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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 OSTI2023

A National Infrastructure for Artificial Intelligence on the Grid (NI4AI) (Final Scientific/Technical Report)

Electric utilities have traditionally taken a very pragmatic yet myopic approach with grid sensors and the resulting collected data. Sensors are purchased and deployed to solve a specific, known problem that has risen to sufficient awareness as to justify the effort of deploying sensors and the needed capital investment. This sensor data flows into proprietary software packages with limited functionality intended only to address the initial problem. This approach aligns with the financial incentives of the utility to deploy capital into fixed hardware assets for which the corporations earn a rate of return. This mentality stands in stark contrast to the big data revolution that started nearly 25 years ago with the rise of Google. In this worldview, data is a fundamental business asset; successful organizations collect, store, explore, merge, and exploit as much data as possible to not only solve problems well understood today but also to tackle new problems that will inevitably rise tomorrow. The ARPA-E Open Innovation 2018 project entitled A National Infrastructure for Artificial Intelligence on the Grid or NI4AI for short was designed to demonstrate this alternative paradigm for using data. To do this, the project was composed of three key thrust areas. The first major component deployed a variety of high-frequency grid sensors and captured terabytes of both wide-scale and localized grid measurements, generating high-value datasets for grid research and algorithm development. The second aspect made available PingThings’ PredictiveGridTM, a horizontally scalable, cloud-based data management and AI platform built for time series data to explore and exploit the collected data. Finally, the project fostered a diverse and open research community composed of experts from numerous fields through focused educational content, code sharing, and data science competitions. Shifting away from “single use” sensors and closed data silos within electric utilities is a major benefit to the public at large. This legacy approach to data is incredibly (1) capital intensive (new sensors must be deployed for each new problem and problems tend to arise continuously) and (2) painfully slow (new problems must be identified first and then new sensors must be deployed to collect data to begin to address the issue). The transition to a carbon neutral grid requires a massive transformation of the existing grid infrastructure and will continue to challenge the legacy grid in unforeseen ways. The only way to make the energy transition cost effective is for utilities to abandon this dated data paradigm and adopt more contemporary approaches. NI4AI has shown that it is technically possible and economically feasible to ingest, explore, and exploit grid data collected from even very high frequency sensing, such as continuous point on wave sensors collecting measurements 10,000 times a second. In fact, the PredictiveGrid platform used is commercially available and deployed at several utilities in the United States. Project accomplishments were numerous and included (1) making available a state of the art time series platform to the community, (2) collecting over 520 streams of time series data from grid sensors totaling over 1 trillion grid measurements, and (3) developing and nurturing a community within the industry focused on the use of data to create value for utilities and, ultimately, end consumers.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

The Blending ToolKit: A simulation framework for evaluation of galaxy detection and deblending

We present an open source Python library for simulating overlapping (i.e., blended) images of galaxies and performing self-consistent comparisons of detection and deblending algorithms based on a suite of metrics. The package, named Blending Toolkit (BTK), serves as a modular, flexible, easy-to-install, and simple-to-use interface for exploring and analyzing systematic effects related to blended galaxies in cosmological surveys such as the Vera Rubin Observatory Legacy Survey of Space and Time (LSST). BTK has three main components: (1) a set of modules that perform fast image simulations of blended galaxies, using the open source image simulation package GalSim; (2) a module that standardizes the inputs and outputs of existing deblending algorithms; (3) a library of deblending metrics commonly defined in the galaxy deblending literature. In combination, these modules allow researchers to explore the impacts of galaxy blending in cosmological surveys. Additionally, BTK provides researchers who are developing a new deblending algorithm a framework to evaluate algorithm performance and make principled comparisons with existing deblenders. BTK includes a suite of tutorials and comprehensive documentation. The source code is publicly available on GitHub at https://github.com/LSSTDESC/BlendingToolKit.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

Community Public Mobility Using On-Demand, Low-Speed Electric Vehicles: A Case Study in Downtown St. Louis, Missouri

