and hydrodynamic galaxy formation simulations.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2017

Early Blue Excess from the Type Ia Supernova 2017cbv and Implications for Its Progenitor

We present very early, high-cadence photometric observations of the nearby Type Ia SN 2017cbv. The light curve is unique in that it has a blue bump during the first five days of observations in the U , B , and g bands, which is clearly resolved given our photometric cadence of 5.7 hr during that time span. We model the light curve as the combination of early shocking of the supernova ejecta against a nondegenerate companion star plus a standard SN Ia component. Our best-fit model suggests the presence of a subgiant star 56 R {sub ☉} from the exploding white dwarf, although this number is highly model-dependent. While this model matches the optical light curve well, it overpredicts the observed flux in the ultraviolet bands. This may indicate that the shock is not a blackbody, perhaps because of line blanketing in the UV. Alternatively, it could point to another physical explanation for the optical blue bump, such as interaction with circumstellar material or an unusual nickel distribution. Early optical spectra of SN 2017cbv show strong carbon (C ii λ 6580) absorption up through day −13 with respect to maximum light, suggesting that the progenitor system contains a significant amount of unburned material. These early results on SN 2017cbv illustrate the power of early discovery and intense follow-up of nearby supernovae to resolve standing questions about the progenitor systems and explosion mechanisms of SNe Ia.

79 ASTRONOMY AND ASTROPHYSICS↗