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DOE OSTI2022

Dark Energy Survey Year 3 results: Cosmological constraints from galaxy clustering and weak lensing

We present the first cosmology results from large-scale structure using the full 5000 deg 2 of imaging data from the Dark Energy Survey (DES) Data Release 1. We perform an analysis of large-scale structure combining three two-point correlation functions ( 3 × 2 pt ): (i) cosmic shear using 100 million source galaxies, (ii) galaxy clustering, and (iii) the cross-correlation of source galaxy shear with lens galaxy positions, galaxy–galaxy lensing. To achieve the cosmological precision enabled by these measurements has required updates to nearly every part of the analysis from DES Year 1, including the use of two independent galaxy clustering samples, modeling advances, and several novel improvements in the calibration of gravitational shear and photometric redshift inference. The analysis was performed under strict conditions to mitigate confirmation or observer bias; we describe specific changes made to the lens galaxy sample following unblinding of the results and tests of the robustness of our results to this decision. We model the data within the flat Λ CDM and w CDM cosmological models, marginalizing over 25 nuisance parameters. We find consistent cosmological results between the three two-point correlation functions; their combination yields clustering amplitude S 8 = 0.77 6 - 0.017 + 0.017 and matter density Ω m = 0.33 9 - 0.031 + 0.032 in Λ CDM , mean with 68% confidence limits; S 8 = 0.77 5 - 0.024 + 0.026 , Ω m = 0.35 2 - 0.041 + 0.035 , and dark energy equation-of-state parameter w = - 0.9 8 - 0.20 + 0.32 in w CDM . These constraints correspond to an improvement in signal-to-noise of the DES Year 3 3 × 2 pt data relative to DES Year 1 by a factor of 2.1, about 20% more than expected from the increase in observing area alone. This combination of DES data is consistent with the prediction of the model favored by the Planck 2018 cosmic microwave background (CMB) primary anisotropy data, which is quantified with a probability-to-exceed p = 0.13 –0.48. We find better agreement between DES 3 × 2 pt and Planck than in DES Y1, despite the significantly improved precision of both. When combining DES 3 × 2 pt data with available baryon acoustic oscillation, redshift-space distortion, and type Ia supernovae data, we find p = 0.34 . Combining all of these datasets with Planck CMB lensing yields joint parameter constraints of S 8 = 0.81 2 - 0.008 + 0.008 , Ω m = 0.30 6 - 0.005 + 0.004 , h = 0.68 0 - 0.003 + 0.004 , and ∑ m ν < 0.13 eV (95% C.L.) in Λ CDM ; S 8 = 0.81 2 - 0.008 + 0.008 , Ω m = 0.30 2 - 0.006 + 0.006 , h = 0.68 7 - 0.007 + 0.006 , and w = - 1.03 1 - 0.027 + 0.030 in w CDM .

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2025

Bayesian calibration of bubble size dynamics applied to CO 2 gas fermenters

To accelerate the scale-up of gaseous CO 2 fermentation reactors, computational models need to predict gas-to-liquid mass transfer which requires capturing the bubble size dynamics, i.e. bubble breakup and coalescence. However, the applicability of existing models beyond air–water mixtures remains to be established. Here, an inverse modeling approach, accelerated with a neural network surrogate, calibrates the breakup and coalescence closure models, that are used in class methods for population balance modeling (PBM). The calibration is performed based on experimental results obtained in a CO 2 -air–water-coflowing bubble column reactor. Bayesian inference is used to account for noise in the experimental dataset and bias in the simulation results. To accurately capture gas holdup and interphase mass transfer, the results show that the breakage rate needs to be increased by one order of magnitude. In conclusion, the inferred model parameters are then used on a separate configuration and shown to also improve bubble size distribution predictions.

