Search NASA⌕ Search

SEARCH · Search NASA

Results for “Uncertainty visualization”

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 91 records · Page 5
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Quantifying uncertainties in crystal electric field Hamiltonian fits to neutron data

We systematically examine uncertainties from fitting rare earth single-ion crystal electric field (CEF) Hamiltonians to inelastic neutron scattering data. Using pyrochlore and delafossite structures as test cases, we find that uncertainty in CEF parameters can be large despite visually excellent fits. These results show Yb ^{3+} 3 + compounds have particularly large g g -tensor uncertainty because of the few available peaks. In such cases, additional constraints are necessary for meaningful fits.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2018

Performance Assessment of a Generic Nuclear Waste Repository in Shale - 18392

A performance assessment (PA) is an important component of a comprehensive safety analysis for a nuclear waste repository. In a PA, probabilistic simulations of the total repository system are performed, and results are evaluated against performance metrics. Uncertainty and sensitivity analyses help prioritize additional research and model development. The United States Department of Energy (DOE) has been developing a state of the art PA simulation software tool-kit, Geologic Disposal Safety Assessment (GDSA) Framework, that couples increasingly higher fidelity models of subsystem processes into total system PA simulations. Over the past several years, PAs of generic repositories in three geologic media (salt, shale, and crystalline rock) have demonstrated ongoing developments in capability. The current PA of a nuclear waste repository in a generic shale formation showcases GDSA Framework, including capabilities in domain discretization (Cubit), multi-physics simulations (PFLOTRAN), uncertainty and sensitivity analysis (Dakota), and visualization (Paraview). The generic shale reference case considers the disposal of 22,000 metric tons heavy metal of commercial spent nuclear fuel (SNF) in a generic shale formation. PA simulations account for the thermal load and radionuclide inventory of the waste form, components of the engineered barrier system including waste package and buffer, and components of the natural barrier system including the host rock shale and underlying and overlying stratigraphic units. Two repository layouts are considered, one for emplacement of waste packages containing 12 pressurized water reactor (PWR) assemblies, and one for 4-PWR waste package emplacement. Model domains are half-symmetry and contain between 7 and 22 million grid cells. Grid refinement captures the detail of individual waste packages, emplacement drifts, access drifts, and shafts. Simulations are run in a high-performance computing (HPC) environment on 512 or 2048 processes. The governing equations describing coupled heat and fluid flow and reactive transport are solved with PFLOTRAN, an open-source, massively parallel multiphase flow and reactive transport code. Additional simulated processes include waste package degradation; waste form dissolution; radioactive decay and ingrowth in aqueous, solid, and adsorbed phases; and a simple biosphere based on a pumping well. Simulations are run to 106 y; dose and radionuclide concentrations are observed within aquifers at a point approximately 5 km downgradient of the repository. Dakota is used to sample likely ranges of input parameters including waste form and waste package degradation rates and properties of engineered and natural materials to quantify uncertainty in predicted concentrations and sensitivity to input parameters. Given the assumptions of the reference case, results of 12-PWR and 4-PWR simulations are very similar. Dose at the pumping well 5-km downgradient of the repository does not exceed 10-9 Sv/y. I-129 is the largest contributor to dose. The dose due to Cl-36, the next largest contributor, is orders of magnitude smaller. At the pumping well, the concentration of I-129 5-km downgradient does not exceed 10-14 mol/L. Rank correlation coefficients indicate that the maximum concentration of I-129 within the aquifer is sensitive to shale (repository host rock) porosity and aquifer permeability. These results affirm that HPC-capable codes can be used to simulate important multi-physics couplings directly in a total system performance assessment of a geologic repository, and can be used in prioritization of future research and model development. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2022

Coordinated Ramping Product and Regulation Reserve Procurements in CAISO and MISO using Multi-Scale Probabilistic Solar Power Forecasts (Pro2R)

