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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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At least 775 records · Page 43
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Chemical-Free Lithium Separation from High-Salinity Brines Using Model-Informed and Machine Learning-Optimized Multi-Column Zwitterionic Chromatography

Direct Lithium Extraction (DLE) technologies often struggle to produce high-purity lithium salts from high-salinity brines, as current approaches require chemical-based elution, regeneration, and precipitation steps, resulting in significant environmental footprints. A novel salt fractionation approach using carboxybetaine resin, known as zwitterionic chromatography (ZIC), has demonstrated that lithium ions can be separated from divalent cations under high-salinity conditions using only water as eluent, with no regeneration required. To enable continuous and scalable deployment of this approach, we developed a chemical-free Multi-column Zwitterionic Chromatography (MZC) process and its theoretical and process models. To predict and optimize this nontraditional separation system, we introduced a novel anti-Langmuir isotherm, and the isotherm parameters were estimated through a machine learning-driven optimization based on artificial neural network ensembles with numerical feasibility assessment. Using machine learning-driven optimization, the MZC process achieved 98.0% lithium recovery, 99.5 % Li/(Li + Mg + Ca) purity, a 31.3% productivity increase, and a 33% reduction in water use compared to batch operation. The proposed MZC process enables lithium separation at $0.6-1.2 kg-1 Li, with costs dominated by resin manufacturing, while offering lower separation costs and carbon footprint compared with conventional carbonation. Overall, these findings position the MZC process as an effective polishing step within scalable and sustainable lithium production pipelines.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Structure based virtual screening identifies small molecule effectors for the sialoglycan binding protein Hsa

Infective endocarditis (IE) is a cardiovascular disease often caused by bacteria of the viridans group of streptococci, which includes Streptococcus gordonii and Streptococcus sanguinis. Previous research has found that serine-rich repeat (SRR) proteins on the S. gordonii bacterial surface play a critical role in pathogenesis by facilitating bacterial attachment to sialylated glycans displayed on human platelets. Despite their important role in disease progression, there are currently no anti-adhesive drugs available on the market. Here, we performed structure-based virtual screening using an ensemble docking approach followed by consensus scoring to identify novel small molecule effectors against the sialoglycan binding domain of the SRR adhesin protein Hsa from the S. gordonii strain DL1. The screening successfully predicted nine compounds which were able to displace the native ligand (sialyl-T antigen) in an in vitro assay and bind competitively to Hsa. Furthermore, hierarchical clustering based on the MACCS fingerprints showed that eight of these small molecules do not share a common scaffold with the native ligand. This study indicates that SRR family of adhesin proteins can be inhibited by diverse small molecules and thus prevent the interaction of the protein with the sialoglycans. This opens new avenues for discovering potential drugs against IE.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2023

A Process Model for ITCZ Narrowing under Warming Highlights Clear-Sky Water Vapor Feedbacks and Gross Moist Stability Changes in AMIP Models

Tropical areas with mean upward motion—and as such the zonal-mean intertropical convergence zone (ITCZ)—are projected to contract under global warming. To understand this process, a simple model based on dry static energy and moisture equations is introduced for zonally symmetric overturning driven by sea surface temperature (SST). Processes governing ascent area fraction and zonal mean precipitation are examined for insight into Atmospheric Model Intercomparison Project (AMIP) simulations. Bulk parameters governing radiative feedbacks and moist static energy transport in the simple model are estimated from the AMIP ensemble. Uniform warming in the simple model produces ascent area contraction and precipitation intensification—similar to observations and climate models. Contributing effects include stronger water vapor radiative feedbacks, weaker cloud-radiative feedbacks, stronger convection-circulation feedbacks, and greater poleward moisture export. The simple model identifies parameters consequential for the inter-AMIP-model spread; an ensemble generated by perturbing parameters governing shortwave water vapor feedbacks and gross moist stability changes under warming tracks inter-AMIP-model variations with a correlation coefficient ~0.46. Here, the simple model also predicts the multimodel mean changes in tropical ascent area and precipitation with reasonable accuracy. Furthermore, the simple model reproduces relationships among ascent area precipitation, ascent strength, and ascent area fraction observed in AMIP models. A substantial portion of the inter-AMIP-model spread is traced to the spread in how moist static energy and vertical velocity profiles change under warming, which in turn impact the gross moist stability in deep convective regions—highlighting the need for observational constraints on these quantities.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Binary Quantum Control Optimization with Uncertain Hamiltonians