09 BIOMASS FUELS↗

DOE OSTI2024

Reconnaissance with JWST of the J-region Asymptotic Giant Branch in Distance Ladder Galaxies: From Irregular Luminosity Functions to Approximation of the Hubble Constant

Abstract We study stars in the J-regions of the asymptotic giant branch (JAGB) of near-infrared color–magnitude diagrams in the maser host NGC 4258 and four hosts of six Type Ia supernovae (SNe Ia): NGC 1448, NGC 1559, NGC 5584, and NGC 5643. These clumps of stars are readily apparent near 1.0 < F150W − F277W < 1.5 andm F150W = 22–25 mag with James Webb Space Telescope NIRCam photometry. Various methods have been proposed to assign an apparent reference magnitude to this recently proposed standard candle, including the mode, median, sigma-clipped mean, or a modeled luminosity function parameter. We test the consistency of these by measuring intrahost variations, finding differences of up to ∼0.2 mag that significantly exceed statistical uncertainties. Brightness differences appear intrinsic, and are further amplified by the nonuniform shape of the JAGB luminosity function, also apparent in the LMC and SMC. We follow a “many methods” approach to measure consistently JAGB magnitudes and distance moduli to the SN Ia host sample calibrated by NGC 4258. We find broad agreement with distance moduli measured from Cepheids, tip of the red giant branch, and Miras. However, the SN host mean distance modulus estimated via the JAGB method necessary to estimateH 0 differs by ∼0.19 mag among the above definitions, the result of different levels of luminosity function asymmetry. The methods yield a full range of 71−78 km s −1 Mpc −1 , i.e., a fiducial result ofH 0 = 74.7 ± 2.1(stat) ± 2.3(sys, ±3.1 if combined in quadrature) km s −1 Mpc −1 , with systematic errors limited by the differences in methods. Future work may seek to standardize and refine this promising tool further, making it more competitive with established distance indicators.

Astronomy & Astrophysics↗

DOE OSTI2021

Clustering of CODEX clusters

The clustering of galaxy clusters links the spatial nonuniformity of dark matter halos to the growth of the primordial spectrum of perturbations. The amplitude of the clustering signal is widely used to estimate the halo mass of astrophysical objects. The advent of cluster mass calibrations enables using clustering in cosmological studies. Aims. We analyze the autocorrelation function of a large contiguous sample of galaxy clusters, the Constrain Dark Energy with X-ray (CODEX) sample, in which we take particular care of cluster definition. These clusters were X-ray selected using the ROentgen SATellite All-Sky Survey and then identified as galaxy clusters using the code redMaPPer run on the photometry of the Sloan Digital Sky Survey. We develop methods for precisely accounting for the sample selection effects on the clustering and demonstrate their robustness using numerical simulations. Methods. Using the clean CODEX sample, which was obtained by applying a redshift-dependent richness selection, we computed the two-point autocorrelation function of galaxy clusters in the 0.1 < z < 0.3 and 0.3 < z < 0.5 redshift bins. We compared the bias in the measured correlation function with values obtained in numerical simulations using a similar cluster mass range. Results. By fitting a power law, we measured a correlation length r 0 = 18.7 ± 1.1 and slope γ = 1.98 ± 0.14 for the correlation function in the full redshift range. By fixing the other cosmological parameters to their nine-year Wilkinson Microwave Anisotropy Probe values, we reproduced the observed shape of the correlation function under the following cosmological conditions: Ω m0 = $0.22_{-0.03}^{+0.04}$ and S 8 = σ 8 (Ω m0 /0.3)0.5 = $0.85_{-0.08}^{+0.10}$ with estimated additional systematic errors of σ Ω m0 = 0.02 and σS 8 = 0.20. We illustrate the complementarity of clustering constraints by combining them with CODEX cosmological constraints based on the X-ray luminosity function, deriving Ωm = 0.25 ± 0.01 and σ 8 = $0.81_{-0.02}^{+0.01}$ with an estimated additional systematic error of σ Ω m0 = 0.07 and σ σ8 = 0.04. The mass calibration and statistical quality of the mass tracers are the dominant source of uncertainty.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2025

Equation of state of boron carbide B 4 ⁢C

We present the results of recent experiments conducted on the Sandia Z machine and a new tabular equation of state for B 4 ⁢C. The equation of state was calibrated to a combination of density functional calculations reported here and fits to preexisting data. It was constructed partly to recover the effects of a shock-driven, polymorphic phase transition of unknown character beginning at particle velocities of just under 3 km/s (shock pressures of 95 GPa). Some of the Z experiments included sound speeds determined by the overtaking rarefaction method, from which we calculate the Grüneisen parameter and compare with previous experiments conducted at the OMEGA laser [Fratanduono et al ., Phys. Rev. B 94 , 184107 (2016)], our own first principles calculations, and another recent tabular equation of state [Zhang et al ., Phys. Rev. E 102 , 053203 (2020)]. We also compare our results with previous static compression, thermophysical, and melt studies, finding mixed consistency. We predict the onset and completion of shock melting at 225 and 265 GPa, respectively, and predict a melt curve that is largely flat to pressures of several hundred GPa.

