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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 253 records · Page 14
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NASA NTRS2011

Deducing Electron Properties from Hard X-Ray Observations

X-radiation from energetic electrons is the prime diagnostic of flare-accelerated electrons. The observed X-ray flux (and polarization state) is fundamentally a convolution of the cross-section for the hard X-ray emission process(es) in question with the electron distribution function, which is in turn a function of energy, direction, spatial location and time. To address the problems of particle propagation and acceleration one needs to infer as much information as possible on this electron distribution function, through a deconvolution of this fundamental relationship. This review presents recent progress toward this goal using spectroscopic, imaging and polarization measurements, primarily from the Reuven Ramaty High Energy Solar Spectroscopic Imager (RHESSI). Previous conclusions regarding the energy, angular (pitch angle) and spatial distributions of energetic electrons in solar flares are critically reviewed. We discuss the role and the observational evidence of several radiation processes: free-free electron-ion, free-free electron-electron, free-bound electron-ion, photoelectric absorption and Compton backscatter (albedo), using both spectroscopic and imaging techniques. This unprecedented quality of data allows for the first time inference of the angular distributions of the X-ray-emitting electrons and improved model-independent inference of electron energy spectra and emission measures of thermal plasma. Moreover, imaging spectroscopy has revealed hitherto unknown details of solar flare morphology and detailed spectroscopy of coronal, footpoint and extended sources in flaring regions. Additional attempts to measure hard X-ray polarization were not sufficient to put constraints on the degree of anisotropy of electrons, but point to the importance of obtaining good quality polarization data in the future.

Kontar, E. P.↗
NASA NTRS2016

Rough Surfaces: Is the Dark Stuff Just Shadow?: "Who knows what evil lurks in the hearts of men? The shadow knows!"

Remote observations of the surfaces of airless planetary objects are fundamental to inferring the physical structure and compositional makeup of the surface material. A number of forward models have been developed to reproduce the photometric behavior of these surfaces, based on specific, assumed structural properties such as macroscopic roughness and associated shadowing. Most work of this type is applied to geometric albedos, which are affected by complicated effects near zero phase angle that represent only a tiny fraction of the net energy reflected by the object. Other applications include parameter fits to resolved portions of some planetary surface as viewed over a range of geometries. The spherical albedo of the entire object (when it can be determined) captures the net energy balance of the particle more robustly than the geometric albedo. In most treatments involving spherical albedos, spherical albedos and particle phase functions are often treated as if they are independent, neglecting the effects of roughness. In this paper we take a different approach. We note that whatever function captures the phase angle dependence of the brightness of a realistic rough, shadowed, flat surface element relative to that of a smooth granular surface of the same material, it is manifested directly in both the integral phase function and the spherical albedo of the object. We suggest that, where broad phase angle coverage is possible, spherical albedos may be easily corrected for the effects of shadowing using observed (or assumed) phase functions, and then modeled more robustly using smooth-surface regolith radiative transfer models without further imposed (forward-modeled) shadowing corrections. Our approach attributes observed "power law" phase functions of various slope (and "linear" ranges of magnitude-vs.-phase angle) to shadowing, as have others, and goes on to suggest that regolith-model-based inferences of composition based on shadow-uncorrected spherical albedos overestimate the amount of absorbing material contained in the regolith.

geometric albedos↗
DOE OSTI2022

Uncertainty Analysis in Multi‐Sector Systems: Considerations for Risk Analysis, Projection, and Planning for Complex Systems

