Search NASA⌕ Search

SEARCH · Search NASA

Results for “CAPTURE EFFECT”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 145 records · Page 8
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Identifying Vehicle Signals in Continuous Seismic Data Using Unsupervised Machine-Learning Techniques

Seismic sensors deployed near roadways effectively capture ground vibrations generated by passing vehicles. Although both traditional and machine‐learning algorithms have been utilized for analyzing such signals, independent validation of detected vehicle events remains limited. We applied two unsupervised machine‐learning algorithms, uniform manifold approximation and projection for dimension reduction, and hierarchical density‐based spatial clustering of applications with noise, to continuous seismic data collected along a road on the main campus of Oak Ridge National Laboratory. The algorithms identified seven distinct cluster labels across the entire dataset. By comparing these cluster labels with precipitation records from a nearby weather station and image‐derived labels from a local camera system, we identified one cluster associated with rainfall and another with vehicle activity. Our algorithms identified a greater number of vehicle‐related labels compared to the camera‐derived labels because seismic data are unaffected by poor lighting conditions. The arrival times of the newly detected vehicle signals corresponded well with the road’s speed limit, supporting our findings. Our algorithm outperformed the short‐term average/long‐term average method and k‐means clustering. Our results suggest that seismic data, when analyzed with machine‐learning algorithms, can complement existing vehicle monitoring systems, particularly under challenging environmental conditions.

Chai, Chengping [Oak Ridge National Laboratory (OR↗
DOE OSTI2020

An energy-based coupling approach to nonlocal interface problems

Nonlocal models provide accurate representations of physical phenomena ranging from fracture mechanics to complex subsurface flows, where traditional partial differential equations fail to capture effects caused by long-range forces at the microscale and mesoscale. However, the application of nonlocal models to problems involving interfaces such as multimaterial simulations and fluid-structure interaction, is hampered by the lack of a rigorous nonlocal interface theory needed to support numerical developments. In this paper, we use an energy-based approach to develop a mathematically rigorous nonlocal interface theory which provides a physically consistent extension of the classical perfect interface PDE formulation. Numerical examples validate the proposed framework and demonstrate the scope of our theory.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2020

A Unified Theory of Fractional Nonlocal and Weighted Nonlocal Vector Calculus

Nonlocal and fractional-order models capture effects that classical partial differential equations cannot describe; for this reason, they are suitable for a broad class of engineering and scientific applications that feature multiscale or anomalous behavior. This has driven a desire for a vector calculus that includes nonlocal and fractional gradient, divergence and Laplacian type operators, as well as tools such as Green's identities, to model subsurface transport, turbulence, and conservation laws. In the literature, several independent definitions and theories of nonlocal and fractional vector calculus have been put forward. Some have been studied rigorously and in depth, while others have been introduced ad-hoc for specific applications. The goal of this work is to provide foundations for a unified vector calculus by (1) consolidating fractional vector calculus as a special case of nonlocal vector calculus, (2) relating unweighted and weighted Laplacian operators by introducing an equivalence kernel, and (3) proving a form of Green's identity to unify the corresponding variational frameworks for the resulting nonlocal volume-constrained problems. The proposed framework goes beyond the analysis of nonlocal equations by supporting new model discovery, establishing theory and interpretation for a broad class of operators, and providing useful analogues of standard tools from the classical vector calculus.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

An energy-based coupling approach to nonlocal interface problems.

Nonlocal models provide accurate representations of physical phenomena ranging from fracture mechanics to complex subsurface flows, settings in which traditional partial differential equation models fail to capture effects caused by long-range forces at the microscale and mesoscale. However, the application of nonlocal models to problems involving interfaces, such as multimaterial simulations and fluid-structure interaction, is hampered by the lack of a physically consistent interface theory which is needed to support numerical developments and, among other features, reduces to classical models in the limit as the extent of nonlocal interactions vanish. In this paper, we use an energy-based approach to develop a formulation of a nonlocal interface problem which provides a physically consistent extension of the classical perfect interface formulation for partial differential equations. Numerical examples in one and two dimensions validate the proposed framework and demonstrate the scope of our theory.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2022

Mathematical Foundations for Nonlocal Interface Problems: Multiscale Simulations of Heterogeneous Materials (Final LDRD Report)