36 MATERIALS SCIENCE↗

DOE OSTI2020

In-situ spectroscopic ellipsometry for real-time characterization of the effects of high-flux helium plasmas on tungsten surfaces

Tungsten samples were exposed to He plasmas generated by an RF source (Γ i = 3.5 x 10 16 He cm -2 s -1 , ion energy = 92 eV.) The range of exposure conditions selected here is conducive to the growth of nearsurface He bubbles, and at higher fluence, the formation of W nanotendrils ranging between 50 — 100 nm in diameter. The evolution of these surface features was probed using a fixed-angle ellipsometer (280 — 1000 nm wavelength range) with direct line-of-sight to the sample. Over the parameter space explored here, changes in the two angles (p, S) that define the polarization of the reflected light followed a distinct trajectory with increasing plasma fluence. Ex-situ ellipsometry of 22 additional tungsten specimens tested at a wide range of plasma fluences and temperatures mapped onto these in-situ results well. We used helium ion microscopy and focused ion beam profiling to provide a direct calibration of the ellipsometry measurements. Our results indicate that for a reproducible process such as the growth helium-induced surface morphologies, ellipsometry is a practical in-situ diagnostic to study how fusion plasmas modify materials. To study more general effects of plasmas on surfaces, including co-deposition and sputtering, different approaches to modelling the optical properties of the exposed surfaces are also considered.

36 MATERIALS SCIENCE↗

DOE OSTI2022

AI-Enabled Discovery and Physics-Based Optimization of Energy Efficient Processing Strategies for Advanced Turbine Alloys (Final Technical Report)

In this project, the multi-organizational team of academic and industrial researchers from the University of Kentucky an aerospace and energy generation OEM partner has leveraged novel Digital Process Twin (DPT) models of process/structure interactions (i.e., process-induced surface integrity) to advance a paradigm of fully integrated computational materials engineering (ICME). Using efficient process models as the core of a digital process simulator for a reinforcement learning algorithm, the team has integrated industrial data and metrics of structure/performance/energy relationships and manufacturing-related energy metrics to optimize dynamic processing parameters for significantly improved life-cycle energy efficiency of advanced γ-TiAl low-pressure turbine (LPT) alloys, as indicated by a set of design relevant parameters (e.g., residual stresses and scrap rate). The key objective and anticipated outcome of the project was at least a 10% reduction in life-cycle embodied energy for a recently developed, γ-TiAl low-pressure turbine (LPT) alloy and nickel-based superalloy Inconel 718, through the adoption of the proposed AI-enabled process optimization approach. The final project outcomes significantly exceeded this original target, realizing manufacturing-related energy efficiency improvements of more than 130% for TiAl and up to 80% for Inconel 718. Rather than following the prevailing and highly inefficient empirical paradigm, the proposed study demonstrated the feasibility of adopting a digital, physics-based process design and optimization paradigm. The recurring need for manual intervention, rework, reinspection causes significant production bottlenecks and unnecessary expense associated with delivering the requisite component quality. The OEM partner, and turbine industry in general, expect to reap significant cost and resource savings if an AI-optimized set of parameters can be applied to specific machining operations. The technical scope of the proposed project involved the paving of a realistic path towards model-based and AI-enabled Integrated Computational Materials Engineering (ICME), and away from inefficient empirical process optimization and legacy manufacturing practices, which are no longer able to efficiently process novel high-performance turbine alloy materials. The project team will address the fundamental knowledge gap that currently exists within the ICME paradigm with respect to the process/structure/performance/energy impacts of finishing processes. While significant resources have been devoted to the ‘early stages’ of manufacturing, such as alloy design, primary and secondary processing, finishing processes have not been adequately integrated within ICME. To provide an actionable path towards model-based finishing process design (e.g., machining, burnishing, grinding, polishing), we will employ a novel AI-enabled process optimization paradigm, based on a computationally efficient, physics-based process simulator. Through limited experimental work to calibrate and validate our process simulator model via an advanced in-situ characterization technique and process optimization via reinforcement learning, the project will seek to demonstrate a viable alternative to the inefficient ‘legacy’ processing strategies, empirical testing and broad scope machining learning approaches, all of which fail to adequately consider complex process physics. The project team has identified an intermetallic γ-TiAl LPT alloy, which is currently being used as part of the OEM partner’s advanced gas turbine designs. This particular alloy poses significant manufacturing challenges during finishing operations, which limit the degree to which the current turbine design can be manufactured in an energy- and cost-efficient manner. Empirical testing and numerical modeling efforts to optimize processing parameters for γ-TiAl have not been able to resolve these manufacturing challenges, so the proposed physics-based AI-enabled optimization technology would offer a truly novel and transformative capability. The multi-organizational team of academic and industry experts from the UKY and the OEM partner will work together closely to demonstrate the analytical and experimental critical function and characteristic proof of concept of this novel approach.