Abstract Simulation models of multi‐sector systems are increasingly used to understand societal resilience to climate and economic shocks and change. However, multi‐sector systems are also subject to numerous uncertainties that prevent the direct application of simulation models for prediction and planning, particularly when extrapolating past behavior to a nonstationary future. Recent studies have developed a combination of methods to characterize, attribute, and quantify these uncertainties for both single‐ and multi‐sector systems. Here, we review challenges and complications to the idealized goal of fully quantifying all uncertainties in a multi‐sector model and their interactions with policy design as they emerge at different stages of analysis: (a) inference and model calibration; (b) projecting future outcomes; and (c) scenario discovery and identification of risk regimes. We also identify potential methods and research opportunities to help navigate the tradeoffs inherent in uncertainty analyses for complex systems. During this discussion, we provide a classification of uncertainty types and discuss model coupling frameworks to support interdisciplinary collaboration on multi‐sector dynamics (MSD) research. Finally, we conclude with recommendations for best practices to ensure that MSD research can be properly contextualized with respect to the underlying uncertainties.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2019

Cosmic distance inference from purely geometric BAO methods: Linear point standard ruler and correlation function model fitting

Leveraging the baryon acoustic oscillations (BAO) feature present in clustering 2-point statistics, we aim to measure cosmological distances independently of the underlying background cosmological model. However this inference is complicated by late-time nonlinearities that introduce model and tracer dependencies in the clustering correlation function and power spectrum, which must be properly accounted for. With this in mind, we introduce the “purely geometric-BAO,” which provides a rigorous tool to measure cosmological distances without assuming a specific background cosmology. We focus on the 2-point clustering correlation function monopole, and show how to implement such an inference scheme employing two different methodologies: the linear point standard ruler (LP) and correlation-function model-fitting (CF-MF). For the first time we demonstrate how, by means of the CF-MF, we can measure very precisely the sound-horizon/isotropic-volume-distance ratio, r d /DV(z¯), while correctly propagating all the uncertainties. Using synthetic data, we compare the outcomes of the two methodologies, and find that the LP provides up to 50% more precise measurements than the CF-MF. Finally, we test a procedure widely employed in BAO analyses: fitting the 2-point function while fixing the cosmological and the non-linear-damping parameters at fiducial values. We find that this underestimates the distance errors by nearly a factor of 2. Furthermore, we recommend that this practice be reconsidered, whether for parameter determination or model selection.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS2012

Polarimetric Imaging of Large Cavity Structures in the Pre-transitional Protoplanetary Disk around PDS 70: Observations of the Disk

We present high resolution H-band polarized intensity (PI; FWHM = 0."1: 14 AU) and L'-band imaging data (FWHM = 0."11: 15 AU) of the circumstellar disk around the weak-lined T Tauri star PDS 70 in Centaurus at a radial distance of 28 AU (0."2) up to 210 AU (1."5). In both images, a giant inner gap is clearly resolvro for the first time, and the radius of the gap is approx 70 AU. Our data show that the geometric center of the disk shifts by approx 6 AU toward the minor axis. We confirm that the brown dwarf companion candidate to the north of PDS 70 is a background star based on its proper motion. As a result of SED fitting by Monte Carlo radiative transfer modeling, we infer the existence of an optically thick inner disk at a few AU. Combining our observations and modeling, we classify the disk of PDS 70 as a pre-transitional disk. Furthermore, based on the analysis of L'-band imaging data, we put an upper limit mass of companions at approx 30 to approx 50M(sub J) within the gap. Taking account of the presence of the large and sharp gap, we suggest that the gap could be formed by dynamical interactions of sub-stellar companions or multiple unseen giant planets in the gap.

Hashimoto, J.↗
NASA NTRS2012

Polarimetric Imaging of Large Cavity Structures in the Pre-transitional Protoplanetary Disk Around PDS 70: Observations of the Disk

We present high resolution H-band polarized intensity (PI; FWHM = 0."1: 14 AU) and L'-band imaging data (FWHM = 0."11: 15 AU) of the circumstellar disk around the weak-lined T Tauri star PDS 70 in Centaurus at a radial distance of 28 AU (0."2) up to 210 AU (1."5). In both images, a giant inner gap is clearly resolved for the first time, and the radius of the gap is approx.70 AU. Our data show that the geometric center of the disk shifts by approx.6 AU toward the minor axis. We confirm that the brown dwarf companion candidate to the north of PDS 70 is a background star based on its proper motion. As a result of SED fitting by Monte Carlo radiative transfer modeling, we infer the existence of an optically thick inner disk at a few AU. Combining our observations and modeling, we classify the disk of PDS 70 as a pre-transitional disk. Furthermore, based on the analysis of L'-band imaging data, we put an upper limit mass of companions at approx.30 to approx.50MJ within the gap. Taking account of the presence of the large and sharp gap, we suggest that the gap could be formed by dynamical interactions of sub-stellar companions or multiple unseen giant planets in the gap. Subject headings: planetary systems - protoplanetary disks - stars: individual (PDS 70) - stars: pre-main sequence - polarization

