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DOE OSTI2023

Non-parametric projections of the net-income distribution for all U.S. states for the Shared Socioeconomic Pathways

Abstract Income distributions are a growing area of interest in the examination of equity impacts brought on by climate change and its responses. Such impacts are especially important at subnational levels, but projections of income distributions at these levels are scarce. Here, we project U.S. state-level income distributions for the Shared Socioeconomic Pathways (SSPs). We apply a non-parametric approach, specifically a recently developed principal components algorithm to generate net income distributions for deciles across 50 U.S. states and the District of Columbia. We produce these projections to 2100 for three SSP scenarios in combination with varying projections of GDP per capita to represent a wide range of possible futures and uncertainties. In the generation of these scenarios, we also generated tax adjusted historical deciles by U.S. states, which we used for validating model performance. Our method thus produces income distributions by decile for each state, reflecting the variability in state income, population, and tax regimes. Our net income projections by decile can be used in both emissions- and impact-related research to understand distributional effects at various income levels and identify economically vulnerable populations.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2024

Soil management practices can contribute to net carbon neutrality in California

Stabilizing climate requires reducing greenhouse gas (GHG) emissions and storing atmospheric carbon dioxide (CO 2 ) in land or ocean systems. Soil management practices can reduce GHG emissions or sequester atmospheric CO 2 into inorganic and organic forms. However, whether soil carbon strategies represent a viable and impactful climate mitigation pathway is uncertain. A specific question concerns the role that land-management practices and soil amendments can play in realizing California's ambition for carbon neutrality by 2045. Here we examine the carbon flux impacts of soil conservation (i.e., compost, reduced tillage, cover crop) and enhanced silicate rock weathering (EW) practices at different areal extents of implementation in cropland, grassland, and savanna in California under two climate change cases. We show that with implementation areas of 15% or 50% of private cultivated land, grassland, and savanna in California, soil conservation practices alone can contribute $1.4^{2.1}_{0.7}$% ($-1.8^{-2.7}_{ -0.9}$ Mt CO 2 eq y -1 ) and $4.6^{6.9}_{2.3}$% ($-6.0^{-8.9}_{-3.0}$ Mt CO 2 eq y -1 ) of the additional emissions reduction needed (beyond previous targets) to meet the 2045 net neutrality goal (-129.3 Mt CO 2 eq y -1 ), respectively, on an average annual basis, including climate uncertainty. Including EW in these scenarios increases the total contributions of management practices to $4.1^{5.6}_{2.5}$% ($-5.2^{-7.3}_{-3.2}$ Mt CO 2 eq y -1 ) and $13.5^{18.6}_{8.2}$% ($-17.5^{-24.2}_{-10.7}$ Mt CO 2 eq y -1 ), respectively, of this reduction. This highlights that the extent of implementation area is a major factor in determining benefits and that EW has the potential to make a real contribution to net reduction targets. Results are similar across climate cases, indicating that contemporary field data can be used to make future projections. With EW there remains mechanistic uncertainties, however, such as rock dissolution rate and environmental controls on weathering products, which require additional field research to improve understanding of the technological efficacy of this approach for California's 2045 carbon neutrality goal.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2025

Precision Measurement of Net-Proton-Number Fluctuations in Au + Au Collisions at RHIC

We report precision measurements on cumulants (𝐶 𝑛 ) and factorial cumulants (𝜅 𝑛 ) of (net) proton number distributions up to fourth order in Au + Au collisions over center-of-mass energies $\sqrt{s_{NN}}$ = 7.7–27 GeV from phase II of the Beam Energy Scan program at RHIC. (Anti)protons are selected at midrapidity (|𝑦| < 0.5) within a transverse momentum range of 0.4 < 𝑝 𝑇 < 2.0 GeV/𝑐. Relative to various noncritical-point model calculations and peripheral collision 70%–80% data, the net proton 𝐶 4 /𝐶 2 measurement in 0%–5% collisions shows a minimum around 19.6 GeV for significance of deviation at ∼2–5⁢𝜎. A minimum in 𝐶 4 /𝐶 2 with respect to a noncritical baseline is expected to be a characteristic feature of the signature associated with a critical point in the QCD phase diagram. In addition, deviations from noncritical baselines around the same collision energy region are also seen in proton factorial cumulant ratios, especially in 𝜅 2 /𝜅 1 and 𝜅 3 /𝜅 1 . As a result, dynamical model calculations including a critical point are called for in order to understand these precision measurements.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2021

