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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Estimating soil thermal inertia profiles from the passive equilibration of a temperature probe: Supporting data and code

This dataset contains measurements of soil thermal properties and organic layer thickness at three field sites on the Seward Peninsula near Nome, Alaska, USA. Code is also provided for estimation of soil thermal properties from temperature data, including a forward heat transfer mode, a library of simulated temperature data, and an inversion scheme to retrieve thermal properties from temperature measurements (Lamb et al., 2025). This dataset and code is published in support of the manuscript “Estimating Soil Thermal Inertia Profiles from the Passive Equilibration of a Temperature Probe” by Lamb et al., 2025, in which an experimental thermal measurements technique is developed. The dataset contains measurements of soil thermal properties and organic layer thickness obtained with an industry standard thermal properties analyzer and direct observations, as well as data generated using the experimental technique to estimate thermal properties and soil structure from temperature observations. All data is reported in .csv format, and all code is written in Python 3.7 using standard packages. This research was performed as a part of the NGEE Arctic project, which aims to advance model predictions of arctic carbon cycle responses to a changing climate over the 21st Century.The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), was a research effort to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy's Office of Biological and Environmental Research. The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska. Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy's Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Assimilation of Multiscale Data into Multifidelity Biogeochemical Models (Final Report)

Quantitative predictions of subsurface processes rely on computational models that capture, with different degrees of fidelity, complex interactions between hydrologic and biogeochemical processes. Molecular- and pore-scale models provide a high-fidelity representation of these processes but are impractical at the field scale. Reduced complexity (e.g., field-scale or data-driven) models sacrifice some degree of fidelity in favor of computational efficiency. Uncertainty and assimilation of data into model predictions, pose a question of model selection: Given a significant difference in computational cost, can a lower-fidelity model be preferable to its higher-fidelity counterpart? Since a predictive model must be computable in reasonable time it has to operate at the field scale, with molecular- and pore-scale data (obtained either from simulations or measurements) determining both the model's structure and parameters. Availability of such multi-resolution data raises a question hitherto undressed in hydrogeology: Can a coarse model dynamically ``learn'' its own structure (e.g., adjust the reaction pathways in its transport module) as more fine-scale data become available during simulations? Our results in multifidelity simulations, data assimilation, and machine learning led us to hypothesize that the answer to these questions is ``yes''. The overarching goal of this project was to confirm this hypothesis by developing scalable, computationally efficient tools for assimilation of data into multiscale models, in which fine-scale data and simulations dynamically inform and autonomously modify coarse-scale models. Our research led to nine manuscripts, three of which have been published and the other six are currently under review.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2020

Measurement of the Ratio of the Neutron to Proton Structure Functions, and the Three-Nucleon EMC Effect in Deep Inelastic Electron Scattering Off Tritium and Helium-3 Mirror Nuclei

The proton and neutron structure functions Fp 2 and Fn 2, respectively are fundamental to understanding many studies in nuclear physics. They provide important information about quark distributions. For example, the ratio Fn 2/Fp 2 is one of the best measurements to find the ratio of d quark over u quark distribution inside the proton. While the calculations of structure functions and quark distributions are non-perturbative, they can be determined by the parameterization of experimental data. The understanding of Fn 2/Fp 2 and d/u as x -> 1 has a large influence on global fits and parameterization, and can be used to distinguish the non-perturbative models which give different predictions. However, Fn 2/Fp 2 measured using deuteron and hydrogen targets has large nuclear uncertainties at large x, because the nuclear effects in the deuteron become significant at large x. The MARATHON experiment, which ran in spring 2018 using the upgraded 11 GeV Jefferson Lab electron beam, employs a novel method. It performed deep inelastic scattering off tritium and helium-3 mirror nuclei to measure Fn 2/Fp 2 over the range x = 0.17 to x = 0.82. Since tritium and helium-3 are mirror nuclei, theoretical uncertainties largely cancel out in the ratio. The extracted Fn 2/Fp 2 has much smaller uncertainties compared with previous experiments at large x. The MARATHON experiment also provided results on the EMC effect for tritium and helium-3 nuclei. The results are considered essential for understanding the EMC effect. This thesis describes the MARATHON experiment, and presents results for Fn 2/Fp 2, and the EMC effect for tritium and helium-3.