Hashimoto, J.↗
NASA NTRS2012

Polarimetric Imaging of Large Cavity Structures in the Pre-transitional Protoplanetary Disk Around PDS 70: Observations of the Disk

We present high-resolution H-band polarized intensity (FWHM=0".1:14AU) and L'-band imaging data(FWHM= 0".11:15 AU) of the circumstellar disk around the weak-lined T Tauri star PDS 70 in Centaurus at a radial distance of 28 AU (0".2) up to 210 AU (1".5). In both images, a giant inner gap is clearly resolved for the first time, and the radius of the gap is approx.70 AU. Our data show that the geometric center of the disk shifts by approx.6 AU toward the minor axis. We confirm that the brown dwarf companion candidate to the north of PDS 70 is a background star based on its proper motion. As a result of spectral energy distribution fitting by Monte Carlo radiative transfer modeling, we infer the existence of an optically thick inner disk at a few AU. Combining our observations and modeling, we classify the disk of PDS 70 as a pre-transitional disk. Furthermore, based on the analysis of L'-band imaging data, we put an upper limit of approx.30 to approx.50 M(sub J) on the mass of companions within the gap. Taking into account the presence of the large and sharp gap, we suggest that the gap could be formed by dynamical interactions of sub-stellar companions or multiple unseen giant planets in the gap. Key words: planetary systems - polarization - protoplanetary disks - stars: individual (PDS 70) - stars: pre-main sequence.

Hashimoto, J.↗
DOE OSTI2024

Application of advanced causal analyses to identify processes governing secondary organic aerosols

Abstract Understanding how different physical and chemical atmospheric processes affect the formation of fine particles has been a persistent challenge. Inferring causal relations between the various measured features affecting the formation of secondary organic aerosol (SOA) particles is complicated since correlations between variables do not necessarily imply causality. Here, we apply a state-of-the-art information transfer measure coupled with the Koopman operator framework to infer causal relations between isoprene epoxydiol SOA (IEPOX-SOA) and different chemistry and meteorological variables derived from detailed regional model predictions over the Amazon rainforest. IEPOX-SOA represents one of the most complex SOA formation pathways and is formed by the interactions between natural biogenic isoprene emissions and anthropogenic emissions affecting sulfate, acidity and particle water. Since the regional model captures the known relations of IEPOX-SOA with different chemistry and meteorological features, their simulated time series implicitly include their causal relations. We show that our causal model successfully infers the known major causal relations between total particle phase 2-methyl tetrols (the dominant component of IEPOX-SOA over the Amazon) and input features. We provide the first proof of concept that the application of our causal model better identifies causal relations compared to correlation and random forest analyses performed over the same dataset. Our work has tremendous implications, as our methodology of causal discovery could be used to identify unknown processes and features affecting fine particles and atmospheric chemistry in the Earth’s atmosphere.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Genetic programming for understanding cognitive biases that generate polarization in social networks

Recent studies have applied agent-based models to infer human-interpretable explanations of individual-scale behaviors that generate macro-scale patterns in complex social systems. Genetic programming has proven to be an ideal explainable AI tool for this purpose, where primitives may be expressed in an interpretable fashion and assembled into agent rules. Evolutionary model discovery (EMD) is a tool that combines genetic programming and random forest feature importance analysis, to infer individual-scale, human-interpretable explanations from agent-based models. We deploy EMD to investigate the cognitive biases behind the emergence of ideological polarization within a population. An agent-based model is developed to simulate a social network, where agents are able to create or sever links with one another, and update an internal ideological stance based on their neighbors' stances. Agent rules govern these actions and constitute of cognitive biases. A set of 7 cognitive biases are included as genetic program primitives in the search for rules that generate hyper-polarization among the population of agents. We find that heterogeneity in cognitive biases is more likely to generate polarized social networks. Highly polarized social networks are likely to emerge when individuals with confirmation bias are exposed to those with either attentional bias, egocentric bias, or cognitive dissonance.