Optimizing the controls of quantum systems plays a crucial role in advancing quantum technologies. The time-varying noises in quantum systems and the widespread use of inhomogeneous quantum ensembles raise the need for high-quality quantum controls under uncertainties. In this paper, we consider a stochastic discrete optimization formulation of a discretized binary optimal quantum control problem involving Hamiltonians with predictable uncertainties. We propose a sample-based reformulation that optimizes both risk-neutral and risk-averse measurements of control policies, and solve these with two gradient-based algorithms using sum-up-rounding approaches. Furthermore, we discuss the differentiability of the objective function and prove upper bounds of the gaps between the optimal solutions to binary control problems and their continuous relaxations. We conduct numerical simulations on various sized problem instances based on two applications of quantum pulse optimization; we evaluate different strategies to mitigate the impact of uncertainties in quantum systems. In conclusion, we demonstrate that the controls of our stochastic optimization model achieve significantly higher quality and robustness compared with the controls of a deterministic model.

conditional value-at-risk (CVaR)↗
NASA NTRS2000

Radiance and Jacobian Intercomparison of Radiative Transfer Models Applied to HIRS and AMSU Channels

The goals of this study are the evaluation of current fast radiative transfer models (RTMs) and line-by-line (LBL) models. The intercomparison focuses on the modeling of 11 representative sounding channels routinely used at numerical weather prediction centers: 7 HIRS (High-resolution Infrared Sounder) and 4 AMSU (Advanced Microwave Sounding Unit) channels. Interest in this topic was evidenced by the participation of 24 scientists from 16 institutions. An ensemble of 42 diverse atmospheres was used and results compiled for 19 infrared models and 10 microwave models, including several LBL RTMs. For the first time, not only radiances, but also Jacobians (of temperature, water vapor and ozone) were compared to various LBL models for many channels. In the infrared, LBL models typically agree to within 0.05-0.15 K (standard deviation) in terms of top-of-the-atmosphere brightness temperature (BT). Individual differences up to 0.5 K still exist, systematic in some channels, and linked to the type of atmosphere in others. The best fast models emulate LBL BTs to within 0.25 K, but no model achieves this desirable level of success for all channels. The ozone modeling is particularly challenging, In the microwave, fast models generally do quite well against the LBL model to which they were tuned. However significant differences were noted among LBL models, Extending the intercomparison to the Jacobians proved very useful in detecting subtle and more obvious modeling errors. In addition, total and single gas optical depths were calculated, which provided additional insight on the nature of differences. Recommendations for future intercomparisons are suggested.

Garand, L.↗
NASA NTRS2000

Radiance and Jacobian Intercomparison of Radiative Transfer Models Applied to HIRS and AMSU Channels

The goals of this study are the evaluation of current fast radiative transfer models (RTMs) and line-by-line (LBL) models. The intercomparison focuses on the modeling of 11 representative sounding channels routinely used at numerical weather prediction centers: seven HIRS (High-resolution Infrared Sounder) and four AMSU (Advanced Microwave Sounding Unit) channels. Interest in this topic was evidenced by the participation of 24 scientists from 16 institutions. An ensemble of 42 diverse atmospheres was used and results compiled for 19 infrared models and 10 microwave models, including several LBL RTMs. For the first time, not only radiances, but also Jacobians (of temperature, water vapor, and ozone) were compared to various LBL models for many channels. In the infrared, LBL models typically agree to within 0.05-0.15 K (standard deviation) in terms of top-of-the-atmosphere brightness temperature (BT). Individual differences up to 0.5 K still exist, systematic in some channels, and linked to the type of atmosphere in others. The best fast models emulate LBL BTs to within 0.25 K, but no model achieves this desirable level of success for all channels. The ozone modeling is particularly challenging. In the microwave, fast models generally do quite well against the LBL model to which they were tuned. However significant differences were noted among LBL models. Extending the intercomparison to the Jacobians proved very useful in detecting subtle and more obvious modeling errors. In addition, total and single gas optical depths were calculated, which provided additional insight on the nature of differences. Recommendations for future intercomparisons are suggested.