Nonlocal models provide a much-needed predictive capability for important Sandia mission applications, ranging from fracture mechanics for nuclear components to subsurface flow for nuclear waste disposal, where traditional partial differential equations (PDEs) models fail to capture effects due to long-range forces at the microscale and mesoscale. However, utilization of this capability is seriously compromised by the lack of a rigorous nonlocal interface theory, required for both application and efficient solution of nonlocal models. To unlock the full potential of nonlocal modeling we developed a mathematically rigorous and physically consistent interface theory and demonstrate its scope in mission-relevant exemplar problems.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Life cycle of carbon in macroalgae for various products

This paper seeks to understand the life cycle and permanence of carbon sequestration for the many possible products of offshore cultivated macroalgae, compared to natural growth, habitat restoration, and intentional sinking. The paper will systematically review existing life cycle analyses (LCAs) for various macroalgae products to identify information gaps and compare the carbon sequestration potential throughout each product life cycle. The sequestration potential of macroalgae is well documented (e.g. Chung et al., 2011; Krause-Jensen & Duarte, 2016) but the permanence of the capture is not understood for harvested macroalgae or end products, which may or may not release the stored carbon dioxide in processing or consumption. This information is necessary to avoid overestimating the benefit and impacts of federal investment in large-scale seaweed aquaculture. The goal of this report is to: 1) Review published LCAs for various macroalgae products and uses; 2) Identify knowledge gaps (experiments and monitoring) to understand the flow of carbon on various time scales; 3) Develop preliminary ranking of macroalgae products by carbon capture effectiveness and permanence.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Talent Acquisition in Scientific and Engineering Disciplines: A Case Study of National Laboratory (TASED) [Thesis]

Identifying the hindrances to performing effective talent acquisition within the science, technology, engineering, and mathematics field is an important topic for technical hiring managers. Top candidates have multiple options during highly competitive market conditions requiring managers to look for unique solutions which diverge from competition. Prior to this study there has been very little research considering national laboratory research and development challenges from a technical hiring manager’s talent acquisition perspective. Utilizing a unique combination of national laboratory multi-organization survey, pilot study, Human Resource (HR) tracking data and trust based business strategy to enhance partnering this research finds hiring managers can leverage out of the box techniques to improve internal processes while developing industry support to target highly qualified individuals. This methodology could be utilized by technical hiring managers across federal national laboratory enterprise to effectively capture next generation staff and leadership talent who align with their organization professionally as well as social culture.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2023

Small Reactors in Microgrids: Technology Modeling and Selection (Net-Zero Microgrid Program Project Report)

This report demonstrates the capabilities of the net-zero microgrid (NZM) Xendee platform for modeling an SR module with electricity, heat extraction and thermal storage in microgrids configurations. The model effectively captures the most important technical and economic considerations for SR technology specific analysis: cost and operational characteristics of SR technology and financial costs and incentives. The model can analyze multiple scenarios to establish metrics for cost-competitive and zero-carbon microgrids connected to the grid or completely isolated. The model is fully integrated within the Xendee platform for modeling and analysis of clean energy microgrids with storage and generation from renewable energy sources. The model captures the capabilities, constraints, and nuances of SR by incorporating parameters related to plant economics, design efficiency and performance, plant operation and component and fuel lifespan. The cost and operational parameters modeled in the SR module are specific to the technology selected for integration in the microgrid. Cost parameters recognize advanced nuclear technology for modular production and installation based on economies of scale from factory manufacture and related commissioning, and cost reduction through technology maturation—first-of-a-kind (FOAK) and nth-of-a-Kind (NOAK). The cost parameters include installation, operations and maintenance (O&M), fuel refueling cycle, and reactor life. Installation cost reflects economies of scale due to unit sizing at scale and colocation. O&M economies of scale for both fixed- and variable-cost fuel life-cycle costs are incurred at every refueling interval, with separate front- and back-end fuel costs, as well as waste-handling and disposition costs. This report investigates key characteristics of different SR technologies suitable for microgrid applications, including design principles, sizing, coolant properties, temperature ratings, fuel structures, and life-cycle considerations. This also includes fuel technologies applicable to these SR systems, alongside strategies for nuclear-waste and spent-fuel management and approaches to address safety, security, and proliferation challenges. Four primary groups of SR technologies are examined: water-cooled, liquid-metal-cooled, high-temperature gas-cooled, and molten-salt-cooled systems. In this report, an initial guideline for technology selection is established, aligning the characteristics of the technologies with the requirements of microgrids. The selection of technology in a microgrid is influenced by various factors, including financial capacity, location and accessibility, demand type and characteristics, reliability and resilience requirements, area constraints, and the lifespan of the microgrid. The types of electrical and non-electrical applications within the microgrid also play a significant role in technology selection. The characteristics of SRs, such as their smaller size, modularity, transportability, long refueling interval, improved safety features, ability to operate in autonomous or semi-autonomous mode, and provision of high-grade heat, are particularly appealing for microgrids. Furthermore, a list of considerations for implementing SRs in microgrids is outlined. The SR model is created to be continuously improved with the acquisition of actual data on investment and operational costs, experience with supply chains, production at scale, and field deployments. In the near term, performance data on applications in microgrids will become available from lessons learned from laboratory tests, such as those planned for the Microreactor Applications Research Validation and Evaluation Project (MARVEL), led by Idaho National Laboratory (INL). The SR model incorporates scenario data and known SR design specifications, enabling technoeconomic analysis for SR deployment in microgrids. It specifically considers the distinctive attributes of SRs as generators in technoeconomic studies. SRs can be modeled and analyzed with generation from renewable-energy sources, energy storage, and flexible loads over a range of functionality and applications. This offers a comprehensive tool for feasibility studies, scenario development, and sensitivity analysis for “what-if” consideration of any range of assumptions about SRs in microgrids and other aggregations of distributed-energy resources, including virtual power plants.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2024