Gunaratne, Chathika↗
DOE OSTI2019

FPGAs as a Service to Accelerate Machine Learning Inference [PowerPoint]

Large-scale particle physics experiments face challenging demands for high-throughput computing resources both now and in the future. New heterogeneous computing paradigms on dedicated hardware with increased parallelization, such as Field Programmable Gate Arrays (FPGAs), offer exciting solutions with large potential gains. The growing applications of machine learning algorithms in particle physics for simulation, reconstruction, and analysis are naturally deployed on such platforms. We demonstrate that the acceleration of machine learning inference as a web service represents a heterogeneous computing solution for particle physics experiments that requires minimal modification to the current computing model. As examples, we retrain the ResNet-50 convolutional neural network to demonstrate state-of-the-art performance for top quark jet tagging at the LHC, and apply a ResNet-50 model with transfer learning for neutrino event classification. Using Project Brainwave b y Microsoft to accelerate the ResNet-50 image classification model, we achieve average inference times of 60 (10) milliseconds with our experimental physics software framework using Brainwave as a cloud (edge or on-premises) service, representing an improvement by a factor of approximately 30 (175) in model inference latency over traditional CPU inference in current experimental hardware. A single FPGA service accessed by many CPUs achieves a throughput of 600--700 inferences per second using an image batch of one, comparable to large batch-size GPU throughput and significantly better than small batch-size GPU throughput. Deployed as an edge or cloud service for the particle physics computing model, coprocessor accelerators can have a higher duty cycle and are potentially much more cost-effective.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
NASA NTRS2000

Latest Word on Retreat of the West Antarctic Ice Sheet

The West Antarctic ice sheet during the Last Glacial Maximum (LGM) is estimated to have been three times its present volume and to have extended close to the edge of the continental shelf Holocene retreat of this ice sheet in the Ross Sea began between 11,000 and 12,000 years ago. This history implies an average contribution of this ice sheet to sea level of 0.9 mm/a. Evidence of dateable past grounding line positions in the Ross sector are broadly consistent with a linear retreat model. However, inferred rates of retreat for some of these grounding line positions are not consistent with a linear retreat model. More rapid retreat approximately 7600 years ago and possible near-stability in the Ross Sea sector at present suggest a slow rate of initial retreat followed by a more rapid-than-average retreat during the late Holocene, returning to a near-zero rate of retreat currently. This model is also consistent with the mid-Holocene high stand observations of eustatic sea level. Recent compilation of Antarctic bed elevations (BEDMAP) illustrates that the LGM and present grounding lines occur in the shallowest waters, further supporting the model of a middle phase of rapid retreat bracketed by an older and a more recent phase of modest retreat. Extension of these hypotheses into the future make subsequent behavior of the West Antarctic ice sheet more difficult to predict but suggest that if it loses its hold on the present shallow bed, the final retreat of the ice sheet could be very rapid.