Liu, Hanjie↗
DOE OSTI2024

The Role of the Toroidal Vortex in Cumulus Clouds' Entrainment and Mixing

Abstract Shallow convective clouds play a crucial role in Earth's energy budget, as they modulate the radiative transfer in the atmosphere and participate in the vertical transport of aerosols, energy, and humidity. The parameterizations representing these complex, vital players in weather and climate models are mostly based on a description of steady‐state plumes and are a source of major uncertainty. Recently, several studies have shown that buoyant thermals are inherent in atmospheric convection and contain a toroidal (ring) vortex. This work studies those vortices in growing shallow cumulus (Cu) clouds using high‐resolution (10 m) Large Eddy Simulations that resolve these vortices in much detail. Recent analysis of such data showed that small‐scale turbulent diffusion is unable to explain the large diluted portion of the cloud. Here we advocate for the important role of the Cu toroidal vortex (TV) in cloud dilution and present the complex dynamics and structure of a Cu TV. Nevertheless, since the vortex dominates the cloud's dilution, simplicity emerges when considering the cloud's lateral mass flux profile. The cloud mixing is quantified using direct flux calculations and Eulerian tracers. In addition, Lagrangian tracers are used to identify the origin of the entrained air and its thermodynamic properties. It shows that most of the air entrained by the vortex is not recycled by the vortex, yet is significantly more humid than the environment. We suggest that the development of new models describing thermals, together with their toroidal vortices, might improve cloud parameterizations in weather and climate models.

Meteorology & Atmospheric Sciences↗
DOE OSTI2022

Distances to Prompt Effects for a Nuclear Device

In 2010, nuclear weapon effects experts at Sandia National Laboratories (SNL) were asked to provide a quick reference document containing estimated prompt nuclear effects. This report is an update to the 2010 document that includes updated model assumptions. This report addresses only the prompt effects associated with a nuclear detonation (e.g., blast, thermal fluence, and prompt ionizing radiation). The potential medium- and longer-term health effects associated with nuclear fallout are not considered in this report because, in part, of the impracticality of making generic estimates given the high dependency of fallout predictions on the local meteorological conditions at the time of the event. The results included in this report also do not consider the urban environment (e.g., shielding by or collapse of structures) which may affect the extent of prompt effects. It is important to note that any operational recommendations made using the estimates in this report are limited by the generic assumptions considered in the analysis and should not replace analyses made for a specific scenario/device. Furthermore, nuclear effects experts (John Hogan, SNL, and Byron Ristvet, Defense Threat Reduction Agency (DTRA)) have indicated that the accuracy of effects predictions below 0.5 kilotons (kT) or 500 tons nuclear yield have greater uncertainty because of the limited data available for the prompt effects in this regime. The Specialized Hazard Assessment Response Capability (SHARC) effects prediction tool was used for these analyses. Specifically, the NUKE model within SHARC 2021 Version 10.2 was used. NUKE models only the prompt effects following a nuclear detonation. The algorithms for predicting range-to-output data contained within the NUKE model are primarily based on nuclear test effects data. Probits have been derived from nuclear test data and the U.S. Environmental Protection Agency (EPA) protective action guides. Probits relate the probability of a hazard (e.g., fatality or injury) caused by a given insult (e.g., overpressure, thermal fluence, dose level). Several probits have been built into SHARC to determine the fatality and injury associated with a given level of insult. Some of these probits differ with varying yield. Such probits were used to develop the tables and plots in this report.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗
DOE OSTI2022

Determining g A / g V with High-Resolution Spectral Measurements Using a LiInSe 2 Bolometer