Overview of Collaborative Research Between UNICAMP in Brazil and Fermilab in Cryogenics

The Long-Baseline Neutrino Facility (LBNF) situated at the Sanford Underground Research Facility (SURF) in Lead, South Dakota, serves as the host for the Deep Underground Neutrino Experiment (DUNE), employing cryostats with nearly 70,000 metric tons of high purity liquid argon (LAr). The integrity of LAr quality is pivotal in determining the electron lifetime within DUNE, directly impacting its signal-to-noise ratio. Specifically, Far Detector 1 (FD-1) in cryostat 1 requires an electron lifetime over 3 ms within its 3.5 m drift, corresponding to less than 100 parts-per-trillion (ppt) Oxygen equivalent contamination. Far Detector 2 (FD-2) in cryostat 2 demands over 6 ms electron lifetime within its 6.0 m drift, corresponding to less than 50 ppt Oxygen equivalent contamination. Nitrogen (N2) absorption of LAr scintillation light, known as quenching, necessitates N2 contamination in LAr to remain below 1 ppm to minimize photon loss and enhance energy reconstruction. Studies indicate that at 1 ppm N2, approximately 20% of scintillation light is lost, highlighting the importance of minimizing N2 contamination. Brazil State University of Campinas's (UNICAMP) contribution to LBNF focuses on developing argon purification and regeneration for DUNE FD-1 and FD-2. To that effect, they constructed a test facility to perform studies on LAr purification at a smaller scale, the Purification Liquid Argon Cryostat (PuLArC) with approximately 90 liters of LAr. One of the filtration materials was considered and tested Li-FAU molecular sieve. Value engineering on argon purification media was conducted, leading to the identification of Li-FAU zeolite's ability to effectively capture N2 impurities during LAr circulation. Testing at UNICAMP's PuLArC facility demonstrated that 1 kg of Li-FAU is capable of reducing N2 contamination from 20-50 ppm to 0.1-1.0 ppm within 1-2 hours of circulation. In October 2023, testing at the Iceberg cryostat in Fermilab's Noble Liquid Test Facility (NLTF), with approximately 2,625 liters of LAr, confirmed the efficacy of 3 kg of Li-FAU in reducing N2 contamination from ~ 5 ppm of injected N2 down to less than 1 ppm over 96-hour cycles, showcasing its potential for larger-scale LAr cryostats. Further tests are planned to validate Li-FAU's use as a possible alternative to Molecular Sieve 4A in LBNF-DUNE and related liquid argon experiments. This contribution will describe how the research was performed and present the test setups and results in detail. This advancement not only has the potential to enhance DUNE's precision but also to elevate liquid argon experiments globally, showcasing the power of international scientific collaboration.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2025