Legacy fixed route transit systems designed to serve commuters struggle to provide efficient and effective service for short neighborhood trips and for population groups unable to access and egress transit stops using active modes (e.g., elderly, disabled). Neighborhood on-demand transit (ODT) services using low-speed electric vehicles (LSEV) are an innovative technological solution that can help fill this gap in service (e.g., short, high-frequency trips) for diverse populations and trip types. This study evaluated user characteristics and travel behavior for a neighborhood ODT service (using LSEVs) in downtown St. Louis, Missouri using responses from a community survey (n=244), ridership data, and vehicle trajectory information. A comparative analysis between neighborhood ODT, fixed route transit, and transportation network companies (TNC) was also conducted from the perspectives of total travel time, cost, and greenhouse gas emissions. Ultimately, the goal of the analysis was to motivate and inform holistic public mobility systems where different services are optimized to meet specific community needs. Findings indicate that the neighborhood ODT was effective at reaching diverse populations (elderly (20%), lower income (27%), and households with limited access to private vehicles (34%)). ODT reduced total travel time by 32% compared to fixed route transit, produced 2.4 - 4.3 times less greenhouse gas emissions per passenger mile (compared to transit and TNCs), and was more affordable (free to users) than alternative options ($1 for transit, $10-12 for TNCs). Overall satisfaction rates were high, with 80% of respondents rating the service a 4 or 5 out of 5.

ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2024

Cross Correlating Cosmological Probes for LSST & CMB-S4

The upcoming years will be populated with state-of-the-art Stage-IV cosmological surveys. This paper forecasts the cosmological information we expect to constrain using probes from the Rubin Observatory Legacy Survey of Space and Time (LSST) and CMB-S4. We explore the effectiveness of different large-scale-structure probes, including the power spectra of galaxy weak lensing, galaxy position, Cosmic Microwave Background (CMB) lensing, and their cross-correlations. We use correlated lognormal simulations with the expected redshift distributions, galaxy number densities, and noise levels of the LSST survey. For CMB weak lensing, we use CMB-S4 lensing simulations with anticipated noise levels to obtain the expected error bars of each probe. We investigate the constraining power of the cosmological parameters for the individual probes and their combinations in an idealized scenario. We do not take into account astrophysical and observational parameters such as galaxy bias variations and photometric redshift un- certainties. Overall, we find the auto-correlated probes hold stronger constraints on cosmological parameters than the cross-correlated probes due to kernel differences. However, the cross-correlation of galaxy clustering and CMB lensing is very comparable to the auto-correlation of galaxy clustering with only an 8% stronger constraint in S8. It will be important for future surveys to use these auto and cross probes in combination due to the different astrophysical and systematic effects involved in CMB and late-time galaxy data. When systematics are added to this analysis, the cross probes will serve as a check for systematic biases in individual probes. In addition, we find a significant increase in constraint using all four probes in combination. This can be illustrated by the increase in constraint of S8 by 93.2% comparing galaxy clustering auto-correlation to a combination of all probes. Our study is a first step toward forecasting the high-precision cosmological constraints we expect to obtain using the next generation of large-scale structure probes.

Gibbins, Grace↗
DOE OSTI2026

Digitizing Today’s Buildings in the Real World: Lessons from Field Demonstrations

Digital twins, created by generating a virtual replica of a building, enable safe evaluation of operational scenarios and applications like fault detection and diagnosis and advanced controls. However, a prerequisite is the creation of a machine-readable digital representation of a building, currently hindered by fragmented information scattered across mechanical drawings, point lists, and natural language sequences. As a result, digital twin development remains labor-intensive, error-prone, and difficult to validate. To address these challenges, two efforts from ASHRAE aim to support the digitalization of buildings. ASHRAE s223 establishes a semantic model of buildings, representing system components, configuration, and data sources. ASHRAE s231 defines a vendor-neutral programming language for expressing their control logic. As the industry evaluates implementing them in their products, understanding the challenges that vendors and implementers may face is crucial. In this paper, we present findings and lessons learned from field demonstrations in five buildings that implemented control applications using ASHRAE s223 and s231. The demonstrations highlight how semantic modeling and formalized control descriptions can significantly reduce software development time, manual point mapping, and hard-coding. Beyond time efficiency, they enable reliable automation by minimizing human interpretation and providing a means for consistency across projects. We describe the processes and best practices for model creation and model usage, from translating heterogeneous building documentation into semantic representations to implementing control logic in real-world systems. Finally, we discuss the challenges that persist, including integration with legacy software environments, gaps in interoperability, and the level of expertise still required to effectively leverage semantic models.