20 FOSSIL-FUELED POWER PLANTS↗

DOE OSTI2025

Cosmological constraints from the cross-correlation of DESI Luminous Red Galaxies with CMB lensing from Planck PR4 and ACT DR6

Here, we infer the growth of large scale structure over the redshift range 0.4 ≲ z ≲ 1 from the cross-correlation of spectroscopically calibrated Luminous Red Galaxies (LRGs) selected from the Dark Energy Spectroscopic Instrument (DESI) legacy imaging survey with CMB lensing maps reconstructed from the latest Planck and ACT data. We adopt a hybrid effective field theory (HEFT) model that robustly regulates the cosmological information obtainable from smaller scales, such that our cosmological constraints are reliably derived from the (predominantly) linear regime. We perform an extensive set of bandpower- and parameter-level systematics checks to ensure the robustness of our results and to characterize the uniformity of the LRG sample. We demonstrate that our results are stable to a wide range of modeling assumptions, finding excellent agreement with a linear theory analysis performed on a restricted range of scales. From a tomographic analysis of the four LRG photometric redshift bins we find that the rate of structure growth is consistent with ΛCDM with an overall amplitude that is ≃ 5-7% lower than predicted by primary CMB measurements with modest (∼ 2σ) statistical significance. From the combined analysis of all four bins and their cross-correlations with Planck we obtain S 8 = 0.765 ± 0.023, which is less discrepant with primary CMB measurements than previous DESI LRG cross Planck CMB lensing results. From the cross-correlation with ACT we obtain S 8 = 0.790 +0.024 -0.027 , while when jointly analyzing Planck and ACT we find S 8 = 0.775 +0.019 -0.022 from our data alone and σ 8 = 0.772 +0.020 -0.023 with the addition of BAO data. These constraints are consistent with the latest Planck primary CMB analyses at the ≃ 1.6-2.2σ level, and are in excellent agreement with galaxy lensing surveys.

cosmological parameters from LSS↗

DOE OSTI2021

Measuring H 0 using X-ray and SZ effect observations of dynamically relaxed galaxy clusters

ABSTRACT We use a sample of 14 massive, dynamically relaxed galaxy clusters to constrain the Hubble constant, H0, by combining X-ray and Sunyaev–Zel’dovich (SZ) effect signals measured with Chandra, Planck, and Bolocam. This is the first such analysis to marginalize over an empirical, data-driven prior on the overall accuracy of X-ray temperature measurements, while our restriction to the most relaxed, massive clusters also minimizes astrophysical systematics. For a cosmological-constant model with Ωm = 0.3 and ΩΛ = 0.7, we find $H_0 = 67.3^{+21.3}_{-13.3}\, \mathrm{km}\, \mathrm{s}^{-1}\, \mathrm{Mpc}^{-1}$, limited by the temperature calibration uncertainty (compared to the statistically limited constraint of $H_0 = 72.3^{+7.6}_{-7.6}\, \mathrm{km}\, \mathrm{s}^{-1}\, \mathrm{Mpc}^{-1}$). The intrinsic scatter in the X-ray/SZ pressure ratio is found to be 13 ± 4 per cent (10 ± 3 per cent when two clusters with significant galactic dust emission are removed from the sample), consistent with being primarily due to triaxiality and projection. We discuss the prospects for reducing the dominant systematic limitation to this analysis, with improved X-ray calibration and/or precise measurements of the relativistic SZ effect providing a plausible route to per cent level constraints on H0.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2023