A Semi-Detailed Pyrolytic Gas-Phase Kinetic Model for the Volatiles of Polyethylene Thermal Degradation

This work presents a semi-detailed kinetic model to address the pyrolytic gas-phase reactivity of volatiles formed during thermal degradation of polyethylene (PE). The model builds on a validated multi-step condensed-phase model and employs validated lumping approaches. Short-chain compounds are modelled with high detail, while long-chain ones are described by surrogate species representative of diesel-cuts (NC16H32) and waxes (NC30H60). The reactivity of short chains is described through the comprehensive CRECK kinetic model, updated to align C5-C7 olefins based on recent literature experimental data. Due to the lack of experimental data for longer olefins, their reactivity is modeled by analogy to the shorter ones, ensuring an asymptotic behavior with increasing carbon numbers. The semi-detailed model is validated through experimental data on PE pyrolysis, assuming an instantaneous mixing of the inert inlet flow with released volatiles, followed by a segregated plug-flow behavior. Validation across different reactor setups confirms the model’s capability to predict detailed product distributions. Despite minor discrepancies, the proposed model effectively captures experimental trends. Further work will address modelling the reactivity in oxygen-containing environments.

kinetics↗
DOE OSTI2023

Evaluation of Multi-Fidelity Soil Moisture Products Across the Continental United States

We have aggregated the most recent soil moisture datasets from a diverse range of sources, encompassing the Continental United States (CONUS). These sources encompass gridded data from remote sensing products, reanalysis products, machine learning-based projects, and land surface modeling products. Additionally, we have obtained and processed in-situ soil moisture observations from the International Soil Moisture Network. The collected datasets exhibit variations in both temporal and spatial resolutions. Among the 20 datasets, six are available at a spatial resolution of 0.25 degrees, while three are at a coarser spatial resolution of 25 km. To minimize spatial interpolation, we conducted data uncertainty evaluations at the 0.25-degree spatial resolution. For our data evaluations, we maintained a monthly temporal resolution, which effectively captures soil moisture seasonality and interannual variability. Our data processing strategy preserves the raw data and interpolated data at their original temporal resolutions. Datasets with higher temporal resolutions, including daily, three-hourly, and hourly datasets, are set aside for subsequent analyses. These analyses will delve into topics such as soil moisture changes and recovery during extreme weather events. Furthermore, we have processed auxiliary data to enhance our evaluation, leveraging tools such as Google Earth Engine. This includes incorporating topography data, land use land cover data, Köppen-Geiger climate classification, and more to provide a comprehensive assessment from multiple sources.

Li, Lingcheng↗
DOE OSTI2022

Photonic Luminescent Solar Concentrator Design for High Efficiency, Low Cost Multijunction Photovoltaics

Despite the extraordinary advances in solar cell efficiency in laboratory settings, the deployment of solar cells continues to be limited to low efficiency (<25%) silicon cells because of cost. In this work, we take advantage of the extraordinary optical properties afforded by nanophotonic structures to create a photonic luminescent solar concentrator for an InGaP-Si multijunction concentrator cell. Finite difference time domain (FDTD) simulations demonstrated a concentrator that could effectively capture, downconvert, and guide concentrated light to an InGaP subcell while still transmitting longer wavelengths to a Si subcell. We fabricated the photonic luminescent solar concentrator, which was comprised of CdSe/CdS quantum dots embedded within alternating layers of Si 3 N 4 and SiO 2 , and experimentally verified the optical performance, showing a 40% increase in light guiding and a significant reduction in reabsorption losses in the plane of the luminescent concentrator as compared to traditional designs. Finally, we utilized modified detailed balance calculations that accounted for cell and optical losses and showed >30% efficiencies are possible with this design, demonstrating the potential to meet the demands for high efficiency, inexpensive solar modules.