Prakash, Anand Krishnan↗
DOE OSTI2020

Mitigation of LEO Satellite Brightness and Trail Effects on the Rubin Observatory LSST

In this work, we report studies on the mitigation of optical effects of bright low-Earth-orbit (LEO) satellites on Vera C. Rubin Observatory and its Legacy Survey of Space and Time (LSST). These include options for pointing the telescope to avoid satellites, laboratory investigations of bright trails on the Rubin Observatory LSST camera sensors, algorithms for correcting image artifacts caused by bright trails, experiments on darkening SpaceX Starlink satellites, and ground-based follow-up observations. The original Starlink v0.9 satellites are g ~ 4.5 mag, and the initial experiment "DarkSat" is g ~ 6.1 mag. Future Starlink darkening plans may reach g ~ 7 mag, a brightness level that enables nonlinear image artifact correction to well below background noise. However, the satellite trails will still exist at a signal-to-noise ratio ~ 100, generating systematic errors that may impact data analysis and limit some science. For the Rubin Observatory 8.4 m mirror and a satellite at 550 km, the full width at half maximum of the trail is about 3'' as the result of an out-of-focus effect, which helps avoid saturation by decreasing the peak surface brightness of the trail. For 48,000 LEOsats of apparent magnitude 4.5, about 1% of pixels in LSST nautical twilight images would need to be masked.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

Estimating the Effects of Soil Remediation on Children’s Blood Lead near a Former Lead Smelter in Omaha, Nebraska, USA

Lead exposures from legacy sources threaten children’s health. Soil in Omaha, Nebraska, was contaminated by emissions from a lead smelter and refinery. The U.S. Environmental Protection Agency excavated and replaced contaminated soil at the Omaha Lead Superfund Site between 1999 and 2016. The goal of this study was to assess the association of soil lead level (SLL) and soil remediation status with blood lead levels (BLLs) in children living near or on the site. We linked information on SLL at residential properties with children’s BLLs and assigned remediation status to children’s BLL measurements based on whether their measurements occurred during residence at remediated or unremediated properties. We examined the association of SLL and remediation status with elevated BLL (EBLL). We distinguished the roles of temporal trend and the intervention with time-by-intervention-status interaction contrasts. All analyses estimated odds ratios (ORs) with a generalized estimating equations approach to ensure robustness under the complex correlations among BLL measurements. All analyses controlled for relevant covariates including children’s characteristics. EBLL (> 5 μg/dL ) was associated with both residential SLL [e.g., OR=2.00; 95% confidence interval (CI): 1.83, 2.19; > 400–800 vs. ≤ 200 ppm] and neighborhood SLL [e.g., OR=1.85 (95% CI: 1.62, 2.11; > 400–800 vs. ≤ 200 ppm)] before remediation but only with neighborhood SLL after remediation. The odds of EBLL were higher before remediation [OR 1.52 (95% CI: 1.34, 1.72)]. Similarly, EBLL was positively associated with preremediation status in our interaction analysis [interaction OR=1.18 (95%CI: 1.02, 1.37)]. Residential and neighborhood SLLs were important predictors of EBLLs in children residing near or on this Superfund site. Neighborhood SLL remained a strong predictor following remediation. Our data analyses showed the benefit of soil remediation. Results from the interaction analyses should be interpreted cautiously due to imperfect correspondence of remediation times between remediation and comparison groups. https://doi.org/10.1289/EHP8657

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Cross Correlating Cosmological Probes for Stage-IV Surveys

The next few years will be populated with Stage-IV cosmological surveys. This paper forecasts the cosmological information we expect to constrain using probes from the Rubin Observatory Legacy Survey of Space and Time (LSST) and CMB Stage-4 surveys. We explore the effectiveness of different large-scale-structure probes, including the power spectra of galaxy weak lensing, galaxy positions, Cosmic Microwave Background (CMB) lensing, and their cross-correlation. We use correlated lognormal simulations with the expected redshift distribution, number density of galaxies, and noise levels of the LSST survey, as well as CMB-S4 lensing simulations with anticipated noise levels, to obtain the expected error bars of each of the probes. We investigate the constraining power of the cosmological parameters for the individual probes and their combinations in an idealized scenario, where we do not account for astrophysical and observational parameters such as galaxy bias variations and photometric redshift uncertainties.