Cumulants and correlation functions of net-proton, proton, and antiproton multiplicity distributions in Au+Au collisions at energies available at the BNL Relativistic Heavy Ion Collider

Here, we report a systematic measurement of cumulants, C n , for net-proton, proton, and antiproton multiplicity distributions, and correlation functions, κ n , for proton and antiproton multiplicity distributions up to the fourth order in Au+Au collisions at $\sqrt{s_{NN}}$ = 7.7, 11.5, 14.5, 19.6, 27, 39, 54.4, 62.4, and 200 GeV. The C n and κ n are presented as a function of collision energy, centrality and kinematic acceptance in rapidity, y, and transverse momentum, p T . The data were taken during the first phase of the Beam Energy Scan (BES) program (2010–2017) at the BNL Relativistic Heavy Ion Collider (RHIC) facility. The measurements are carried out at midrapidity (|y| < 0.5) and transverse momentum 0.4 < p T < 2.0 GeV/c, using the STAR detector at RHIC. We observe a nonmonotonic energy dependence ($\sqrt{s_{NN}}$ = 7.7–62.4 GeV) of the net-proton C 4 /C 2 with the significance of 3.1σ for the 0–5% central Au+Au collisions. This is consistent with the expectations of critical fluctuations in a QCD-inspired model. Thermal and transport model calculations show a monotonic variation with $\sqrt{s_{NN}}$. For the multiparticle correlation functions, we observe significant negative values for a two-particle correlation function, κ 2 , of protons and antiprotons, which are mainly due to the effects of baryon number conservation. Furthermore, it is found that the four-particle correlation function, κ 4 , of protons plays a role in determining the energy dependence of proton C 4 /C 1 below 19.6 GeV, which cannot be understood by the effect of baryon number conservation.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2023

Beam Energy Dependence of Fifth- and Sixth-Order Net-Proton Number Fluctuations in Au + Au Collisions at RHIC

We report the beam energy and collision centrality dependence of fifth and sixth order cumulants (C 5 , C 6 ) and factorial cumulants (κ 5 , κ 6 ) of net-proton and proton distributions, from $\sqrt{s_{NN}}$=3-200 GeV Au+Au collisions at RHIC. The net-proton cumulant ratios generally follow the hierarchy expected from QCD thermodynamics, except for the case of collisions at $\sqrt{s_{NN}}$ = 3 GeV. C 6 /C 2 for 0-40\% centrality collisions is increasingly negative with decreasing $\sqrt{s_{NN}}$, while it is positive for the lowest $\sqrt{s_{NN}}$ studied. These observed negative signs are consistent with QCD calculations (at baryon chemical potential, μ B ≤ 110 MeV) that include a crossover quark-hadron transition. In addition, for $\sqrt{s_{NN}}$≥ 11.5 GeV, the measured proton κ n , within uncertainties, does not support the two-component shape of proton distributions that would be expected from a first-order phase transition. Taken in combination, the hyper-order proton number fluctuations suggest that the structure of QCD matter at high baryon density, μ B ~750 MeV ($\sqrt{s_{NN}}$ = 3 GeV) is starkly different from those at vanishing μ B ~20MeV ($\sqrt{s_{NN}}$ = 200 GeV and higher).

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2025

String membrane nets from higher-form gauging: An alternate route to 𝑝-string condensation

We present a new perspective on the $p$-string condensation procedure for constructing 3+1D fracton phases by implementing this process via the gauging of higher-form symmetries. Specifically, we show that gauging a 1-form symmetry in 3+1D that is generated by Abelian anyons in isotropic stacks of 2+1D topological orders naturally results in a 3+1D $p$-string condensed phase, providing a controlled non-perturbative construction that realizes fracton orders. This approach clarifies the symmetry principles underlying $p$-string condensation and generalizes the familiar connection between anyon condensation and one-form gauging in two spatial dimensions. We demonstrate this correspondence explicitly in both field theories and lattice models: in field theory, we derive the foliated field theory description of the $\mathbb{Z}_N$ X-Cube model by gauging a higher-form symmetry in stacks of 2+1D $\mathbb{Z}_N$ gauge theories; on the lattice, we show how gauging a diagonal 1-form symmetry in isotropic stacks of $G$-graded string-net models leads to string-membrane-nets hosting restricted mobility excitations. This perspective naturally generalizes to spatial dimensions $d \geq 2$ and provides a step towards building an algebraic theory of $p$-string condensation.