Neutrinoless double beta decay (0νββ) processes sample a wide range of intermediate forbidden nuclear transitions, which may be impacted by quenching of the axial vector coupling constant (g A /g V ), the uncertainty of which plays a pivotal role in determining the sensitivity reach of 0νββ experiments. Here we present measurements performed on a high-resolution LiInSe 2 bolometer in a “source = detector” configuration to measure the spectral shape of the fourfold forbidden β decay of 115 In . The value of g A /g V is determined by comparing the spectral shape of theoretical predictions to the experimental β spectrum taking into account various simulated background components as well as a variety of detector effects. We find evidence of quenching of g A /g V at >5 σ with a model-dependent quenching factor of 0.655 ± 0.002 as compared to the free-nucleon value for the interacting shell model. We also measured the 115 In half-life to be [5.18 ± 0.06(stat) $^{+0.005}_{- 0.015}$(sys)] x 10 14 yr within the interacting shell model framework. This Letter demonstrates the power of the bolometeric technique to perform precision nuclear physics single-β decay measurements, which along with improved nuclear modeling can help reduce the uncertainties in the calculation of several decay nuclear matrix elements including those used in 0νββ sensitivity calculations.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2023

Measurement of gravitational lensing of the cosmic microwave background using SPT-3G 2018 data

We present a measurement of gravitational lensing over 1500 deg 2 of the Southern sky using SPT-3G temperature data at 95 GHz and 150 GHz taken in 2018. The lensing amplitude relative to a fiducial Planck 2018 Lambda cold dark matter ( Λ CDM ) cosmology is found to be 1.020 ± 0.060 , excluding instrumental and astrophysical systematic uncertainties. We conduct extensive systematic and null tests to check the robustness of the lensing measurements, and report a minimum-variance combined lensing power spectrum over angular multipoles of 50 < L < 2000 , which we use to constrain cosmological models. When analyzed alone and jointly with primary cosmic microwave background (CMB) spectra within the Λ CDM model, our lensing amplitude measurements are consistent with measurements from SPT-SZ, SPTpol, ACT, and Planck. Incorporating loose priors on the baryon density and other parameters including uncertainties on a foreground bias template, we obtain a 1 σ constraint on σ 8 Ω m 0.25 = 0.595 ± 0.026 using the SPT-3G 2018 lensing data alone, where σ 8 is a common measure of the amplitude of structure today and Ω m is the matter density parameter. Combining SPT-3G 2018 lensing measurements with baryon acoustic oscillation (BAO) data, we derive parameter constraints of σ 8 = 0.810 ± 0.033 , S 8 ≡ σ 8 ( Ω m / 0.3 ) 0.5 = 0.836 ± 0.039 , and Hubble constant H 0 = 68.8 - 1.6 + 1.3 km s - 1 Mpc - 1 . Our preferred S 8 value is higher by 1.6 to 1.8 σ compared to cosmic shear measurements from DES-Y3, HSC-Y3, and KiDS-1000 at lower redshift and smaller scales. We combine our lensing data with CMB anisotropy measurements from both SPT-3G and Planck to constrain extensions of Λ CDM . Using CMB anisotropy and lensing measurements from SPT-3G only, we provide independent constraints on the spatial curvature of Ω K = 0.01 4 - 0.026 + 0.023 (95% C.L.) and the dark energy density of Ω Λ = 0.72 2 - 0.026 + 0.031 (68% C.L.). When combining SPT-3G lensing data with SPT-3G CMB anisotropy and BAO data, we find an upper limit on the sum of the neutrino masses of ∑ m ν < 0.30 eV (95% C.L.). Due to the different combination of angular scales and sky area, this lensing analysis provides an independent check on lensing measurements by ACT and Planck.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

Electronic structure prediction of medium and high entropy alloys across composition space