Bindschadler, R.↗
DOE OSTI2019

FPGA-Accelerated Machine Learning Inference as a Service for Particle Physics Computing

Large-scale particle physics experiments face challenging demands for high-throughput computing resources both now and in the future. New heterogeneous computing paradigms on dedicated hardware with increased parallelization, such as Field Programmable Gate Arrays (FPGAs), offer exciting solutions with large potential gains. The growing applications of machine learning algorithms in particle physics for simulation, reconstruction, and analysis are naturally deployed on such platforms. We demonstrate that the acceleration of machine learning inference as a web service represents a heterogeneous computing solution for particle physics experiments that potentially requires minimal modification to the current computing model. As examples, we retrain the ResNet-50 convolutional neural network to demonstrate state-of-the-art performance for top quark jet tagging at the LHC and apply a ResNet-50 model with transfer learning for neutrino event classification. Using Project Brainwave by Microsoft to accelerate the ResNet-50 image classification model, we achieve average inference times of 60 (10) ms with our experimental physics software framework using Brainwave as a cloud (edge or on-premises) service, representing an improvement by a factor of approximately 30 (175) in model inference latency over traditional CPU inference in current experimental hardware. A single FPGA service accessed by many CPUs achieves a throughput of 600–700 inferences per second using an image batch of one, comparable to large batch-size GPU throughput and significantly better than small batch-size GPU throughput. Deployed as an edge or cloud service for the particle physics computing model, coprocessor accelerators can have a higher duty cycle and are potentially much more cost-effective.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2023

Tencoder: tensor-product encoder-decoder architecture for predicting solutions of PDEs with variable boundary data

It is widely hoped that artificial intelligence will boost data-driven surrogate models in science and engineering. However, fundamental spatial aspects of AI surrogate models remain under-studied. We investigate the ability of neural-network surrogate models to predict solutions to PDEs under variable boundary values. We do not wish to retrain the model when the boundary values change but to make them inputs to the model and infer the solution of the PDE under those boundary conditions. Such a capability is essential to making AI-based surrogate models practically useful. While simple feedforward networks are used for one-dimensional (1D) Poisson equation, an encoder-decoder architecture with a tensor-product layer is developed for the two-dimensional Poisson equation posed on a rectangular domain. We show that it is indeed possible to infer solutions to PDEs from variable boundary data using neural networks in this relatively simple setting, and point to future directions.

Kashi, Aditya↗
NASA NTRS2017

On Tidal Inference in the Diurnal Band

Standard methods of tidal inference should be revised to account for a known resonance that occurs mostly within the K(sub 1) tidal group in the diurnal band. The resonance arises from a free rotational mode of Earth caused by the fluid core. In a set of 110 bottom-pressure tide stations, the amplitude of the P(sub 1) tidal constituent is shown to be suppressed relative to K(sub 1), which is in good agreement with the resonance theory. Standard formulas for the K(sub 1) nodal modulation remain essentially unaffected. Two examples are given of applications of the refined inference methodology: one with monthly tide gauge data and one with satellite altimetry. For some altimeter-constrained tide models, an inferred P(sub 1) constituent is found to be more accurate than a directly determined one.

Ray, R. D.↗
NASA NTRS2017

BrO and Inferred Bry Profiles over the Western Pacific: Relevance of Inorganic Bromine Sources and a Bry Minimum in the Aged Tropical Tropopause Layer