Anyons↗

DOE OSTI2024

Oxygen Storage Incorporated Into Net Power and the Allam–Fetvedt Oxy-Fuel sCO2 Power Cycle—Techno-Economic Analysis

Abstract With the planned future reliance on variable renewable energy, the ability to store energy for prolonged time periods will be required to reduce the disruption of market fluctuations. This paper presents a method to analyze a hybrid liquid-oxygen (LOx) storage/direct-fired supercritical carbon dioxide (sCO2) power cycle and optimize the economic performance over a diverse range of scenarios. The system utilizes a modified version of the NET Power process to produce energy when energy demand exceeds the supply while displacing much of the cost of the air separation unit (ASU) energy requirements through cryogenic storage of oxygen. The model uses marginal cost of energy data to determine the optimal times to charge and discharge the system over a given scenario. The model then applies ramp rates and other time-dependent factors to generate an economic model for the system without storage considerations. The size of the storage system is then applied to create a realistic model of the plant operation. From the real plant operation model, the amount of energy charged and discharged, the capital expenditures (CAPEX) of each system, energy costs and revenue and other parameters can be calculated. The economic parameters are then combined to calculate the net present value (NPV) of the system for the given scenario. The model was then run through the SMPSO genetic algorithm in Python for a variety of geographic regions and large-scale scenarios (high solar penetration) to maximize the NPV based on multiple parameters for each subsystem. The LOx storage requirements will also be discussed.

Engineering↗

DOE OSTI2020

JEDI-net: a jet identification algorithm based on interaction networks

Abstract We investigate the performance of a jet identification algorithm based on interaction networks (JEDI-net) to identify all-hadronic decays of high-momentum heavy particles produced at the LHC and distinguish them from ordinary jets originating from the hadronization of quarks and gluons. The jet dynamics are described as a set of one-to-one interactions between the jet constituents. Based on a representation learned from these interactions, the jet is associated to one of the considered categories. Unlike other architectures, the JEDI-net models achieve their performance without special handling of the sparse input jet representation, extensive pre-processing, particle ordering, or specific assumptions regarding the underlying detector geometry. The presented models give better results with less model parameters, offering interesting prospects for LHC applications.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2025

Multiplicity and net-electric charge fluctuations in central Ar+Sc interactions at 13 A , 19 A , 30 A , 40 A , 75 A , and 150 A GeV/c beam momenta measured by NA61/SHINE at the CERN SPS

This paper presents results on multiplicity fluctuations of positively and negatively charged hadrons as well as net-electric charge fluctuations measured in central Ar+Sc interactions at beam momenta 13 A , 19 A , 30 A , 40 A , 75 A , and 150 A GeV/c. The fluctuation analysis is one of the tools to search for the predicted critical point of strongly interacting matter. Results are corrected for the experimental biases and quantified using cumulant ratios. In most instances, multiplicity and net-charge distributions appear narrower than the corresponding Poisson or Skellam distributions. Cumulant ratios are compared with the EPOS1.99 model predictions, which provide a qualitative description that aligns with observations for positively and negatively charged particles. The obtained results are also compared to earlier NA61/SHINE results from inelastic p+p interactions in the same analysis acceptance.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2024

UFNet: Joint U-Net and Fully Connected Neural Network to Bias Correct Precipitation Predictions from