Eisler, Carissa N.↗
DOE OSTI2024

STFM: Accurate Spatio-Temporal Fusion Model for Weather Forecasting

Meteorological prediction is crucial for various sectors, including agriculture, navigation, daily life, disaster prevention, and scientific research. However, traditional numerical weather prediction (NWP) models are constrained by their high computational resource requirements, while the accuracy of deep learning models remains suboptimal. In response to these challenges, we propose a novel deep learning-based model, the Spatiotemporal Fusion Model (STFM), designed to enhance the accuracy of meteorological predictions. Our model leverages Fifth-Generation ECMWF Reanalysis (ERA5) data and introduces two key components: a spatiotemporal encoder module and a spatiotemporal fusion module. The spatiotemporal encoder integrates the strengths of convolutional neural networks (CNNs) and recurrent neural networks (RNNs), effectively capturing both spatial and temporal dependencies. Meanwhile, the spatiotemporal fusion module employs a dual attention mechanism, decomposing spatial attention into global static attention and channel dynamic attention. This approach ensures comprehensive extraction of spatial features from meteorological data. The combination of these modules significantly improves prediction performance. Experimental results demonstrate that STFM excels in extracting spatiotemporal features from reanalysis data, yielding predictions that closely align with observed values. In comparative studies, STFM outperformed other models, achieving a 7% improvement in ground and high-altitude temperature predictions, a 5% enhancement in the prediction of the u/v components of 10 m wind speed, and an increase in the accuracy of potential height and relative humidity predictions by 3% and 1%, respectively. This enhanced performance highlights STFM’s potential to advance the accuracy and reliability of meteorological forecasting.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Privacy-Preserving Real-Time Action Detection in Intelligent Vehicles Using Federated Learning-Based Temporal Recurrent Network

This study introduces a privacy-preserving approach for the real-time action detection in intelligent vehicles using a federated learning (FL)-based temporal recurrent network (TRN). This approach enables edge devices to independently train models, enhancing data privacy and scalability by eliminating central data consolidation. Our FL-based TRN effectively captures temporal dependencies, anticipating future actions with high precision. Extensive testing on the Honda HDD and TVSeries datasets demonstrated robust performance in centralized and decentralized settings, with competitive mean average precision (mAP) scores. The experimental results highlighted that our FL-based TRN achieved an mAP of 40.0% in decentralized settings, closely matching the 40.1% in centralized configurations. Notably, the model excelled in detecting complex driving maneuvers, with mAPs of 80.7% for intersection passing and 78.1% for right turns. These outcomes affirm the model’s accuracy in action localization and identification. The system showed significant scalability and adaptability, maintaining robust performance across increased client device counts. The integration of a temporal decoder enabled predictions of future actions up to 2 s ahead, enhancing the responsiveness. Our research advances intelligent vehicle technology, promoting safety and efficiency while maintaining strict privacy standards.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2025

PowerModel-AI: A First On-the-Fly Machine-Learning Predictor for AC Power Flow Solutions

The real-time creation of machine-learning models via active or on-the-fly learning has attracted considerable interest across various scientific and engineering disciplines. These algorithms enable machines to build models autonomously while remaining operational. Through a series of query strategies, the machine can evaluate whether newly encountered data fall outside the scope of the existing training set. In this study, we introduce PowerModel-AI, an end-to-end machine learning software designed to accurately predict AC power flow solutions. We present detailed justifications for our model design choices and demonstrate that selecting the right input features effectively captures load flow decoupling inherent in power flow equations. Our approach incorporates on-the-fly learning, where power flow calculations are initiated only when the machine detects a need to improve the dataset in regions where the model’s suboptimal performance is based on specific criteria. Otherwise, the existing model is used for power flow predictions. This study includes analyses of five Texas A&M synthetic power grid cases, encompassing the 14-, 30-, 37-, 200-, and 500-bus systems. The training and test datasets were generated using PowerModels.jl, an open-source power flow solver/optimizer developed at Los Alamos National Laboratory, NM, USA.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

Time Series Foundation Models and Deep Learning Architectures for Earthquake Temporal and Spatial Nowcasting

Advancing the capabilities of earthquake nowcasting, the real-time forecasting of seismic activities, remains crucial for reducing casualties. This multifaceted challenge has recently gained attention within the deep learning domain, facilitated by the availability of extensive earthquake datasets. Despite significant advancements, the existing literature on earthquake nowcasting lacks comprehensive evaluations of pre-trained foundation models and modern deep learning architectures; each focuses on a different aspect of data, such as spatial relationships, temporal patterns, and multi-scale dependencies. This paper addresses the mentioned gap by analyzing different architectures and introducing two innovative approaches called Multi Foundation Quake and GNNCoder. We formulate earthquake nowcasting as a time series forecasting problem for the next 14 days within 0.1-degree spatial bins in Southern California. Earthquake time series are generated using the logarithm energy released by quakes, spanning 1986 to 2024. Our comprehensive evaluations demonstrate that our introduced models outperform other custom architectures by effectively capturing temporal-spatial relationships inherent in seismic data. The performance of existing foundation models varies significantly based on the pre-training datasets, emphasizing the need for careful dataset selection. However, we introduce a novel method, Multi Foundation Quake, that achieves the best overall performance by combining a bespoke pattern with Foundation model results handled as auxiliary streams.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Best estimate of the planetary boundary layer height from multiple remote sensing measurements