We report measurements of bromine monoxide (BrO) and use an observationally constrained chemical box model to infer total gas-phase inorganic bromine (Br(sub y)) over the tropical western Pacific Ocean (tWPO) during the CONTRAST field campaign (January-February 2014). The observed BrO and inferred Bry profiles peak in the marine boundary layer (MBL), suggesting the need for a bromine source from sea-salt aerosol (SSA), in addition to organic bromine (CBry ). Both profiles are found to be C-shaped with local maxima in the upper free troposphere (FT). The median tropospheric BrO vertical column density (VCD) was measured as 1.6 x 10(exp 13) molec cm(exp -2), compared to model predictions of 0.9 x 10(exp 13) molec cm(exp -2) in GEOS-Chem (CBr(sub y) but no SSA source), 0.4 x 10(exp 13) molec cm(exp -2) in CAM-Chem (CBr(sub y) and SSA), and 2.1 x 10(exp 13) molec cm(exp -2) in GEOS-Chem (CBry and SSA). Neither global model fully captures the Cshape of the Br(sun y) profile. A local Br(sub y) maximum of 3.6 ppt (2.9-4.4 ppt; 95% confidence interval, CI) is inferred between 9.5 and 13.5 km in air masses influenced by recent convective outflow. Unlike BrO, which increases from the convective tropical tropopause layer (TTL) to the aged TTL, gas-phase Br(sub y) decreases from the convective TTL to the aged TTL. Analysis of gas-phase Br(sub y) against multiple tracers (CFC-11, H2O/O3 ratio, and potential temperature) reveals a Br(sub y) minimum of 2.7 ppt (2.3-3.1 ppt; 95% CI) in the aged TTL, which agrees closely with a stratospheric injection of 2.6 +/- 0.6 ppt of inorganic Br(sub y) (estimated from CFC-11 correlations), and is remarkably insensitive to assumptions about heterogeneous chemistry. Bry increases to 6.3 ppt (5.6-7.0 ppt; 95% CI) in the stratospheric "middleworld" and 6.9 ppt (6.5-7.3 ppt; 95% CI) in the stratospheric "overworld". The local Br(sub y) minimum in the aged TTL is qualitatively (but not quantitatively) captured by CAM-Chem, and suggests a more complex partitioning of gas-phase and aerosol Br(sub y) species than previously recognized. Our data provide corroborating evidence that inorganic bromine sources (e.g., SSA-derived gas-phase Br(sub y) ) are needed to explain the gas-phase Br(sub y) budget in the upper free troposphere and TTL. They are also consistent with observations of significant bromide in Upper Troposphere-Lower Stratosphere aerosols. The total Br(sub y) budget in the TTL is currently not closed, because of the lack of concurrent quantitative measurements of gas-phase Br(sub y) species (i.e., BrO, HOBr, HBr, etc.) and aerosol bromide. Such simultaneous measurements are needed to (1) quantify SSA-derived Br(sub y) in the upper FT, (2) test Br(sub y) partitioning, and possibly explain the gas-phase Br(sub y) minimum in the aged TTL, (3) constrain heterogeneous reaction rates of bromine, and (4) account for all of the sources of Br(sub y) to the lower stratosphere.

Atmospheric Chemistry; bromine; troposphere; CONTR↗
DOE OSTI2024

Uncertainty quantification of material parameters in modeling coupled metal and high explosive experiments

Experiments involving the coupling of metal and high explosives (HE) are of notable defense-related interest, and we seek to refine the uncertainty quantification associated with models of such experiments. In particular, our focus is on how uncertainty related to the metal constitutive model challenges our ability to infer high explosive model parameters when analyzing focused science experiments. We consider three focused experiments involving an HE accelerating metal: small plate tests with tantalum/LX-14 and tantalum/LX-17 pairings as well as a tantalum/LX-17 cylinder test. For all three models, we perform sensitivity analysis to ascertain the influence of metal strength on the coupled experimental response. Moreover, we calibrate each model in a Bayesian setting and study the quantification of metal strength on the inference of the HE parameters. Based on our results, we offer guidance for future metal/HE experiments.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Planck 2018 results. VI. Cosmological parameters

