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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 OSTI2019

Ellipticity of brightest cluster galaxies as tracer of halo orientation and weak-lensing mass bias

Weak-lensing measurements of the masses of galaxy clusters are commonly based on the assumption of spherically symmetric density profiles. Yet, the cold dark matter model predicts the shapes of dark matter haloes to be triaxial. Halo triaxiality, and the orientation of the major axis with respect to the line of sight, are expected to be the leading cause of intrinsic scatter in weak-lensing mass measurements. The shape of central cluster galaxies (brightest cluster galaxies; BCGs) is expected to follow the shape of the dark matter halo. In this work we investigate the use of BCG ellipticity as predictor of the weak-lensing mass bias in individual clusters compared to the mean. Using weak-lensing masses $M^{\rm WL}_{500}$ from the Weighing the Giants project, and M 500 derived from gas masses as low-scatter mass proxy, we find that, on average, the lensing masses of clusters with the roundest/most elliptical 25 percent of BCGs are biased ~20 percent high/low compared to the average, as qualitatively predicted by the cold dark matter model. For cluster cosmology projects utilizing weak-lensing mass estimates, the shape of the BCG can thus contribute useful information on the effect of orientation bias in weak-lensing mass estimates as well as on cluster selection bias.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2017

Joule Heating Effect on Field-Free Magnetization Switching by Spin-Orbit Torque in Exchange-Biased Systems

Switching of magnetization via spin-orbit torque provides an efficient alternative for nonvolatile memory and logic devices. However, to achieve deterministic switching of perpendicular magnetization, an external magnetic field collinear with the current is usually required, which makes these devices inappropriate for practical applications. As such, in this work, we examine the current-induced magnetization switching in a perpendicularly magnetized exchange-biased Pt/CoFe/IrMn system. A magnetic field annealing technique is used to introduce in-plane exchange biases, which are quantitatively characterized. Under proper conditions, field-free current-driven switching is achieved.We study the Joule heating effect, and we show how it can decrease the in-plane exchange bias and degrade the field-free switching. Furthermore, we discuss that the exchange-bias training effect can have similar effects.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2022

Mitigating foreground bias to the CMB lensing power spectrum for a CMB-HD survey

A promising way to measure the distribution of matter on small scales (k~10 h Mpc –1 ) is to use gravitational lensing of the cosmic microwave background (CMB). CMB-HD, a proposed high-resolution, low-noise millimeter survey over half the sky, can measure the CMB lensing auto spectrum on such small scales enabling measurements that can distinguish between a cold dark matter (CDM) model and alternative models designed to solve problems with CDM on small scales. However, extragalactic foregrounds can bias the CMB lensing auto spectrum if left untreated. Here, we present a foreground mitigation strategy that provides a path to reduce the bias from two of the most dominant foregrounds, the thermal Sunyaev-Zel’dovich effect and the cosmic infrared background. Given the level of realism included in our analysis, we find that the thermal Sunyaev-Zel’dovich alone and the cosmic infrared background alone bias the lensing auto spectrum by 0.6σ and 1.1σ, respectively, in the lensing multipole range of Lϵ[5000,20000] for a CMB-HD survey; combined these foregrounds yield a bias of only 1.3σ. Including these foregrounds, we also find that a CMB-HD survey can distinguish between a CDM model and a 10 –22 eV fuzzy dark matter model at the 5σ level. These results provide an important step in demonstrating that foreground contamination can be sufficiently reduced to enable a robust measurement of the small-scale matter power spectrum with CMB-HD.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2024

Photometric redshifts and intrinsic alignments: Degeneracies and biases in the 3 × 2 pt analysis