We propose machine learning (ML) models to predict the electron density — the fundamental unknown of a material’s ground state — across the composition space of concentrated alloys. From this, other physical properties can be inferred, enabling accelerated exploration. A significant challenge is that the number of descriptors and sampled compositions required for accurate prediction grows rapidly with species. To address this, we employ Bayesian Active Learning (AL), which minimizes training data requirements by leveraging uncertainty quantification capabilities of Bayesian Neural Networks. Compared to the strategic tessellation of the composition space, Bayesian-AL reduces the number of training data points by a factor of 2.5 for ternary (SiGeSn) and 1.7 for quaternary (CrFeCoNi) systems. We also introduce easy-to-optimize, body-attached-frame descriptors, which respect physical symmetries while keeping descriptor-vector size nearly constant as alloy complexity increases. Our ML models demonstrate high accuracy and generalizability in predicting both electron density and energy across composition space.

materials science↗
DOE OSTI2023

Develop and Connect TRISO Failure Analysis and Uncertainty Quantification to Fission Product Release Calculation Capability

The U.S. Department of Energy’s Nuclear Energy Advanced Modeling and Simulation (NEAMS) program aims to develop predictive capabilities by applying computational methods to the analysis and design of advanced reactor and fuel cycle systems. This program has been providing engineering-scale support for the development of BISON, a high-fidelity and high-resolution fuel performance tool. Stress-based failure probability has been developed and analyzed to assess the integrity of tri-structural isotropic (TRISO) fuel particles during fuel life cycles. While simple, stress-based approaches to failure probability leveraging the Weibull statistical distribution entails a number of drawbacks when stress concentration occurs near crack tips, including finite element mesh size dependency. In this report, we use an interaction integral approach to the computation of stress intensity factors in functionally graded materials (FGM) for axisymmetric models. The inner pyrolytic carbon (IPyC) cracking induced silicon carbide (SiC) failure is one of the dominated failure modes in TRISO failure analysis. In this study, we consider a crack in the IPyC layer perpendicular to the SiC layer. The interface between these two TRISO layers is considered to be porous, which we simulate considering a transition of mechanical properties over the porous length. These aspects are considered in the computation of stress intensity factor (SIF) from a fracture mechanics approach and compared with the known stress-based failure probability approach.

42 ENGINEERING↗
DOE OSTI2025

Neural chaos: A spectral stochastic neural operator

Building surrogate models for operators with uncertainty quantification capabilities is essential for many engineering applications where randomness–such as variability in material properties, boundary conditions, and initial conditions–is unavoidable. Polynomial Chaos Expansion (PCE) is widely recognized as a go-to method for constructing stochastic surrogates in both intrusive and non-intrusive ways, and it has recently been used in the context of operator learning. However, its application becomes challenging for complex or high-dimensional processes, as achieving accuracy requires higher-order polynomials, which can increase computational demand and/or the risk of overfitting. Furthermore, PCE requires specialized treatments to manage random variables that are not independent, and these treatments may be problem-dependent or may fail with increasing complexity. Here, in this work, we adopt the same formalism as the spectral expansion used in PCE; however, we replace the classical polynomial basis functions with neural network (NN) basis functions to leverage their expressivity. To achieve this, we propose an algorithm that identifies NN-parameterized basis functions in a purely data-driven manner, without any prior assumptions about the joint distribution of the random variables involved, whether independent or dependent, or about their marginal distributions. The proposed algorithm identifies each NN-parameterized basis function sequentially, ensuring they are orthogonal with respect to the data distribution. The basis functions are constructed directly on the joint stochastic variables without requiring a tensor product structure or assuming independence of the random variables. This approach may offer greater flexibility for complex stochastic models, while simplifying implementation compared to the tensor product structures typically used in PCE to handle random vectors. This is particularly advantageous given the current state of open-source packages, where building and training neural networks can be done with just a few lines of code and extensive community support. We demonstrate the effectiveness of the proposed scheme through several numerical examples of varying complexity and provide comparisons with classical PCE.