BEYONDPLANCK X. Planck Low Frequency Instrument frequency maps with sample-based error propagation

We present Planck Low Frequency Instrument (LFI) frequency sky maps derived within the BEYONDPLANCK framework. This framework draws samples from a global posterior distribution that includes instrumental, astrophysical, and cosmological parameters, and the main product is an entire ensemble of frequency sky map samples, each of which corresponds to one possible realization of the various modeled instrumental systematic corrections, including correlated noise, time-variable gain, as well as far sidelobe and bandpass corrections. This ensemble allows for computationally convenient end-to-end propagation of low-level instrumental uncertainties into higher-level science products, including astrophysical component maps, angular power spectra, and cosmological parameters. We show that the two dominant sources of LFI instrumental systematic uncertainties are correlated noise and gain fluctuations, and the products presented here support – for the first time – full Bayesian error propagation for these effects at full angular resolution. We compared our posterior mean maps with traditional frequency maps delivered by the Planck Collaboration, and find generally good agreement. The most important quality improvement is due to significantly lower calibration uncertainties in the new processing, as we find a fractional absolute calibration uncertainty at 70 GHz of Δg 0 /g 0 = 5 x 10 -5 , which is nominally 40 times smaller than that reported by Planck 2018. However, we also note that the original Planck 2018 estimate has a nontrivial statistical interpretation, and this further illustrates the advantage of the new framework in terms of producing self-consistent and well-defined error estimates of all involved quantities without the need of ad hoc uncertainty contributions. We describe how low-resolution data products, including dense pixel-pixel covariance matrices, may be produced from the posterior samples directly, without the need for computationally expensive analytic calculations or simulations. We conclude that posterior-based frequency map sampling provides unique capabilities in terms of low-level systematics modeling and error propagation, and may play an important role for future Cosmic Microwave Background (CMB) B-mode experiments aiming at nanokelvin precision.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2025

Computational materials reliability assessment of hydrogen fueled gas turbine power generation engines

The use of blended fuel sources in land based gas turbine engines drives variations in the resulting operational profile (temperatures and pressures) which can impact engine reliability. Furthermore, variability in the manufacture of components affects the resulting microstructure which directly impacts material performance and reliability. Currently, data-driven models are typically used for maintaining and inspecting fleets of engines. Without explicitly capturing material and operational sources of variability conservatism must be used in developing component-level reliability models. Therefore, there exists an opportunity to use information from materials-scale physics models to better inform reliability modeling and reduce conservatism; the impact is more cost-efficient operation and maintenance of current and future fleets. Specifically, this work establishes a computational framework for evaluating the probabilistic high temperature creep performance of hot-section Ni-based superalloys where uncertainty comes from both microstructural and operational variability. A novel high-fidelity physics model which phenomenologically captures grain-boundary sensitive phenomena has been established. A probabilistic calibration procedure was used to calibrate the model and capture uncertainty in the parameterized model coefficients. A design of experiments methodology was established for identifying informative microstructural digital representations for suitable for forward model evaluation. Results show that training a machine-learning surrogate using this design criteria outperforms random selection of microstructural representations. Finally, two surrogate models were developed: (1) a deterministic surrogate model which predicts the local field response given microstructure, constitutive model parameters, and operating conditions (stress, temperature) and (2) a probabilistic model, where uncertainty comes from constitutive law uncertainty, built using denoising diffusion probabilistic models which samples responses given (1) microstructure and (2) operating conditions. These surrogate models enable partner Siemens Energy to rapidly perform UQ analysis specific to creep deformation across a range of microstructures and operating conditions. The impact is that these ML and physics codes can be used to establish more advanced reliability models for the inspection, servicing, and maintenance of land based gas turbine engines.