Garand, L.↗
DOE OSTI2022

Improving Seasonal Forecast Using Probabilistic Deep Learning

The path toward realizing the potential of seasonal forecasting and its socioeconomic benefits relies on improving general circulation model (GCM) based dynamical forecast systems. To improve dynamical seasonal forecasts, it is crucial to set up forecast benchmarks, and clarify forecast limitations posed by model initialization errors, formulation deficiencies, and internal climate variability. With huge costs in generating large forecast ensembles, and limited observations for forecast verification, the seasonal forecast benchmarking and diagnosing task proves challenging. Here, we develop a probabilistic deep learning-based statistical forecast methodology, drawing on a wealth of climate simulations to enhance seasonal forecast capability and forecast diagnosis. By explicitly modeling the internal climate variability and GCM formulation differences, the proposed Conditional Generative Forecasting (CGF) methodology enables bypassing crucial barriers in dynamical forecast, and offers a top-down viewpoint to examine how complicated GCMs encode the seasonal predictability information. We apply the CGF methodology for global seasonal forecast of precipitation and 2 m air temperature, based on a unique data set consisting 52,201 years of climate simulation. Results show that the CGF methodology can faithfully represent the seasonal predictability information encoded in GCMs. We successfully apply this learned relationship in real-world seasonal forecast, achieving competitive performance compared to dynamical forecasts. Using this CGF as benchmark, we reveal the impact of insufficient forecast spread sampling that limits the skill of the considered dynamical forecast system. Finally, we introduce different strategies for composing ensembles using the CGF methodology, highlighting the potential for leveraging the strengths of multiple GCMs to achieve advantgeous seasonal forecast.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Dataset for manuscript "Consequences of the failure of equipartition for the p-V behavior of liquid water and the hydration free energy components of a small protein"

Previously, we showed that in the molecular dynamics simulation of a rigid model of water it is necessary to use an integration time-step dt that is less than or equal to 0.5 fs to ensure equipartition between translational and rotational modes. We extended that study in the NVT ensemble to NpT conditions and to an aqueous protein. We study neat liquid water with the rigid, SPC/E model and the protein BBA (PDB ID: 1FME) solvated in the rigid, TIP3P model. We examined integration time-steps ranging from 0.5 fs to 4.0 fs for various thermostat plus barostat combinations. We find that a small time-step, dt, is necessary to ensure consistent prediction of the simulation volume. Hydrogen mass repartitioning alleviates the problem somewhat, but is ineffective for the typical time-step used with this approach. The compressibility, a measure of volume fluctuations, is seen to be sensitive to dt. Using the mean volume estimated from the NpT simulation, we examined the electrostatic and van der Waals contribution to the hydration free energy of the protein in the NVT ensemble. These contributions are also sensitive to dt. In going from a time-step of 2 fs to a time-step of 0.5 fs, the change in the net electrostatic plus van der Waals contribution to the hydration of BBA is already in excess of the folding free energy reported for this protein. The data-set contains the simulation metadata and log files that support the claims noted above.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS2017