Polynomial chaos expansion↗
DOE OSTI2020

Emergent mystery in the Kondo insulator samarium hexaboride

Samarium hexaboride (SmB 6 ) is an example of a Kondo insulator, in which strong electron correlations cause a band gap to open. SmB 6 hosts both a bulk insulating state and a conductive surface state. Within a Fermi-liquid framework, the strongly correlated ground-state electronic structure can be mapped to a simple state resembling a topological insulator. Although uncertainties remain, many experiments provide compelling evidence that the conductive surface states have a topological origin. However, the bulk behavior is less well understood and some experiments indicate bulk in-gap states. This has inspired the development of many theories that predict the emergence of new bulk quantum phases beyond Landau’s Fermi-liquid model. We review the current progress on understanding both the surface and the bulk states, especially the experimental evidence for each. A mystery centres on the existence of the bulk in-gap states and why they appear in some experiments but not others. Adding to the mystery is why quantum oscillations in SmB6 appear only in magnetization but not in resistivity. Here, we conclude by elaborating on three questions: why SmB 6 is worth studying, what can be done to move forwards and what other correlated insulators could give additional insight.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2022

Linear Seismic Source Equivalents in 3D Nonlinear Models: Effects of Embedded Small-Scale, Near-Source Structures

Gaining a proper understanding of how Earth structure and other near-source properties affect estimates of explosion yield is important to the nonproliferation mission. The yields of explosion sources are often based on seismic moment or waveform amplitudes. Quantifying how the seismic waveforms or estimates of the source characteristics derived from those waveforms are influenced by natural or man-made structures within the near-source region, where the wavefield behaves nonlinearly, is required to understand the full range of uncertainty in those yield estimates. We simulate tamped chemical explosions using a nonlinear, shock physics code and couple the ground motions beyond the elastic radius to a linear elastic, full waveform seismic simulation algorithm through 3D media. In order to isolate the effects of simple small-scale 3D structures on the seismic wavefield and linear seismic source estimates, we embed spheres and cylinders close to the fully- tamped source location within an otherwise homogenous half-space. The 3 m diameters spheres, given their small size compared to the predominate wavelengths investigated, not surprisingly are virtually invisible with only negligible perturbations to the far-field waveforms and resultant seismic source time functions. Similarly, the 11 m diameter basalt sphere has a larger, but still relatively minor impact on the wavefield. However, the 11 m diameter air-filled sphere has the largest impact on both waveforms and the estimated seismic moment of any of the investigated cases with a reduction of ~25% compared to the tamped moment. This significant reduction is likely due in large part to the cavity collapsing from the shock instead of being solely due to diffraction effects . Although the cylinders have the same diameters as the 3 m spheres, their length of interaction with the wavefield produces noticeable changes to the seismic waveforms and estimated source terms with reductions in the peak seismic moment on the order of 10%. Both the cylinders and 11 m diameter spheres generate strong shear waves that appear to emanate from body force sources.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗
DOE OSTI2024

Simulated Feasibility of 3-D Lightning Mapping From Space

In addition to the awe it inspires, lightning can illuminate the microphysical processes hidden away within deep convection. The current generation of space-based lightning mapping uses mostly 2-D optical imaging to connect overall flash characteristics to their parent storm dynamics, but are missing a dimension’s worth of information. With lightning now classified as an essential climate variable, future spaceborne mappers will need improved capabilities to take advantage of the 3-D structure of lightning flashes to support meteorological and climate modeling. We report here on a study of the feasibility of high-resolution 3-D lightning mapping using a radio frequency (RF)-based network of satellites from low-Earth orbit (LEO). Lightning sources are simulated using existing lightning mapping array (LMA) tools, modified for orbital detection, and spatially reconstructed using a Levenberg–Marquardt geolocation algorithm to assess sources of uncertainty in these solutions. We analyze the benefits and limitations of this approach compared to existing orbital and ground-based methods. Results of this study show that lightning can be mapped in 3-D with a vertical location accuracy better than 2 km using as few as five satellites in LEO capable of measuring the time-of-arrival of impulsive RF signals in the very high-frequency (VHF) band. The consequence of this study is that high-resolution, spaceborne 3-D mapping of lightning is achievable across most of the globe, having crucial implications for our understanding of not only lightning, but also severe weather development, climate science, and more.