36 MATERIALS SCIENCE↗

DOE OSTI2022

A probabilistic inverse prediction method for predicting plutonium processing conditions

In the past decade, nuclear chemists and physicists have been conducting studies to investigate the signatures associated with the production of special nuclear material (SNM). In particular, these studies aim to determine how various processing parameters impact the physical, chemical, and morphological properties of the resulting special nuclear material. By better understanding how these properties relate to the processing parameters, scientists can better contribute to nuclear forensics investigations by quantifying their results and ultimately shortening the forensic timeline. This paper aims to statistically analyze and quantify the relationships that exist between the processing conditions used in these experiments and the various properties of the nuclear end-product by invoking inverse methods. In particular, these methods make use of Bayesian Adaptive Spline Surface models in conjunction with Bayesian model calibration techniques to probabilistically determine processing conditions as an inverse function of morphological characteristics. Not only does the model presented in this paper allow for providing point estimates of a sample of special nuclear material, but it also incorporates uncertainty into these predictions. This model proves sufficient for predicting processing conditions within a standard deviation of the observed processing conditions, on average, provides a solid foundation for future work in predicting processing conditions of particles of special nuclear material using only their observed morphological characteristics, and is generalizable to the field of chemometrics for applicability across different materials.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2020

Measurements of a 2.1 MeV H− beam with an Allison scanner

Transverse 2D phase space distributions of a 2.1 MeV, 5 mA H− beam are measured at the Proton Improvement Plan II Injector Test accelerator at Fermilab with an Allison scanner. This paper describes the design, calibration, and performance of the scanner along with the main results from beam measurements. Analyses of the recorded phase portraits are performed primarily in action-phase coordinates. The stability of the action under linear optics makes it easier to compare measurements taken under different beamline conditions. The amplitude of a single measured point (“pixel”) is proportional to the phase density in the corresponding portion of the beam. When the Twiss parameters are calculated using only the high-phase density part of the beam, the pixel amplitude in the beam core is found to be decreasing approximately exponentially with action and to be phase-independent. Outside of the core, the amplitudes decrease with action at a slower rate than in the core. This “tail” comprises 10%–30% of the beam, with 0.1% of the total measured intensity extending beyond action 10–20 times larger than the rms emittance. The transition from the core to the tail is accompanied by the appearance of two “branches” that are separated in phase and extend beyond the core. A set of selected measurements shows that there is no measurable emittance dilution along the beamline; the beam parameters are practically constant over a 0.5 ms pulse; and scraping in various parts of the beamline is an effective way to decrease the transverse tails by removing the branches.

Richard, C. (ORCID:000000029891135X)↗

DOE OSTI2026

Chiral interactions and superfluidity in the calcium isotopic chain

We perform ab initio calculations of three-point mass differences in the odd- and even-mass 39−49Ca isotopes to probe nuclear superfluidity via empirical neutron pairing gaps. We also quantify the sensitivity of those gaps to the parameters of the interaction at mean-field level. Recent studies employing accurate chiral nuclear interactions have found these gaps to be too small. We show that experimental values can be reproduced at mean-field level by substantially increasing the attraction of the singlet 𝑆 -wave two-nucleon contact interaction, but doing so induces an unphysical bound state of the dineutron. The sensitivity of these predictions to the full calibration of the nuclear interaction is then studied by performing Bayesian posterior sampling in a delta-full chiral effective field theory at third chiral order. We find that pairing gaps remain largely unaffected, leaving the explanation of nuclear superfluidity as a future task for improved many-body modeling and refined interactions at higher chiral orders.

Hagen, Gaute [ORNL] (ORCID:0000000160191687)↗

DOE OSTI2023

Refined room-temperature equation of state of Bi up to 260 GPa

At room temperature, bismuth undergoes several structural transitions with increasing pressure before taking on a body-centered cubic (bcc) phase at approximately 8 GPa. The bcc structure is stable to the highest measured pressure and its simplicity, along with its high compressibility and atomic number, makes it an enticing choice as a pressure calibrant. Here, we present three data sets on the compression of bismuth in a diamond anvil cell in a neon pressure medium, up to a maximum pressure of about 260 GPa. The use of a soft pressure medium reduces deviatoric stress when compared to previous work. With an expanded pressure range, a higher point density, and a decreased uniaxial stress component, we are able to provide more reliable equation of state parameters. We also conduct density functional theory electronic-structure calculations that confirm that the bcc phase is energetically favored at high pressure.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