How can probabilistic solar forecasts lower costs and improve reliability for independent system operator (ISO) markets? We tackle this question in three steps. First, we enhance an existing solar forecasting system to provide well-calibrated hours-ahead probabilistic forecasts. We then relate the degree of uncertainty in those forecasts to error distributions for net load ramps for the California ISO (CAISO) using statistical and machine learning methods. Projected net load errors conditioned on solar uncertainty are translated into flexible ramp requirements that therefore reflect real-time meteorological and solar conditions, improving on typical ISO procedures. Finally, a multi-period look-ahead production cost model quantifies how conditional ramp requirements can a) decrease operating costs by lowering requirements compared to often conservative unconditional methods, and b) reduce generation scarcity events and consequently improve reliability by increasing flexibility requirements at times when unconditional forecast-based requirements understate actual ramp uncertainty. In addition to the products just described (quantification of solar uncertainty, its translation into requirements for ramp capability product, and quantification of the benefits of more accurate ramp requirements), this project also developed a visualization system that alerts system operators of ramp and uncertainty conditions within the network based on solar forecasts. The system is called Resource Forecast and Ramp Visualization for Situational Awareness (RaVIS). These four products represent significant advances in the state-of-the-art of probabilistic solar forecasting, development of weather-informed reserve requirements, production costing methods for estimating the benefits of more accurate reserve requirements, and visualization of system status, respectively. Yet the products are also practical and can be immediately implemented, potentially enabling system operators to save millions of dollars in ramp product procurement costs per year.

14 SOLAR ENERGY↗
DOE OSTI2018

Information Guided Exploration of Scalar Values and Isocontours in Ensemble Datasets

Uncertainty of scalar values in an ensemble dataset is often represented by the collection of their corresponding isocontours. Various techniques such as contour-boxplot, contour variability plot, glyphs and probabilistic marching-cubes have been proposed to analyze and visualize ensemble isocontours. All these techniques assume that a scalar value of interest is already known to the user. Not much work has been done in guiding users to select the scalar values for such uncertainty analysis. Moreover, analyzing and visualizing a large collection of ensemble isocontours for a selected scalar value has its own challenges. Interpreting the visualizations of such large collections of isocontours is also a difficult task. In this work, we propose a new information-theoretic approach towards addressing these issues. Using specific information measures that estimate the predictability and surprise of specific scalar values, we evaluate the overall uncertainty associated with all the scalar values in an ensemble system. This helps the scientist to understand the effects of uncertainty on different data features. To understand in finer details the contribution of individual members towards the uncertainty of the ensemble isocontours of a selected scalar value, we propose a conditional entropy based algorithm to quantify the individual contributions. This can help simplify analysis and visualization for systems with more members by identifying the members contributing the most towards overall uncertainty. We demonstrate the efficacy of our method by applying it on real-world datasets from material sciences, weather forecasting and ocean simulation experiments.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2018

Demonstration of LOTUS multiphysics BEPU analysis framework for LB-LOCA simulations

The LOCA Toolkit for U.S. light water reactors (LOTUS) currently under development at Idaho National Labs (INL) is a plug and play, multiphysics environment to be used in support of system level plant transient analysis. New proposed rule changes in LOCA safety regulations (10 CFR 50.46c) require the inclusion of cladding hydrogen content in the evaluation of equivalent cladding reacted (ECR) and peak cladding temperature (PCT). The potential for a future implementation of said rule change motivates the current development of a unique union of core design, system analysis, and fuel performance codes within a framework allowing for uncertainty quantification (UQ). A demonstration of LOTUS capabilities to address this potential need is performed with an integration of the core design code PHISICS, the fuel performance code FRAPCON, and the system analysis code RELAP5-3D. UQ is performed via Wilks’ method to compute the one sided 95/95 confidence values of the aforementioned safety metrics. Results demonstrate the benefits of the Multiphysics Best Estimate Plus Uncertainty (MP-BEPU) methodology and provide useful visualization of the limiting cases.