Remote sensing measurements have been widely used to estimate the planetary boundary layer height (PBLHT). Each remote sensing approach offers unique strengths and faces different limitations. In this study, we use machine learning (ML) methods to produce a best-estimate PBLHT (PBLHT-BE-ML) by integrating four PBLHT estimates derived from remote sensing measurements at the Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) Southern Great Plains (SGP) observatory. Three ML models – random forest (RF) classifier, RF regressor, and light gradient-boosting machine (LightGBM) – were trained on a dataset from 2017 to 2023 that included radiosonde, various remote sensing PBLHT estimates, and atmospheric meteorological conditions. Evaluations indicated that PBLHT-BE-ML from all three models improved alignment with the PBLHT derived from radiosonde data (PBLHT-SONDE), with LightGBM demonstrating the highest accuracy under both stable and unstable boundary layer conditions. Feature analysis revealed that the most influential input features at the SGP site were the PBLHT estimates derived from (a) potential temperature profiles retrieved using Raman lidar (RL) and atmospheric emitted radiance interferometer (AERI) measurements (PBLHT-THERMO), (b) vertical velocity variance profiles from Doppler lidar (PBLHT-DL), and (c) aerosol backscatter profiles from micropulse lidar (PBLHT-MPL). The trained models were then used to predict PBLHT-BE-ML at a temporal resolution of 10 min, effectively capturing the diurnal evolution of PBLHT and its significant seasonal variations, with the largest diurnal variation observed over summer at the SGP site. We applied these trained models to data from the ARM Eastern Pacific Cloud Aerosol Precipitation Experiment (EPCAPE) field campaign (EPC), where the PBLHT-BE-ML, particularly with the LightGBM model, demonstrated improved accuracy against PBLHT-SONDE. Analyses of model performance at both the SGP and EPC sites suggest that expanding the training dataset to include various surface types, such as ocean and ice-covered areas, could further enhance ML model performance for PBLHT estimation across varied geographic regions.

Zhang, Damao [Pacific Northwest National Laborator↗
DOE OSTI2021

Dark Energy Survey Year 3 Results: Constraints on cosmological parameters and galaxy bias models from galaxy clustering and galaxy-galaxy lensing using the redMaGiC sample

We constrain cosmological parameters and galaxy-bias parameters using the combination of galaxy clustering and galaxy-galaxy lensing measurements from the Dark Energy Survey Year-3 data. We describe our modeling framework and choice of scales analyzed, validating their robustness to theoretical uncertainties in small-scale clustering by analyzing simulated data. Using a linear galaxy bias model and redMaGiC galaxy sample, we obtain constraints on the matter content of the universe to be $\Omega_{\rm m} = 0.325^{+0.033}_{-0.034}$. We also implement a non-linear galaxy bias model to probe smaller scales that includes parameterizations based on hybrid perturbation theory, and find that it leads to a 17% gain in cosmological constraining power. Using the redMaGiC galaxy sample as foreground lens galaxies, we find the galaxy clustering and galaxy-galaxy lensing measurements to exhibit significant signals akin to decorrelation between galaxies and mass on large scales, which is not expected in any current models. This likely systematic measurement error biases our constraints on galaxy bias and the $S_8$ parameter. We find that a scale-, redshift- and sky-area-independent phenomenological decorrelation parameter can effectively capture this inconsistency between the galaxy clustering and galaxy-galaxy lensing. We perform robustness tests of our methodology pipeline and demonstrate stability of the constraints to changes in the theory model. After accounting for this decorrelation, we infer the constraints on the mean host halo mass of the redMaGiC galaxies from the large-scale bias constraints, finding the galaxies occupy halos of mass approximately $1.5 \times 10^{13} M_{\odot}/h$.

79 ASTRONOMY AND ASTROPHYSICS↗