DOE OSTI2022

Hot Spot Evolution Measured by High-Resolution X-Ray Spectroscopy at the National Ignition Facility

Evolution of the hot spot plasma conditions was measured using high-resolution x-ray spectroscopy at the National Ignition Facility. The capsules were filled with DD gas with trace levels of Kr and had either a high-density-carbon (HDC) ablator or a tungsten (W)-doped HDC ablator. Time-resolved measurement of the Kr He β spectra, absolutely calibrated by a simultaneous time-integrated measurement, allows inference of the electron density and temperature through observing Stark broadening and the relative intensities of dielectronic satellites. By matching the calculated hot spot emission using a collisional-radiative code to experimental observations, the hot spot size and areal density are determined. Here these advanced spectroscopy techniques further reveal the effect of W dopant in the ablator on the hot spot parameters for their improved implosion performance.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2026

The S-PLUS Fifth Data Release: Over 4500 Square Degrees of the Southern Sky and a Multicolor View of the Hydra and Antlia Galaxy Clusters

Abstract We present the fifth data release (DR5) of the Southern Photometric Local Universe Survey (S-PLUS), covering 4592 deg 2 across 2491 fields. Observations were conducted with the T80-South, a Brazilian robotic telescope equipped with the Javalambre 12-filter system, containing five broadband and seven narrowband filters. Data products feature FITS images and extensive catalogs containing fluxes, magnitudes, and shape parameters for over 113 million detections. In addition, several value-added catalogs are provided, offering photometric redshifts (photo- zs ), object classifications, masks, and extinction coefficients. For the first time, this release includes coverage of 110 deg 2 along the Galactic disk, facilitating new research into Galactic structure and stellar populations. The release also provides full coverage of the Hydra Supercluster and numerous other nearby clusters with improved data reduction and calibration, enhancing photo- z accuracy, which is vital for large-scale structure studies. A preliminary analysis of the Hydra and Antlia galaxy clusters up to 5 × R 200 yields an updated catalog of 1706 cluster members based on both spectroscopic and high-quality photo- zs . Our photometric data shows that both clusters have a similar proportion of galaxies with an H α excess relative to their clustercentric distance, though Hydra has a higher fraction near its center. Additionally, the spatial distribution of all objects in our sample highlights a bridge connecting both clusters. We verify that S-PLUS DR5 provides a solid foundation for future scientific investigations, ranging from solar system studies to cosmology.

Vinicius Rodrigues de Lima, Erik [Universidade de ↗

DOE OSTI2022

The WRF-Solar Ensemble Prediction System: Development, Test, and Validation

Providing reliable probabilistic solar radiation information is needed to improve management of the uncertainty and variability of solar generation. Thus, guidance on how to develop skillful and accurate ensemble forecasts is essential and it will ultimately contribute to integration of high amounts of solar energy on the grid. A team from the National Renewable Energy Laboratory and the National Center for Atmospheric Research had been collaborating to develop the WRF-Solar ensemble prediction system (WRF-Solar EPS) in the past three years to produce probabilistic solar irradiance forecasts and better predict solar energy by quantifying forecast uncertainty. The WRF-Solar EPS basically generates ensemble members for solar irradiance based on stochastic perturbations to provide intraday and day-ahead probabilistic forecasts. This study will present main research steps in developing the WRF-Solar EPS including: (a) tangent linear analysis for identifying key input variables of six WRF-Solar modules significantly related to predicting of cloud and solar irradiance, (b) combining stochastic perturbation technique with the WRF-Solar model, and (c) ensemble calibration method to decrease error and uncertainty of ensemble-based solar forecasts. The capability of WRF-Solar EPS is now updated to the most recent version of standard WRF model. This presentation will summarize comprehensive results from the evaluation of forecasts against the National Solar Radiation Data Base as well as ground-measured observations. Moreover, we will introduce the user's guide for WRF-Solar EPS (e.g., parameters to configure stochastic perturbations) and future extension of this research.

day-ahead forecast↗