10CFR50.46c↗
DOE OSTI2024

Uncertainty quantification for molecular property predictions with graph neural architecture search

Graph Neural Networks (GNNs) have emerged as a prominent class of data-driven methods for molecular property prediction. However, a key limitation of typical GNN models is their inability to quantify uncertainties in the predictions. This capability is crucial for ensuring the trustworthy use and deployment of models in downstream tasks. To that end, we introduce AutoGNNUQ, an automated uncertainty quantification (UQ) approach for molecular property prediction. AutoGNNUQ leverages architecture search to generate an ensemble of high-performing GNNs, enabling the estimation of predictive uncertainties. Our approach employs variance decomposition to separate data (aleatoric) and model (epistemic) uncertainties, providing valuable insights for reducing them. In our computational experiments, we demonstrate that AutoGNNUQ outperforms existing UQ methods in terms of both prediction accuracy and UQ performance on multiple benchmark datasets, and generalizes well to out-of-distribution datasets. Additionally, we utilize t-SNE visualization to explore correlations between molecular features and uncertainty, offering insight for dataset improvement. AutoGNNUQ has broad applicability in domains such as drug discovery and materials science, where accurate uncertainty quantification is crucial for decision-making.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2016

User-defined feature comparison for vector field ensembles

Most of the existing approaches to visualize vector field ensembles are to reveal the uncertainty of individual variables, for example, statistics, variability, etc. However, a user-defined derived feature like vortex or air mass is also quite significant, since they make more sense to domain scientists. In this paper, we present a new framework to extract user-defined derived features from different simulation runs. Specially, we use a detail-to-overview searching scheme to help extract vortex with a user-defined shape. We further compute the geometry information including the size, the geo-spatial location of the extracted vortexes. We also design some linked views to compare them between different runs. At last, the temporal information such as the occurrence time of the feature is further estimated and compared. Results show that our method is capable of extracting the features across different runs and comparing them spatially and temporally.

Comparative visualization↗
DOE OSTI2025

Trust Your Gut: Comparing Human and Machine Inference from Noisy Visualizations

People commonly utilize visualizations not only to examine a given dataset, but also to draw generalizable conclusions about the underlying models or phenomena. Prior research has compared human visual inference to that of an optimal Bayesian agent, with deviations from rational analysis viewed as problematic. However, human reliance on non-normative heuristics may prove advantageous in certain circumstances. We investigate scenarios where human intuition might surpass idealized statistical rationality. In two experiments, we examine individuals’ accuracy in characterizing the parameters of known data-generating models from bivariate visualizations. Our findings indicate that, although participants generally exhibited lower accuracy compared to statistical models, they frequently outperformed Bayesian agents, particularly when faced with extreme samples. Participants appeared to rely on their internal models to filter out noisy visualizations, thus improving their resilience against spurious data. However, participants displayed overconfidence and struggled with uncertainty estimation. They also exhibited higher variance than statistical machines. Our findings suggest that analyst gut reactions to visualizations may provide an advantage, even when departing from rationality. These results carry implications for designing visual analytics tools, offering new perspectives on how to integrate statistical models and analyst intuition for improved inference and decision-making. The data and materials for this paper are available at https://osf.io/qmfv6

human-machine collaboration↗
DOE OSTI2023

Model data for numerical evaluation of photosensitive tracers as a strategy for separating surface and subsurface transient storage in streams

This model-data archive pertains to a study aimed at the numerical evaluation of the photosensitive tracers as a potential strategy for separating the effects of surface and hyporheic storage zones (SSZs and HSZs, respectively). Separating effects of SSZ and HSZ are important for accurately representing stream function as HSZs and SSZs expose solutes to significantly different biogeochemical conditions. We perform numerical experiments using a multiscale reactive transport model for stream corridors implemented in ATS code, which allows for representing multiple storage zones with their respective travel time distributions and biogeochemistry. For each of the numerical experiment, we provide in this model-data archive python wrapper to drive ATS (forward_model.py), synthetic observation (synthetic_btc.py, BTC_observed.csv), forward model files including ATS input (multiscale_transport.tpl), PFLOTRAN inputs (reactions_channel.tpl, reactions_hz.tpl, reactions_sz.tpl), MCMC (mcmc_run.py), predictive uncertainty (pred_uncert.py, BTCs_simulated_day.csv, BTCs_simulated_night.csv), MCMC outputs (tracer_test_GR.npy, tracer_test_logps.npy, tracer_test_parameters.npy) and Jupyter notebook for post-processing and visualization (post-processing.ipynb). For the denitrification application, ATS input file (denitrification_multisubgrid.tpl), PFLOTRAN input files (denitrification_channel.in, denitrification_hz.in), predictive uncertainty (pred_uncert_denitrification.py, BTCs_simulated_DO.csv, BTCs_simulated_DOC.csv, BTCs_simulated_Nitrate.csv).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Evidence-based indicator approach to guide preliminary environmental impact assessments of hydropower development