GEOS S2S-2_1: GMAO's New High Resolution Seasonal Prediction System

A new version of the modeling and analysis system used to produce sub-seasonal to seasonal forecasts has just been released by the NASA Goddard Global Modeling and Assimilation Office. The new version runs at higher atmospheric resolution (approximately 12 degree globally), contains a substantially improved model description of the cryosphere, and includes additional interactive earth system model components (aerosol model). In addition, the Ocean data assimilation system has been replaced with a Local Ensemble Transform Kalman Filter. Here will describe the new system, along with the plans for the future (GEOS S2S-3_0) which will include a higher resolution ocean model and more interactive earth system model components (interactive vegetation, biomass burning from fires). We will also present results from a free-running coupled simulation with the new system and results from a series of retrospective seasonal forecasts. Results from retrospective forecasts show significant improvements in surface temperatures over much of the northern hemisphere and a much improved prediction of sea ice extent in both hemispheres. The precipitation forecast skill is comparable to previous S2S systems, and the only trade off is an increased double ITCZ, which is expected as we go to higher atmospheric resolution.

Molod, Andrea↗
DOE OSTI2025

Determination of Site Occupancy in the M–Pd–Zn (M = Cu, Ag, and Au) γ-Brass Phase by CALculation of PHAse Diagrams Modeling and Rietveld Refinement

The Pd–Zn γ-brass phase provides exciting opportunities for synthesizing site-isolated catalysts with precisely controlled Pd active site ensembles. Introducing a third metallic element into the γ-brass lattice further perturbs the catalytic active site ensembles. Here, in this work, we introduce coinage metallic elements M (M = Cu, Ag, and Au) into the Pd–Zn γ-brass phase and investigate the site occupation factors of each element in the γ-brass lattice. The CALculation of PHAse Diagrams (CALPHAD) modeling approach supported by energetics predicted by the density functional theory and X-ray and neutron diffraction with Rietveld refinement were used to identify the SOF on each Wyckoff site for various M amounts alloyed into the Pd–Zn γ-brass phase. The present analysis unveils the strong preference for Pd occupying the outer tetrahedral (OT) site in the γ-brass lattice, while the coinage metallic elements tend to substitute for Zn on the octahedral (OH) site. The determination of site occupancy in the bulk M–Pd–Zn γ-brass phase provides opportunities to investigate and tailor potential catalytically active site ensembles in the γ-brass phase materials.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Evaluation of Ocean Biogeochemistry and Carbon Cycling in CMIP Earth System Models With the International Ocean Model Benchmarking (IOMB) Software System

Abstract The International Ocean Model Benchmarking (IOMB) software package is a new community resource that we use here to evaluate surface and upper ocean biogeochemical variables and integrated anthropogenic carbon uptake from earth system models (ESMs) contributing to the 5th and 6th phases of the Coupled Model Intercomparison Project (CMIP5 and CMIP6). IOMB generates graphics and tables for systematically comparing model predictions against multiple datasets. Our analysis reveals some improvement in the multi‐model mean from CMIP5 to CMIP6 for most of the variables we examined. Compared to data‐constrained estimates of ocean anthropogenic carbon uptake for the 1994–2007 period, negative biases exist for many models between 30 and 50°S. Global model estimates of anthropogenic carbon uptake for the same period do not change significantly from CMIP5 to CMIP6, with the combined ensemble mean estimate of 27.8 ± 0.5 Pg C lower than a data‐constrained estimate of 33.0 ± 4.0 Pg C. At the same time, the change in the natural carbon inventory from CMIP is estimated to be a source of 0.7 ± 0.3 Pg C, which is considerably smaller in magnitude than a data‐constrained estimate of 5.0 ± 3.0 Pg C. With chlorofluorocarbon (CFC) predictions available for several models, we demonstrate that negative anthropogenic dissolved inorganic carbon biases coincide with negative biases in CFC concentration, highlighting the importance of weak exchange between the surface and interior ocean in regulating rates of anthropogenic carbon uptake. To examine the robustness of this attribution across the CMIP models, we calculate the global vertical temperature gradient between 200 and 1,000 m as a metric for global stratification and exchange between the surface and deeper waters. We find a linear relationship between the bias of the vertical temperature gradients and the bias in global anthropogenic carbon uptake, consistent with the hypothesis that model biases in anthropogenic carbon uptake are related to biases in surface‐to‐interior exchange by physical processes.