We present a systematic study of cosmological parameter bias in weak lensing and large-scale structure analyses for upcoming imaging surveys induced by the interplay of intrinsic alignments (IA) and photometric redshift (photo-z) model misspecification error. We first examine the degeneracies between the parameters of the tidal alignment–tidal torquing (TATT) model for IA and of a photo-z model including a mean shift ( Δ z ¯ ) and variance ( σ z ) for each tomographic bin of lenses and sources, under a variety of underlying true IA behaviors. We identify strong degeneracies between: (1) the redshift scaling of the tidal alignment amplitude and the mean shift and variances of source bins, (2) the redshift scaling of the tidal torquing amplitude and the variance of the lowest- z source bin, and (3) the IA source density weighting and the mean shift and variance of several source bins. We then use this information to guide our exploration of the level of cosmological parameter bias which can be induced given incorrect modeling of IA, photo-z, or both. We find that marginalizing over all the parameters of TATT is generally sufficient to preclude cosmological parameter bias in the scenarios we consider. However, this does not necessarily mean that IA and photo-z parameters are themselves unbiased, nor does it mean that the best-fit model is a good fit to the data. We also find scenarios where the inferred parameters produce χ d . o . f . 2 values indicative of a good fit but cosmological parameter bias is significant, particularly when the IA source density weighting parameter is not marginalized over. Published by the American Physical Society 2024

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2018

Scale-dependent bias and bispectrum in neutrino separate universe simulations

Cosmic background neutrinos have a large velocity dispersion, which causes the evolution of long-wavelength density perturbations to depend on scale. As such, this scale-dependent growth leads to the well-known suppression in the linear theory matter power spectrum that is used to probe neutrino mass. In this paper, we study the impact of long-wavelength density perturbations on small-scale structure formation. By performing separate universe simulations where the long-wavelength mode is absorbed into the local expansion, we measure the responses of the cold dark matter (CDM) power spectrum and halo mass function, which correspond to the squeezed-limit bispectrum and halo bias. We find that the scale-dependent evolution of the long-wavelength modes causes these quantities to depend on scale and provide simple expressions to model them in terms of scale and the amount of massive neutrinos. Importantly, this scale-dependent bias reduces the suppression in the linear halo power spectrum due to massive neutrinos by 13% and 26% for objects of bias b ¯ = 2 and b ¯ &#187 1 , respectively. We demonstrate with high statistical significance that the scale-dependent halo bias cannot be modeled by the CDM and neutrino density transfer functions at the time when the halos are identified. This reinforces the importance of the temporal nonlocality of structure formation, especially when the growth is scale dependent.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2019

Programmable bias field observed in graded ferromagnetic semiconductor films with broken symmetry

We report on the observation of a field-induced magnetic bias effect in a Ga 0.94 Mn 0.06 As 1-y P y thin film with digitally graded phosphorus content. Although phosphorus concentration in the sample is changed in steps from y approximate to 0.03 to y approximate to 0.28, the magnetometry and magnetotransport data display a coherent magnetic response typical for single-layer in-plane magnetized films with cubic and weak [110] uniaxial anisotropy. Unexpectedly, low-temperature planar Hall resistance loops exhibit remarkable asymmetry tunable by application of strong in-plane initial magnetic field H Ini . We discuss this unusual memory effect, resembling the magnetization asymmetry in the exchange biased magnetic bilayers, in terms of a unidirectional bias field H b induced by H Ini . We show that such bias field defines the delayed or accelerated nucleation of domains with chiral domain walls performing the magnetization reversal.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Large Rashba spin splittings in bulk and monolayer of BiAs

There is great interest in developing new materials with Rashba split bands near the Fermi level for spintronics. Here, using first-principles calculations, we predict BiAs as a semiconductor with large Rashba splitting in bulk and monolayer forms. Bulk BiAs has a layered crystal structure with two atoms in a rhombohedral primitive cell, derived from the structure of the parent Bi and As elemental phases. It is a narrow band gap semiconductor, and it shows a combination of Rashba and Dresselhaus spin splitting with a characteristic spin texture around the L point in the Brillouin zone of the hexagonal conventional unit cell. It has sizable Rashba energies and Rashba coupling constants in the valence and conduction bands at the band edges. The 2D monolayer of BiAs has a much larger band gap at Γ, with a circular spin texture characteristic of a pure Rashba effect. The Rashba energy and Rashba coupling constant of monolayer BiAs are large compared to other known 2D materials and rapidly increase under biaxial tensile strain.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Nonequilibrium dynamics of two-level systems directly after cryogenic alternating bias