Global expansion of hydropower resources has increased in recent years to meet growing energy demands and fill worldwide gaps in electricity supply. However, hydropower induces significant environmental impacts on river ecosystems - impacts that are addressed through environmental impact assessment (EIA) processes. The need for effective EIA processes is increasing as environmental regulations are either stressed in developing countries undertaking rapid expansion of hydropower capacity or time- and resource-intensive in developed countries. Part of the challenge in implementing EIAs lies in reaching a consensus among stakeholders regarding the most important environmental factors as the focus of impact studies. To help address this gap, we developed a weight-of-evidence approach (and toolkit) as a preliminary and coarse assessment of the most relevant impacts of hydropower on primary components of the river ecosystem, as identified using river function indicators. Through a science-based questionnaire and predictive model, users identify which environmental indicators may be impacted during hydropower development as well as those indicators that have the highest levels of uncertainty and require further investigation. Furthermore, an assessment tool visualizes inter-dependent indicator relationships, which help formulate hypotheses about causal relationships explored through environmental studies. We apply these tools to four existing hydropower projects and one hypothetical new hydropower project of varying sizes and environmental contexts. We observed consistencies between the output of our tools and the Federal Energy Regulatory Commission licensing process (inclusive of EIAs) but also important differences arising from holistic scientific evaluations (our toolkit) versus regulatory policies. The tools presented herein are aimed at increasing the efficiency of the EIA processes that engender environmental studies without loss of rigor or transparency of rationale necessary for understanding, considering, and mitigating the environmental consequences of hydropower.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2019

Bayesian Segmentation (BCNN) v.1

Deep learning has been applied with great success to the segmentation of 3D X-Ray Computed Tomography (CT) scans. Establishing the credibility of these segmentations requires uncertainty quantification (UQ) to identify problem areas. Bayesian neural networks (BNNs), which use variational inference to learn the posterior distribution of the neural network weights, have been proposed to incorporate UQ into deep learning models. This software is an implementation of a novel 3D Bayesian convolutional neural network (BCNN) that provides accurate binary segmentations and uncertainty maps for 3D volumes. In particular, the uncertainty maps generated by this BCNN capture continuity and visual gradients, making them interpretable as confidence intervals for segmentation usable in numerical simulations. SAND2019-12927 M Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy's National Nuclear Security Administration under contract DE-NA0003525.

LaBonte, TylerMatthew↗
DOE OSTI2022

Experimental Investigation of a Phase Change Material Charged Finned-Tube Heat Exchanger

Understanding different thermal performance aspects of phase change material heat exchangers (PCM-HX) is critical for designing future energy-efficient thermal energy storage systems. This paper presents an experimental performance analysis of a vertically oriented annular finned-tube PCM-HX in a cylindrical container tested under six different melting and solidification test conditions, varied by heat transfer fluid’s (HTF) inlet temperatures and flow directions. The PCM-HX structure with 20 evenly spaced fins was 3D printed in aluminum, and the PCM-HX container was 285 mm in height. The PCM-HX average porosity was 89.7%, where the PCM mass was 246.4 g. Water served as the HTF, and a PCM with a nominal phase change temperature of 35 °C was selected. Instead of opaque conventional insulation sheets, a combination of air-and-argon-filled see-through insulation cylinders was assembled with the PCM-HX to visualize the phase-change phenomena, while minimizing the uncertainties due to heat loss. In total, there were 78 T-type thermocouples installed in the test section which allows a more precise assessment of thermal mass and heat loss, which are often neglected in similar studies found in the literature. We validated the reliability of the test facility as the repeated test results had negligible deviations under the same test conditions. The energy balances between the estimated theoretical and experimentally calculated PCM storage, considering heat loss and thermal mass, were within ±9% deviation for all test conditions. From the experimental investigations, we made several following observations. It was found that the melting pattern of the finned-tube PCM-HX with small gaps between the container wall and the fins was largely affected by the HTF flow directions, unlike the typical straight annular tube PCM-HX. For effective charging and discharging processes, a combination of upward melting and downward solidification is recommended to shorten the test duration. However, an upward solidification should be considered to minimize the cavity formation between the fins caused by the PCM volume contraction during the solidification process.