Gibbins, Grace↗
DOE OSTI2026

Deep Spectroscopy with DESI for Photometric Redshift Training and Calibration

Deep spectroscopic samples can improve photometric redshift (photo-z) estimates and reduce uncertainties on redshift distributions. Such improvements can increase the cosmological constraining power of large imaging-based experiments such as the Vera C. Rubin Observatory’s Legacy Survey of Space and Time (LSST) and mitigate what may be a limiting systematic effect. We present results from the “DESI-Deep pilot” program, which was designed to assess the capability of the Dark Energy Spectroscopic Instrument (DESI) on the 4m Mayall telescope to measure redshifts of galaxies as faint as expected lensing samples for early LSST data (m i ≤ 24.5). We find that DESI is remarkably efficient at this task, with redshift success rates comparable to the results of observations from 10 m class telescopes with only ∼2 × longer integration time (rather than ∼8 × longer as would be expected from aperture-area scaling), while simultaneously achieving ∼30 times larger multiplexing. We also find that the signal-to-noise ratio of the spectra scales as expected for background-limited observations even for the longest exposure times (∼7 hr) and faintest targets in the program. These results demonstrate that DESI could provide the definitive redshift sample for the early years of LSST with a modest investment of observing time. Based upon the results of this program, we provide updated predictions for the time required to collect benchmark samples for photo-z training and calibration using a variety of spectroscopic facilities. Finally, we describe a potential “DESI-Deep” survey designed to train and calibrate photo-z’s for imaging experiments, and provide forecasts of its impact on cosmological inference.

Dey, Biprateep [University of Toronto; University ↗
DOE OSTI2021

The legacy of weapons grade plutonium production: Health status of Hanford complex workers who manage the waste

The extent and etiology of health effects in workers who maintain underground storage tanks at the Hanford Nuclear Reservation (Hanford) have been subjects of controversy and concern for several decades. Hanford is a decommissioned nuclear production complex managed by the US Department of Energy in southeast Washington State. This integration-of-evidence review evaluates the relationship between exposure to vapors from mixed chemical and radioactive waste stored in underground storage tanks at Hanford and worker health. Hanford workers’ health information was gathered from technical reports, media reports, and published literature, including the systematic search of seven databases. This review describes the health status and health concerns of Hanford tank farm workers based on the integration of the available health effects data from disparate sources. In interviews with external groups, Hanford workers reported both irritant-type symptoms and diseases that they believe are attributable to tank farm vapors. However, the results of this integration-of-evidence review indicated that no pervasive pattern of occupational disease was identified that can be associated with exposure to tank farm vapors. Inhalation exposure to asbestos and beryllium is associated with lung disease from various types of nuclear industry work but not from work on tank farms. This review concluded that while irritant-type symptoms and isolated cases of occupational disease are plausible under certain conditions, the currently available data do not support a pervasive pattern of occupational disease associated with vapor exposure.

Cherry, Debra↗
DOE OSTI2024

Jurassic deserts and CO2 sequestration: the Glen Canyon Group in Utah, western USA

The Early Jurassic in the Western Interior of USA consisted of an extensive desert environment, including one of the largest ergs in geologic history. The Navajo erg has been estimated to extend as much as 2.2 million square kilometers, though the preserved extent is somewhat smaller. The Mesozoic was a global greenhouse phase, and during the Jurassic this region experienced fluctuations in climate aridity, reflected in the depositional environment. In addition to extensive dunefields, interdune deposits (lakes and oases) and fluvial systems are also documented within formations of the Glen Canyon Group. The Navajo Sandstone has received much attention as a potential CO2 injection target in recent years. It consists of thick, aeolian sandstones with high porosity and permeability, occurs in both outcrop and subcrop, and has industry data. Mapping of stratigraphic and sedimentologic changes within the Navajo Sandstone has been undertaken in localized areas, however piecing together these studies and developing regional models for CO2 potential has not received as much attention. There is significant industry data across Utah, and utilizing this data to pivot from a focus on hydrocarbon extraction to CO2 injection is an effective way to move forward with green energy, and to meet carbon neutral emission goals. Using legacy well data, we are developing a more comprehensive study of the Navajo Sandstone as a potential CO2 reservoir, in addition to identifying other zones of interest within the Glen Canyon Group, and improving understanding of stratigraphic complexity within one of the most significant aeolian systems in the world.