47 OTHER INSTRUMENTATION↗
DOE OSTI2020

Benchmarking and parameter sensitivity of physiological and vegetation dynamics using the Functionally Assembled Terrestrial Ecosystem Simulator (FATES) at Barro Colorado Island, Panama

Plant functional traits determine vegetation responses to environmental variation, but variation in trait values is large, even within a single site. Likewise, uncertainty in how these traits map to Earth system feedbacks is large. We use a vegetation demographic model (VDM), the Functionally Assembled Terrestrial Ecosystem Simulator (FATES), to explore parameter sensitivity of model predictions, and comparison to observations, at a tropical forest site: Barro Colorado Island in Panama. We define a single 12-dimensional distribution of plant trait variation, derived primarily from observations in Panama, and define plant functional types (PFTs) as random draws from this distribution. We compare several model ensembles, where individual ensemble members vary only in the plant traits that define PFTs, and separate ensembles differ from each other based on either model structural assumptions or non-trait, ecosystem-level parameters, which include (a) the number of competing PFTs present in any simulation and (b) parameters that govern disturbance and height-based light competition. While single-PFT simulations are roughly consistent with observations of productivity at Barro Colorado Island, increasing the number of competing PFTs strongly shifts model predictions towards higher productivity and biomass forests. Different ecosystem variables show greater sensitivity than others to the number of competing PFTs, with the predictions that are most dominated by large trees, such as biomass, being the most sensitive. Changing disturbance and height-sorting parameters, i.e., the rules of competitive trait filtering, shifts regimes of dominance or coexistence between early- and late-successional PFTs in the model. Increases to the extent or severity of disturbance, or to the degree of determinism in height-based light competition, all act to shift the community towards early-successional PFTs. In turn, these shifts in competitive outcomes alter predictions of ecosystem states and fluxes, with more early-successional-dominated forests having lower biomass. It is thus crucial to differentiate between plant traits, which are under competitive pressure in VDMs, from those model parameters that are not and to better understand the relationships between these two types of model parameters to quantify sources of uncertainty in VDMs.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Documentation, assessment, and improvements of fission gas modeling capabilities in BISON

Fission gas behavior critically affects nuclear fuel performance, influencing thermal conductivity, microstructure, swelling, and fuel-cladding interactions. The Simple Integrated Fission Gas Release and Swelling (Sifgrs) model, implemented within the BISON fuel performance code, provides advanced capabilities for modeling these phenomena. This manuscript reviews and assesses Sifgrs’ modular structure and submodels describing intragranular, intergranular, and release mechanisms. The modular implementation enables rapid improvements as our understanding advances and new fuel forms emerge. After an overview of capabilities, we present a comprehensive validation suite, comparing predictions to experimental data from tests under normal and transient conditions, including reproduction of the Vitanza curve. Results demonstrate Sifgrs’ accuracy, robustness, and applicability. Current limitations and ongoing efforts are discussed, including mechanistic, multiscale modeling, uncertainty quantification, and validation within the Nuclear Energy Advanced Modeling and Simulation (NEAMS) program, supporting the safe and efficient use of nuclear fuels.