58 GEOSCIENCES↗
DOE OSTI2021

Improving Prediction of Surface Solar Irradiance Variability by Integrating Observed Cloud Characteristics and Machine Learning

A 5-year, 1-minute resolution observational dataset of clouds and solar radiation was produced that includes two metrics of the variability in surface solar irradiance due to cloud type and fractional sky cover. Multiple regression models were trained to fit observations of surface solar irradiance variability from those two cloud property predictors. We found that ensemble tree-based methods, Random Forest and Gradient Boosting Machine, have the least overfitting issues and showed the best performance with an R2 of 0.42. While the observational data trained in this study was only from one site, the U.S. Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) Southern Great Plains (SGP) site in Oklahoma, initial comparisons of the seasonality of the statistics suggest that these results are relatively weather regime independent; the generality of such a finding across sites will be tested in future work. The observational data and developed machine learning model are being used to create a numerical weather prediction model parameterization to enable day-ahead solar variability prediction in a computationally efficient way. This is a first step towards creating a new paradigm of predicting day-ahead variability with the potential to provide a new tool to improve grid operation, planning, and resilience.

Riihimaki, Laura↗
DOE OSTI2020

Forecasting Remote Atmospheric Responses to Decadal Kuroshio Stability Transitions

Based on observational estimates and global ocean eddy-resolving coupled retrospective initialized predictions, we show that Kuroshio Extension variability affects rainfall variability along the west coast of North America. We show that the teleconnection between the current undulations and downstream rainfall can lead to improved subseasonal to seasonal predictions of precipitation over California, and we demonstrate that capturing these teleconnections requires coupled systems with sufficient ocean resolution (i.e., eddy-resolving), especially over time scales longer than one season. The improved forecast skill is diagnosed in terms of 35 years of retrospective initialized ensemble forecasts with an ocean eddy-resolving and an ocean eddy-parameterized coupled model. Not only does the ocean eddy-resolving model show sensitivity to Kuroshio Extension variability in terms of western North America precipitation, but the ocean eddy-resolving forecasts also show improved forecast skill compared to the ocean eddy-parameterized model. Additionally, the ocean eddy-parameterized coupled model shows no sensitivity to Kuroshio Extension variability. We also find near-decadal variability associated with a progression of a lower-tropospheric height dipole around the North Pacific and how these height anomalies lead to wind-driven Rossby waves that affect the eddy activity in the Kuroshio Extension with a time lag on the order of four years. This decadal-scale variability (~10 years) opens the possibility of multiyear predictability of western North American rainfall.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

$Ξ$ 𝑐 → $Ξ$ form factors from lattice QCD with domain-wall quarks: A new piece in the puzzle of $Ξ^0_𝑐$ decay rates

We present a lattice quantum chromodynamics (QCD) determination of the vector and axial-vector form factors that describe the charm-baryon semileptonic decays Ξ 𝑐 → Ξ⁢ℓ + ⁢𝜈 ℓ . The calculation uses a domain-wall action for the up, down, and strange quarks, and an anisotropic clover action for the charm quark. We use four ensembles of gauge-field configurations generated by the RBC and UKQCD collaborations, with lattice spacings between 0.111 and 0.073 fm and pion masses ranging from 420 to 230 MeV. We present Standard Model predictions for the decay rates and branching fractions of Ξ$^0_𝑐$ → Ξ$^−_𝑐$⁢ℓ + ⁢𝜈 ℓ and Ξ$^+_𝑐$ → Ξ$^0_𝑐$⁢ℓ + ⁢𝜈 ℓ for ℓ =𝑒, 𝜇. In particular, we obtain Γ⁡(Ξ$^0_𝑐$ → Ξ − ⁢𝑒 + ⁢𝜈 𝑒 )/|𝑉 𝑐⁢𝑠 | 2 = 0.2515⁢(73) ps −1 and ℬ⁡(Ξ$^0_𝑐$ → Ξ − ⁢𝑒 +⁢ 𝜈 𝑒 ) = 3.58⁢(12)%. These values are higher than those predicted by a previous lattice calculation and substantially higher than the experimentally measured values, but consistent with expectations from approximate 𝑆⁢𝑈⁡(3) flavor symmetry.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
NASA NTRS2020