25 ENERGY STORAGE↗
DOE OSTI2021

Structure Perception in 3D Point Clouds

Understanding human perception is critical to the design of effective visualizations. The relative benefits of using 2D versus 3D techniques for data visualization is a complex decision space, with varying levels of uncertainty and disagreement in both the literature and in practice. This study aims to add easily reproducible, empirical evidence on the role of depth cues in perceiving structures or patterns in 3D point clouds. We describe a method to synthesize a 3D point cloud that contains a 3D structure, where 2D projections of the data strongly resemble a Gaussian distribution. We performed a within-subjects structure identification study with 128 participants that compared scatterplot matrices (canonical 2D projections) and 3D scatterplots under three types of motion: rotation, xy-translation, and z-translation. We found that users could consistently identify three separate hidden structures under rotation, while those structures remained hidden in the scatterplot matrices and under translation. This work contributes a set of 3D point clouds that provide definitive examples of 3D patterns perceptible in 3D scatterplots under rotation but imperceptible in 2D scatterplots.

data analysis↗
DOE OSTI2021

Structure Perception in 3D Point Clouds: Preprint

Understanding human perception is critical to the design of ef- fective visualizations. The relative benefits of using 2D versus 3D techniques for data visualization is a complex decision space, with varying levels of uncertainty and disagreement in both the liter- ature and in practice. This study aims to add easily reproducible, empirical evidence on the role of depth cues in perceiving structures or patterns in 3D point clouds. We describe a method to synthesize a 3D point cloud that contains a 3D structure, where 2D projec- tions of the data strongly resemble a Gaussian distribution. We performed a within-subjects structure identification study with 128 participants that compared scatterplot matrices (canonical 2D projections) and 3D scatterplots under three types of motion: rota- tion, xy-translation, and z-translation. We found that users could consistently identify three separate hidden structures under ro- tation, while those structures remained hidden in the scatterplot matrices and under translation. This work contributes a set of 3D point clouds that provide definitive examples of 3D patterns per- ceptible in 3D scatterplots under rotation but imperceptible in 2D scatterplots.