Mahon, Elizabeth↗
DOE OSTI2021

Improving the astrometric solution of the Hyper Suprime-Cam with anisotropic Gaussian processes

Context. We study astrometric residuals from a simultaneous fit of Hyper Suprime-Cam images. Aims. We aim to characterize these residuals and study the extent to which they are dominated by atmospheric contributions for bright sources. Methods. We used Gaussian process interpolation with a correlation function (kernel) measured from the data to smooth and correct the observed astrometric residual field. Results. We find that a Gaussian process interpolation with a von Kármán kernel allows us to reduce the covariances of astrometric residuals for nearby sources by about one order of magnitude, from 30 mas 2 to 3 mas 2 at angular scales of ~1 arcmin. This also allows us to halve the rms residuals. Those reductions using Gaussian process interpolation are similar to recent result published with the Dark Energy Survey dataset. We are then able to detect the small static astrometric residuals due to the Hyper Suprime-Cam sensors effects. We discuss how the Gaussian process interpolation of astrometric residuals impacts galaxy shape measurements, particularly in the context of cosmic shear analyses at the Rubin Observatory Legacy Survey of Space and Time.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2020

Hybrid Sorbents for 129 I Capture from Contaminated Groundwater

Radioiodine ( 129 I) poses a risk to the environment due to its long half-life, toxicity, and mobility. It is found at the U.S. Department of Energy Hanford Site due to legacy releases of nuclear wastes to the subsurface where 129 I is predominantly present as iodate (IO 3 – ). To date, a cost-effective and scalable cleanup technology for 129 I has not been identified, with hydraulic containment implemented as the remedial approach. Here, novel high-performing sorbents for 129 I remediation with the capacity to reduce 129 I concentrations to or below the US Environmental Protection Agency (EPA) drinking water standard and procedures to deploy them in an ex-situ pump and treat (P&T) system are introduced. Furthermore, this includes implementation of hybridized polyacrylonitrile (PAN) beads for ex-situ remediation of IO 3 – -contaminated groundwater for the first time. Iron (Fe) oxyhydroxide and bismuth (Bi) oxyhydroxide sorbents were deployed on silica substrates or encapsulated in porous PAN beads. In addition, Fe–, cerium (Ce)–, and Bi–oxyhydroxides were encapsulated with anion-exchange resins. The PAN–bismuth oxyhydroxide and PAN–ferrihydrite composites along with Fe- and Ce-based hybrid anion-exchange resins performed well in batch sorption experiments with distribution coefficients for IO 3 – of >1000 mL/g and rapid removal kinetics. Of the tested materials, the Ce-based hybrid anion-exchange resin was the most efficient for removal of IO 3 – from Hanford groundwater in a column system, with 50% breakthrough occurring at 324 pore volumes. The functional amine groups on the parent resin and amount of active sorbent in the resin can be customized to improve the iodine loading capacity. These results highlight the potential for IO 3 – remediation by hybrid sorbents and represent a benchmark for the implementation of commercially available materials to meet EPA standards for cleanup of 129 I in a large-scale P&T system.

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

A Novel Relative Permeability Model for Gas and Water Flow in Hydrate-Bearing Sediments With Laboratory and Field-Scale Application

In a producing gas hydrate reservoir the effective porosity available for fluid flow constantly changes with dissociation of gas hydrate. Therefore, accurate prediction of relative permeability using legacy models (e.g. Brooks-Corey (B-C), van Genuchten, etc.) that were developed for conventional oil and gas reservoirs would require empirical parameters to be calibrated at various S h over its range of variation, but such calibrations are precluded because of lack of experimental relative permeability data. This study proposes a new relative permeability model for gas hydrate-bearing media that is a function of maximum capillary pressure, capillary entry pressure, pore size distribution index, residual saturations, hydrate saturation, and four other constants. The three novel features of the proposed model are: (i) requires fitting its six empirical parameters only once using experimental data from any single S h , and the same set of empirical parameters predict relative permeability at all S h , (ii) includes the effect of capillarity, and (iii) includes the effect of pore-size distribution. From practical standpoint, the model can be used to simulate multiphase flow in gas hydrate-bearing sediments where the proposed relative permeability can account for the evolving hydrate saturation. The proposed model is implemented in a numerical simulator and the wall time required to perform simulations using the proposed model is shown to be similar to the time it takes to run same simulations with the B-C model. The proposed model is a step forward towards achieving the goal of physically accurate modeling of multiphase flow for gas hydrate-bearing sediments that accounts for the effect of gas hydrate saturation change on relative permeability.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Comparison of plasma start-up with high Z and low Z first wall in WEST