Paper information. Shuang Yu, Indrasis Chakraborty, Gemma J. Anderson, Donald D. Lucas, Yannic Lops, and Daniel Galea. UFNet: Joint U-Net and fully connected neural network to bias correct precipitation predictions from climate models. Artificial Intelligence for the Earth Systems, 2024. Overview. This work develops the UFNet methodology to correct E3SM historical precipitation projection bias. The UFNet deep learning framework consists of a two-part architecture: a U-Net convolutional network to capture the spatiotemporal distribution of precipitation and a fully connected network to capture the distribution of higher-order statistics. The joint network, termed UFNet, can simultaneously improve the spatial structure of the modeled precipitation and capture the distribution of extreme precipitation values. Below we provide guidance for applying UFNet to correct the Energy Exascale Earth System Model (E3SM; Golaz et al. 2019) daily precipitation projection over the contiguous United States (CONUS). Getting started 1. Obtain the historical climate simulation and observation data. The E3SM historical simulation data are available through https://aims2.llnl.gov/search/cmip6/. The CPC unified gauge-based analysis of daily precipitation can be found through https://psl.noaa.gov/data/gridded/data.cpc.globalprecip.html. The ECMWF atmospheric reanalysis of the 20th century (ERA-20C) data are available through https://www.ecmwf.int/en/forecasts/datasets/reanalysis-datasets/era-20c. The spatial resolution of E3SM and observed datasets are both regridded to a common 1° resolution grid using conservative interpolation. The regridded E3SM, CPC and ERA-20C with 1° resolution can be found throught ./data/. 2. Train the fully connected network (DNN) Python train_dnn.py 3. Train the UFNet Python train_ufnet.py 4. Evaluation and compared with the baseline Python evaluation.py

Lucas, DonaldD↗

DOE OSTI2025

QUANT-NET Control Plane Framework (QNCP) v1.0.0

The QUANT-NET Control Plane (QNCP) provides a software framework for expressing and managing quantum network resources. It may be used to orchestrate a physical quantum testbed with real device driver implementations, or it may be used as a proving ground when developing new protocols and management functions. In practice, both approaches may be useful when undertaking research and development in emerging quantum testbeds. While a number of control systems have been developed for specific quantum platform demonstrations, an openly available and general solution for operating quantum networks has not emerged. QNCP is designed to fill this gap. The framework has been designed to provide extensible, modular capabilities that include scheduling, routing, monitoring, and pluggable protocols. A number of reference implementations in each module category have been included in the installable packages; however, the intent is that each of these modules may be extended or re-implemented to meet the needs of the particular deployment or research need. The software is currently being used in the QUANT-NET testbed project, which spans resources between LBNL and UC Berkeley Physics.

Zhang, Liang [Lawrence Berkeley National Laborator↗

DOE OSTI2026

CHM-MS-net-Canopy-Height-Model

The CHM-MS-net Canopy Height Model is a MS-net neural network applied to high resolution satellite imagery to estimate tree height, tree location, and crown radius.

Atchley, Adam↗

DOE OSTI2026

SPUS-Small-PDE-U-net-Solver

Small PDE U-Net Solver (SPUS) is a compact and efficient foundation model (FM) designed as a unified neural operator for solving a wide range of partial differentialequations (PDEs). SPUS leverages a lightweight residual U-Net-based architecture as a foundation model architecture. To enable effective learning in this minimalist framework, SPUS utilizes a simple yet powerful auto-regressive pretraining strategy which closely replicates the behavior of numerical solvers to learn the underlying physics. SPUS is designed to be pretrained on a diverse set of fluid dynamics PDEs from public benchmark datasets.

Siddik, Abu↗

DOE OSTI2022

Multi-head attention-based U-Nets for predicting protein domain boundaries using 1D sequence features and 2D distance maps

Abstract The information about the domain architecture of proteins is useful for studying protein structure and function. However, accurate prediction of protein domain boundaries (i.e., sequence regions separating two domains) from sequence remains a significant challenge. In this work, we develop a deep learning method based on multi-head U-Nets (called DistDom) to predict protein domain boundaries utilizing 1D sequence features and predicted 2D inter-residue distance map as input. The 1D features contain the evolutionary and physicochemical information of protein sequences, whereas the 2D distance map includes the structural information of proteins that was rarely used in domain boundary prediction before. The 1D and 2D features are processed by the 1D and 2D U-Nets respectively to generate hidden features. The hidden features are then used by the multi-head attention to predict the probability of each residue of a protein being in a domain boundary, leveraging both local and global information in the features. The residue-level domain boundary predictions can be used to classify proteins as single-domain or multi-domain proteins. It classifies the CASP14 single-domain and multi-domain targets at the accuracy of 75.9%, 13.28% more accurate than the state-of-the-art method. Tested on the CASP14 multi-domain protein targets with expert annotated domain boundaries, the average per-target F1 measure score of the domain boundary prediction by DistDom is 0.263, 29.56% higher than the state-of-the-art method.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2022