data analysis↗
DOE OSTI2016

SCALE Code System

The SCALE Code System is a widely-used modeling and simulation suite for nuclear safety analysis and design that is developed, maintained, tested, and managed by the Reactor and Nuclear Systems Division (RNSD) of Oak Ridge National Laboratory (ORNL). SCALE provides a comprehensive, verified and validated, user-friendly tool set for criticality safety, reactor and lattice physics, radiation shielding, spent fuel and radioactive source term characterization, and sensitivity and uncertainty analysis. Since 1980, regulators, licensees, and research institutions around the world have used SCALE for safety analysis and design. SCALE provides an integrated framework with dozens of computational modules including three deterministic and three Monte Carlo radiation transport solvers that are selected based on the desired solution strategy. SCALE includes current nuclear data libraries and problem-dependent processing tools for continuous-energy (CE) and multigroup (MG) neutronics and coupled neutron-gamma calculations, as well as activation, depletion, and decay calculations. SCALE includes unique capabilities for automated variance reduction for shielding calculations, as well as sensitivity and uncertainty analysis. SCALE’s graphical user interfaces assist with accurate system modeling, visualization of nuclear data, and convenient access to desired results.SCALE 6.2 provides many new capabilities and significant improvements of existing features.New capabilities include:• ENDF/B-VII.1 nuclear data libraries CE and MG with enhanced group structures,• Neutron covariance data based on ENDF/B-VII.1 and supplemented with ORNL data,• Covariance data for fission product yields and decay constants,• Stochastic uncertainty and correlation quantification for any SCALE sequence with Sampler,• Parallel calculations with KENO,• Problem-dependent temperature corrections for CE calculations,• CE shielding and criticality accident alarm system analysis with MAVRIC,• CE depletion with TRITON (T5-DEPL/T6-DEPL),• CE sensitivity/uncertainty analysis with TSUNAMI-3D,• Simplified and efficient LWR lattice physics with Polaris,• Large scale detailed spent fuel characterization with ORIGAMI and ORIGAMI Automator,• Advanced fission source convergence acceleration capabilities with Sourcerer,• Nuclear data library generation with AMPX, and• Integrated user interface with Fulcrum.Enhanced capabilities include:• Accurate and efficient CE Monte Carlo methods for eigenvalue and fixed source calculations,• Improved MG resonance self-shielding methodologies and data,• Resonance self-shielding with modernized and efficient XSProc integrated into most sequences,• Accelerated calculations with TRITON/NEWT (generally 4x faster than SCALE 6.1),• Spent fuel characterization with 1470 new reactor-specific libraries for ORIGEN,• Modernization of ORIGEN (Chebyshev Rational Approximation Method [CRAM] solver, API for high-performance depletion, new keyword input format)• Extension of the maximum mixture number to values well beyond the previous limit of 2147 to ~2 billion,• Nuclear data formats enabling the use of more than 999 energy groups,• Updated standard composition library to provide more accurate use of natural abundances, andvi• Numerous other enhancements for improved usability and stability.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗
DOE OSTI2023

Driving mode analysis—How uncertain functional inputs propagate to an output

Abstract Driving mode analysis elucidates how correlated features of uncertain functional inputs jointly propagate to produce uncertainty in the output of a computation. Uncertain input functions are decomposed into three terms: the mean functions, a zero‐mean driving mode, and zero‐mean residual. The random driving mode varies along a single direction, having fixed functional shape and random scale. It is uncorrelated with the residual, and under linear error propagation, it produces an output variance equal to that of the full input uncertainty. Finally, the driving mode best represents how input uncertainties propagate to the output because it minimizes expected squared Mahalanobis distance amongst competitors. These characteristics recommend interpretation of the driving mode as the single‐degree‐of‐freedom component of input uncertainty that drives output uncertainty. We derive the functional driving mode, show its superiority to other seemingly sensible definitions, and demonstrate the utility of driving mode analysis in an application. The application is the simulation of neutron transport in criticality experiments. The uncertain input functions are nuclear data that describe how Pu reacts to bombardment by neutrons. Visualization of the driving mode helps scientists understand what aspects of correlated functional uncertainty have effects that either reinforce or cancel one another in propagating to the output of the simulation.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Stellar Population Properties in the Stellar Streams around SPRC047

Abstract We have investigated the properties (e.g., age, metallicity) of the stellar populations of a ringlike tidal stellar stream (or streams) around the edge-on galaxy SPRC047 (z= 0.031) using spectral energy distribution (SED) fits to integrated broadband aperture flux densities. We used visual images in six different bands and Spitzer/IRAC 3.6μm data. We have attempted to derive best-fit stellar population parameters (metallicity, age) in three noncontiguous segments of the stream. Due to the very low surface brightness of the stream, we have performed a deconvolution with a Richardson–Lucy–type algorithm of the low spatial resolution 3.6μm IRAC image, thereby reducing the effect of the point-spread function aliasedemissionfrom the bright edge-on central galaxy at the locations of our three stream segments. Our SED fits that used several different star formation (SF) history priors, from an exponentially decaying SF burst to continuous SF, indicate that the age–metallicity–dust degeneracy is not resolved, most likely because of inadequate wavelength coverage and low signal-to-noise ratios of the low surface brightness features. We also discuss how future deep visual–near-infrared observations, combined with absolute flux calibration uncertainties at or below the 1% level, complemented by equally well absolute flux-calibrated observations in ultraviolet and mid-infrared bands, would improve the accuracy of broadband SED fitting results for low surface brightness targets, such as stellar streams around nearby galaxies that are not resolved into stars.

Astronomy & Astrophysics↗