BISON↗
DOE OSTI2020

Reconstruction of effective potential from statistical analysis of dynamic trajectories

The broad incorporation of microscopic methods is yielding a wealth of information on the atomic and mesoscale dynamics of individual atoms, molecules, and particles on surfaces and in open volumes. Analysis of such data necessitates statistical frameworks to convert observed dynamic behaviors to effective properties of materials. Here, we develop a method for the stochastic reconstruction of effective local potentials solely from observed structural data collected from molecular dynamics simulations (i.e., data analogous to those obtained via atomically resolved microscopies). Using the silicon vacancy defect in graphene as a model, we apply the statistical framework presented herein to reconstruct the free energy landscape from the calculated atomic displacements. Evidence of consistency between the reconstructed local potential and the trajectory data from which it was produced is presented, along with a quantitative assessment of the uncertainty in the inferred parameters.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2026

Measurement of fifth- and sixth-order fluctuations of (net-)proton number in Au + Au collisions from phase II of the beam energy scan program at RHIC

We report high-statistics measurements of fifth- and sixth-order factorial cumulants and cumulant ratios of (net-)proton multiplicity distributions in Au+Au collisions at $\sqrt{s_{NN}}$ = 7.7–27GeV, using data from the STAR experiment collected during the Beam Energy Scan Phase II at RHIC. Protons and antiprotons are identified at midrapidity (|𝑦| < 0.5) with transverse momentum 0.4 < 𝑝 𝑇 < 2.0GeV/𝑐. The proton factorial cumulants 𝜅 4 , 𝜅 5 , and 𝜅 6 increase with order but exhibit no sign alternation within current uncertainties, offering no evidence for a two-component structure in the proton multiplicity distribution, as might be expected near a first-order phase transition. The cumulant ratios 𝐶 5 /𝐶 1 and 𝐶 6 /𝐶 2 fluctuate around zero in collisions at 0–40% centrality. The results are consistent with both the negative predictions from lattice QCD and the positive trends obtained from the ultrarelativistic quantum molecular dynamics (UrQMD) model. At $\sqrt{s_{NN}}$ ≳ 27GeV, the 𝐶 4 /𝐶 2 and 𝐶 5 /𝐶 1 results are compatible with predictions from lattice QCD, functional renormalization group (FRG), and hadron resonance gas (HRG) models, while UrQMD describes the data better at lower energies. Here, these measurements place constraints on baryon number fluctuations and offer valuable insights into the QCD phase structure.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2021

Science Validation for Dark Energy Research with Optical Imaging Surveys

The Universe has been expanding at an accelerating rate over the past several billion years, as though an unknown form of dark energy permeates all of space. The ultimate scientific goal of the proposed research is to distinguish between different physical mechanisms that could account for this observed accelerated expansion, for example, the zero-point energy of the vacuum, a dynamical form of energy that varies in time and/or space, or a modification to our theory of gravity. Wide- field optical imaging surveys of the night sky can test these competing models by measuring both the cosmic expansion history and the growth of large-scale structure. To this end, the Dark Energy Survey (DES) has cataloged several hundred million galaxies and thousands of supernovae. The Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST), will enlarge the census to billions of galaxies and hundreds of thousands of supernovae. A critical question for these dark energy experiments is whether systematic uncertainties can continue to be controlled at a level to keep pace with the statistical precision offered by such enormous datasets. The immediate research objectives of this project were (1) to prepare and validate input datasets that are the foundation of cosmological analyses with DES, and (2) to prepare for value-added characterization of Rubin Observatory commissioning data to inform early operations and accelerate the realization of dark energy science from LSST data products. For DES, we assembled and curated cosmology-ready data releases that include value-added components such as enhanced photometric and astrometric calibrations, alternative source extraction algorithms, maps of the survey coverage and survey conditions, object classifications, object quality selections, galaxy shapes, and photometric redshifts. We used the galaxy clustering technique to validate the photometric redshift distributions of various galaxy samples to be used as lenses in combined studies of galaxy clustering and weak gravitational lensing. The galaxy clustering redshift analysis was enhanced by use of a larger sample of reference galaxies from the eBOSS spectroscopic survey that extends to higher redshifts. For LSST, we prepared for science validation studies of commissioning data aimed at dark energy science capability that extend beyond the normative system-level tests to be done by the Rubin Observatory Construction Project. We identified a set of proposed survey strategies and candidate target fields that could be observed during the commissioning period to enhance science validation activities related to studies of dark energy.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