Scaling Observation Error for Optimal Assimilation of CCI SST Data into a Regional HYCOM EnOI System

South Africa currently possesses no operational ocean forecasting system for the purpose of predicting ocean state variables including temperature,salinity and velocity. Substantial initial efforts towards this goal have been made and resulted in a system using a regional Hybrid Coordinate Ocean Model (HYCOM) along with the Ensemble Optimal Interpolation (EnOI)assimilation scheme. Assimilating only sea surface temperature (SST) observations from the Operational Sea Surface Temperature and Sea Ice Analysis (OSTIA) product into the system resulted in a degraded forecast. Aiming to address this, Climate Change Initiative (CCI) SSTs are assimilated into the system in an effort to improve the forecast skill. Observation errors in the assimilated product are used in the EnOI to determine whether more confidence should be placed in the model or observations in producing the analysis, but overconfidence in observations can shock the model and result in failure. To tweak the impact of the assimilation, a scaling factor is applied in the assimilation code. A scaling factor of 25 was found to produce a favourable result with lowest mean root mean square error (RMSE;1.098°C) between the model and observations over time. Postulating the error to be overconfident, a floor value is introduced in order to set a minimum value for the observation error thereby reducing confidence in the observations. These experiments fared less favourably with a floor value of 0.5 and a scaling factor of 15 producing the best mean RMSE (1.118°C).

Luyt, Hermann↗
NASA NTRS2015

Physical Models of Layered Polar Firn Brightness Temperatures from 0.5 to 2 GHz

We investigate physical effects influencing 0.5-2 GHz brightness temperatures of layered polar firn to support the Ultra Wide Band Software Defined Radiometer (UWBRAD) experiment to be conducted in Greenland and in Antarctica. We find that because ice particle grain sizes are very small compared to the 0.5-2 GHz wavelengths, volume scattering effects are small. Variations in firn density over cm- to m-length scales, however, cause significant effects. Both incoherent and coherent models are used to examine these effects. Incoherent models include a 'cloud model' that neglects any reflections internal to the ice sheet, and the DMRT-ML and MEMLS radiative transfer codes that are publicly available. The coherent model is based on the layered medium implementation of the fluctuation dissipation theorem for thermal microwave radiation from a medium having a nonuniform temperature. Density profiles are modeled using a stochastic approach, and model predictions are averaged over a large number of realizations to take into account an averaging over the radiometer footprint. Density profiles are described by combining a smooth average density profile with a spatially correlated random process to model density fluctuations. It is shown that coherent model results after ensemble averaging depend on the correlation lengths of the vertical density fluctuations. If the correlation length is moderate or long compared with the wavelength (approximately 0.6x longer or greater for Gaussian correlation function without regard for layer thinning due to compaction), coherent and incoherent model results are similar (within approximately 1 K). However, when the correlation length is short compared to the wavelength, coherent model results are significantly different from the incoherent model by several tens of kelvins. For a 10-cm correlation length, the differences are significant between 0.5 and 1.1 GHz, and less for 1.1-2 GHz. Model results are shown to be able to match the v-pol SMOS data closely and predict the h-pol data for small observation angles.

layered polar firn emission↗
DOE OSTI2020

Predicting synthesizable multi-functional edge reconstructions in two-dimensional transition metal dichalcogenides