Hybrid Capture for Net-Negative Emissions

The Energy & Environmental Research Center, in partnership with the U.S. Department of Energy and North Dakota ethanol producer Red Trail Energy, LLC (RTE), completed an initial engineering design (IED) for a potential hybrid capture system at the RTE CCS (carbon capture and storage) site. The RTE CCS Project is currently operating a CO2 capture facility, adjacent to the RTE ethanol facility in western North Dakota, and injecting the CO2 more than a mile below RTE property for permanent storage. This novel process would add capturing CO2 produced from natural gas boilers to the existing bioprocessing capture system to achieve a net-negative CO2 emissions industrial process at commercial scale (~310,000 tonnes/yr CO2). Initial findings of the IED showed the hybrid system to be technically viable with a moderate estimated cost of $55/tonne CO2 captured for the hybrid system. A cradle-to-gate life cycle assessment (LCA) was also conducted, showing preliminary net-negative carbon emissions potential anticipated from implementing a hybrid CCS system at a commercial scale. Recommended next steps toward potential implementation include hybrid capture system demonstration testing for detailed engineering and LCA model comparisons with low-carbon fuel incentive programs for financial support.

01 COAL, LIGNITE, AND PEAT↗

DOE OSTI2020

Tracer gas test in Net-Zero Energy Residential Test Facility

The Net-Zero Energy Residential Test Facility (NZERTF) at NIST was built in 2012 to support the development and adoption of cost-effective net-zero energy designs, technologies, and construction methods. It is two-story, and has four bedrooms, three bathrooms, and an open living room, dining room, and kitchen space. The NZERTF also has a basement and attic, both located within the conditioned space because the thermal and air-moisture barriers encompass the basement walls and attic roof. Transfer grilles link the living spaces to these two zones. The central heating and cooling system includes an air-toair heat pump, which delivers air to the basement, first and second floors. The heat pump has a cooling capacity of 7.6 kW and a heating capacity of 7.8 kW. The indoor unit is in the basement and ductwork runs along the basement ceiling. A balanced heat recovery ventilator (HRV) is installed in the basement and has its own dedicated ductwork. The HRV supplies 47 L/s of outdoor air to the house, with supplies on the first floor (in the kitchen/dining area) and in each of the three second-floor bedrooms. Air from the first-floor bathroom and both second-floor bathrooms is returned to the HRV before being exhausted, though the HRV was turned off during the tests. The house also has a range hood exhaust and a clothes dryer exhaust, both of which were turned off for the tests. The temperature of the house was measured and the operation of the space conditioning system, including its recirculating air distribution fan, were controlled by a commercially available thermostat.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2022

The Net Zero World Initiative’s Preliminary Analysis of Decarbonization Pathways for Five Countries. Executive Summary

Under the Net Zero World Initiative, the United States is marshalling the forces of nine U.S. government agencies, led by the U.S. Department of Energy, to partner with philanthropies and multiple countries to cocreate and implement tailored technical and investment pathways to accelerate the decarbonization of global energy systems. In addition, 10 of the DOE national laboratories have built a consortium housed in the Net Zero World Action Center to implement this vision by providing the deep analysis and modeling required to carry out the vision.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2023

BTRIC Technical Support for Appalachia: FY 2023 Summary Report on Net Negative Carbon Building Demonstration with AgPod

This study investigates the effects of various concentrations of carbon dioxide (CO 2 ) on the photosynthesis and biomass accumulation of crops growing in an Agricultural Pod (AgPod). The initial test crop was kale. It has been found that increasing the CO 2 concentration in the AgPod promotes higher photosynthetic rates in kale plants up to 1300 ppm (highest CO 2 concentration). An interesting finding was that net photosynthetic rates of kale leaves remain unsaturated until unusually high levels of photosynthetically active radiation (PAR, > 1200 µmolm-2s-1) and CO 2 concentration (> 1100 ppm) are reached. Such high levels of saturating PAR and CO 2 concentration values rarely occur under natural conditions, suggesting a promising potential for carbon capture via enriched greenhouse crop production. This task is one part of an overall plan to incorporate an AgPod to utilize carbon captured from the atmosphere to achieve net negative carbon emissions in a small neighborhood or community.

54 ENVIRONMENTAL SCIENCES↗