In this paper, we present cosmological parameter results from the final full-mission Planck measurements of the cosmic microwave background (CMB) anisotropies, combining information from the temperature and polarization maps and the lensing reconstruction. Compared to the 2015 results, improved measurements of large-scale polarization allow the reionization optical depth to be measured with higher precision, leading to significant gains in the precision of other correlated parameters. Improved modelling of the small-scale polarization leads to more robust constraints on many parameters, with residual modelling uncertainties estimated to affect them only at the 0.5 σ level. We find good consistency with the standard spatially-flat 6-parameter ΛCDM cosmology having a power-law spectrum of adiabatic scalar perturbations (denoted “base ΛCDM” in this paper), from polarization, temperature, and lensing, separately and in combination. A combined analysis gives dark matter density Ω c h 2 = 0.120 ± 0.001, baryon density Ω b h 2 = 0.0224 ± 0.0001, scalar spectral index n s = 0.965 ± 0.004, and optical depth τ = 0.054 ± 0.007 (in this abstract we quote 68% confidence regions on measured parameters and 95% on upper limits). The angular acoustic scale is measured to 0.03% precision, with 100 θ * = 1.0411 ± 0.0003. These results are only weakly dependent on the cosmological model and remain stable, with somewhat increased errors, in many commonly considered extensions. Assuming the base-ΛCDM cosmology, the inferred (model-dependent) late-Universe parameters are: Hubble constant H 0 = (67.4 ± 0.5) km s –1 Mpc –1 ; matter density parameter Ω m = 0.315 ± 0.007; and matter fluctuation amplitude σ 8 = 0.811 ± 0.006. We find no compelling evidence for extensions to the base-ΛCDM model. Combining with baryon acoustic oscillation (BAO) measurements (and considering single-parameter extensions) we constrain the effective extra relativistic degrees of freedom to be N eff = 2.99 ± 0.17, in agreement with the Standard Model prediction N eff = 3.046, and find that the neutrino mass is tightly constrained to Σ m ν < 0.12 eV. The CMB spectra continue to prefer higher lensing amplitudes than predicted in base ΛCDM at over 2 σ , which pulls some parameters that affect the lensing amplitude away from the ΛCDM model; however, this is not supported by the lensing reconstruction or (in models that also change the background geometry) BAO data. The joint constraint with BAO measurements on spatial curvature is consistent with a flat universe, Ω K = 0.001 ± 0.002. Also combining with Type Ia supernovae (SNe), the dark-energy equation of state parameter is measured to be w 0 = –1.03 ± 0.03, consistent with a cosmological constant. We find no evidence for deviations from a purely power-law primordial spectrum, and combining with data from BAO, BICEP2, and Keck Array data, we place a limit on the tensor-to-scalar ratio r 0.002 < 0.06. Standard big-bang nucleosynthesis predictions for the helium and deuterium abundances for the base-ΛCDM cosmology are in excellent agreement with observations. The Planck base-ΛCDM results are in good agreement with BAO, SNe, and some galaxy lensing observations, but in slight tension with the Dark Energy Survey’s combined-probe results including galaxy clustering (which prefers lower fluctuation amplitudes or matter density parameters), and in significant, 3.6 σ , tension with local measurements of the Hubble constant (which prefer a higher value). Simple model extensions that can partially resolve these tensions are not favoured by the Planck data.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

Machine learning enhanced predictions of ICRF heating: Overcoming numerical limitations via data curation

In this work, we present the development of robust surrogate models for Ion Cyclotron Range of Frequencies (ICRF) and High-Harmonic Fast Wave (HHFW) heating predictions in fusion plasmas. Building upon our previous efforts to achieve real-time capable models, we identify the cause of the outliers found using TORIC in certain HHFW heating scenarios. The outliers are observed to be spurious ion Bernstein wave (IBW)-like modes caused by a wavelength control algorithm designed to address challenging scenarios with high perpendicular wavenumbers. The effect arises from the modulation in the perpendicular susceptibility, which can induce sign reversal and IBW-like propagation for scenarios featuring normalized ion Larmor radius λ i ≫ 1. We use TORIC with this algorithm disabled to generate a novel HHFW-NSTX database that is free of outliers. Surrogate models trained on this database, including Random Forest Regressor (RFR), Multi-Layer Perceptrons, and Gaussian Process Regressors (GPR), demonstrate the ability to accurately predict HHFW heating profiles, with regression scores of R 2 ∈[0.93−0.99]. Additionally we demonstrate that it is possible to generalize predictions beyond training data by the use of both RFR and GPR models, enabling the prediction of scenarios previously limited to the original model. GPR models also provide uncertainty quantification, offering insights into model confidence. This work introduces a comprehensive Verification, Validation, and Uncertainty Quantification methodology for surrogate modeling, applicable not only to ICRF heating but also to other RF heating challenges and fusion physics problems. Beyond accelerated inference, these models show effective extrapolation capabilities, providing an alternative for addressing numerical challenges.

Artificial neural networks↗