Abstract Two-dimensional (2D) transition metal dichalcogenides (TMDCs) have attracted tremendous interest as functional materials due to their exceptionally diverse and tunable properties, especially in their edges. In addition to the conventional armchair and zigzag edges common to hexagonal 2D materials, more complex edge reconstructions can be realized through careful control over the synthesis conditions. However, the whole family of synthesizable, reconstructed edges remains poorly studied. Here, we develop a computational approach integrating ensemble-generation, force-relaxation, and electronic-structure calculations to systematically and efficiently discover additional reconstructed edges and screen their functional properties. Using MoS 2 as a model system, we screened hundreds of edge-reconstruction to discover over 160 reconstructed edges to be more stable than the conventional ones. More excitingly, we discovered nine new synthesizable reconstructred edges with record thermodynamic stability, in addition to successfully reproducing three recently synthesized edges. We also find our predicted reconstructed edges to have multi-functional properties—they show near optimal hydrogen evolution activity over the conventional edges, ideal for catalyzing hydrogen-evolution reaction (HER) and also exhibit half-metallicity with a broad variation in magnetic moments, making them uniquely suitable for nanospintronic applications. Our work reveals the existence of a wide family of synthesizable, reconstructed edges in 2D TMDCs and opens a new materials-by-design paradigm of ‘intrinsic’ edge engineering multifunctionality in 2D materials.

Hu, Guoxiang (ORCID:0000000329428564)↗
DOE OSTI2021

Effects of overlapping sources on cosmic shear estimation: Statistical sensitivity and pixel-noise bias

The next generation of dark-energy imaging surveys — so called “Stage-IV” surveys, such as that of the Rubin Observatory Legacy Survey of Space and Time (LSST) — will cross a threshold in the number density of detected sources on the sky that requires qualitatively different image analysis and measurement techniques compared to the current generation of Stage-III surveys. In Stage-IV surveys, a significant amount of the cosmologically useful information is due to sources whose images overlap with those of other sources on the sky. Here, we focus on the weak gravitational lensing probe, for which we expect the largest impact since the cosmic shear signal is primarily encoded in the estimated shapes of observed galaxies and thus directly impacted by overlaps. We introduce a framework based on the Fisher formalism to analyze the effect of the overlapping sources (“blending”) on the estimation of cosmic shear. This method gives concrete predictions for the minimum loss of information due to noise and blending for any choice of “deblending” scheme and shape-measurement algorithm. Our studies account for undetected sources but do not address their full effects and biases they may introduce. We use simulated images and predict this impact of blending for three surveys: the Dark Energy Survey (DES), the Hyper-Suprime Cam Subaru Strategic Program (HSC-SSP), and the Rubin LSST. Our methodology successfully estimates the statistical sensitivity to weak lensing for DES and HSC early results. For LSST, we present the expected loss in statistical sensitivity for the ten-year survey due to blending. We find that for approximately 62% of galaxies that are likely to be detected in full-depth LSST images, at least 1% of the flux in their pixels is from overlapping sources. We also find that the statistical correlations between measures of overlapping galaxies and, to a much lesser extent (0.2%) the higher shot noise level due to their presence, decrease the effective number density of galaxies, N eff , by ~ 18%. We calculate an upper limit on N eff of 39.4 galaxies per arcmin 2 in r band. We study the impact of stars on as a function of stellar density and illustrate the diminishing returns of extending the survey into lower Galactic latitudes. We extend the simulation-based Fisher formalism to predict the expected increase in pixel-noise bias due to blending for maximum-likelihood (ML) shape estimators. We find that noise bias depends sensitively on the particular shape estimator and measure of ensemble-average shape that is used, and properties of the galaxy that include redshift-dependent quantities such as size and luminosity. The source code for these studies is available online.[The documented software developed for the catalog-level studies are available in the open-source LSST DESC github repository https://github.com/LSSTDESC/WeakLensingDeblending. The software for analyzing one or two galaxies with user-defined parameters is in the open-source github repository https://github.com/ismael-mendoza/ShapeMeasurementFisherFormalism.]

79 ASTRONOMY AND ASTROPHYSICS↗