The choice of first wall material is of paramount importance for the plasma start-up conditions in ITER and future fusion power plants. In this context, the present work focuses on the correlations between first wall impurity sources and total radiated power during plasma start-up in the tungsten (W) Environment in Steady-state Tokamak (WEST). The objective is to highlight experimental indications for a preferable combination of start-up plasma scenario and first wall materials. Until 2019, WEST featured a full high Z first wall with all limiters exposing only W surfaces to the plasma. To study the impact of a low Z first wall in WEST, boron nitride tiles were installed in the central part of the inner and outer limiters in 2020. Although visible spectroscopy and bolometry measurements show respectively a strong weakening of the WI line intensity on the limiters and a reduction of radiated power after the changeover, a degradation occurs with the accumulation of plasma exposure. In addition, the different plasma facing elements of the main chamber do not influence equally the radiated power during start-up. In both high Z and low Z environments, a clear non-linear dependence is found between the start-up radiated power and the outer limiter W impurity source. Since W seems to be the main cause for core radiation, correlation between outer limiter W sources and other impurity sources are investigated. Finally, analysis of the legacy of B powder drops on a number of start-up plasmas suggests that it is less effective at reducing radiated power when the first wall is covered with W.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2024

Detecting Anomalous Images in Astronomical Datasets

Abstract Environmental and instrumental conditions can cause anomalies in astronomical images, which can potentially bias all kinds of measurements if not excluded. Detection of the anomalous images is usually done by human eyes, which is slow and sometimes not accurate. This is an important issue in weak lensing studies, particularly in the era of large-scale galaxy surveys, in which image qualities are crucial for the success of galaxy shape measurements. In this work we present two automatic methods for detecting anomalous images in astronomical data sets. The anomalous features can be divided into two types: one is associated with the source images, and the other appears on the background. Our first method, called the entropy method, utilizes the randomness of the orientation distribution of the source shapes and the background gradients to quantify the likelihood of an exposure being anomalous. Our second method involves training a neural network (autoencoder) to detect anomalies. We evaluate the effectiveness of the entropy method on the Canada–France–Hawaii Telescope Lensing Survey (CFHTLenS) and Dark Energy Camera Legacy Survey (DECaLS DR3) data. In CFHTLenS, with 1171 exposures, the entropy method outperforms human inspection by detecting 12 of the 13 anomalous exposures found during human inspection and uncovering 10 new ones. In DECaLS DR3, with 17112 exposures, the entropy method detects a significant number of anomalous exposures while keeping a low false-positive rate. We find that although the neural network performs relatively well in detecting source anomalies, its current performance is not as good as the entropy method.

Astronomy & Astrophysics↗
DOE OSTI2023

Mitigation of the Brighter-Fatter Effect in the LSST Camera

Thick, fully depleted charge-coupled devices (CCDs) are known to exhibit non-linear behavior at high signal levels due to the dynamic behavior of charges collecting in the potential wells of pixels, called the brighter-fatter effect (BFE). This particularly impacts bright calibration stars, which appear larger than their intrinsic shape, creating a flux-dependent point-spread function (PSF) that if left unmitigated, could make up a large fraction of the error budget in Stage IV weak-lensing (WL) surveys such as the Legacy Survey of Space and Time (LSST). In this paper, we analyze image measurements of flat fields and artificial stars taken at different illumination levels with the LSST Camera (LSSTCam) at SLAC National Accelerator Laboratory in order to quantify this effect in the LSST Camera before and after a previously introduced correction technique. We observe that the BFE evolves anisotropically as a function of flux due to higher-order BFEs, which violates the fundamental assumption of this correction method. We then introduce a new sampling method based on a physically motivated model to account these higher-order terms in the correction, and then we test the modified correction on both datasets. We find that the new method corrects the effect in flat fields better than it corrects the effect in artificial stars which we conclude is the result of a unmodeled curl component of the deflection field by the correction. We use these results to define a new metric for the full-well capacity of our sensors and advise image processing strategies to further limit the impact of the effect on LSST WL science pathways.

47 OTHER INSTRUMENTATION↗