Two-level systems (TLSs) are a dominant decoherence mechanism in superconducting qubits, microwave kinetic inductance detectors, and quantum dots. TLSs are tunneling states commonly found in amorphous materials and interfaces, which interact electromechanically, leading to energy loss. Fluctuations in the frequency of the TLSs are a significant problem for qubit operation and force recalibration every few hours. In this study, we probe the effect of alternating bias at cryogenic temperatures on TLSs in a purpose-built LC oscillator. When an in situ alternating bias is applied, the steady-state spectra of the TLSs disappear. Spectroscopy reveals transient behavior, in which the TLS frequency fluctuates on the order of minutes. Thermal cycling above 10 K reverses these effects, restoring the TLS spectrum to its original state. Importantly, the intrinsic loss tangent of the LC oscillator remains unchanged before and after the application of the alternating bias. We propose that the disappearance of the steady-state spectrum is caused by nonequilibrium energy buildup from strain in the oxide film introduced by the pulsed voltage-bias sequence. Understanding this nonequilibrium energy could shed light on TLS fluctuations and reduce the calibration requirements for qubits.

general physics↗
DOE OSTI2019

Potential-Induced Degradation Depends on Leakage Current and Light/Electrical Bias in Cu(In,Ga)Se 2 Devices

Potential-induced degradation (PID) of encapsulated Cu(In,Ga)Se 2 (CIGS) solar cells was researched for different substrates: soda-lime glass (SLG), SLG with an Al 2 O 3 diffusion barrier, and borosilicate glass (BSG). The Al 2 O 3 and BSG samples increased the time to PID failure by 5x and 30x, respectively. The relationship among PID and coulombs transferred from the substrate to the device through leakage current differed depending on the sample type. When the (unrepresentative) dark PID stress test was interrupted to characterize devices with light/electrical bias, PID was partially reversed. Furthermore, applying high voltage stress to open-circuit devices was less damaging, relative to forward-biased and short-circuited cells. Light/electrical bias may therefore reduce PID by driving alkali metal cations away from the p-n junction to diminish their degrading effects. These results show that device light/electrical bias should be carefully controlled during CIGS PID testing, as it affects PID in addition to leakage current.

14 SOLAR ENERGY↗
DOE OSTI2015

Secondary Ion Mass Spectrometry Bias on Isotope Ratios in Dolomite-Ankerite, Part II: δ 13 C Matrix Effects

Here, this study is Part II of a series that documents the development of a suite of calibration reference materials for in situ SIMS analysis of stable isotope ratios in Ca-Mg-Fe carbonates. Part I explored the effects of Fe 2+ substitution on SIMS δ18O bias measured from the dolomite–ankerite solid solution series [CaMg(CO3)2–CaFe(CO 3 ) 2 ], whereas this complementary work explores the compositional dependence of SIMS δ 13 C bias (calibrated range: Fe# = 0.004–0.789, where Fe# = molar Fe/(Mg+Fe)). Under routine operating conditions for carbonate δ 13 C analysis at WiscSIMS (CAMECA IMS 1280), the magnitude of instrumental bias increased exponentially by 2.5–5.5‰ (session-specific) with increasing Fe-content in the dolomite structure, but appeared insensitive to minor Mn substitution [< 2.6 mole % Mn/(Ca+Mg+Fe+Mn)]. The compositional dependence of bias (i.e., the matrix effect) was expressed using the Hill equation, yielding calibration residual values ≤ 0.3‰ relative to CRM NBS-19 for eleven carbonate reference materials (6-μm-diameter spot size measurements). Based on the spot-to-spot repeatability of a drift monitor material that ‘bracketed’ each set of ten sample-spot analyses, the analytical precision was ± 0.6–1.2‰ (2s, standard deviations). The analytical uncertainty for individual sample analyses was approximated by combining the precision and calibration residual values (propagated in quadrature), suggesting an uncertainty of ± 1.0–1.5‰ (2s).

58 GEOSCIENCES↗
DOE OSTI2016

Comparative study of ITO and TiN fabricated by low-temperature RF biased sputtering

Radio frequency (RF) biasing induced by a second plasma source at the substrate is applied to low-temperature sputtering processes for indium tin oxide (ITO) and titanium nitride (TiN) thin films. Investigations on crystal structure and surface morphology show that RF-biased substrate plasma processes result in a changed growth regime with different grain sizes and orientations than those produced by processes without a substrate bias. The influence of the RF bias is shown comparatively for reactive RF-sputtered ITO and reactive direct-current-sputtered TiN. The ITO layers exhibit an improved electrical resistivity of 0.5 mΩ cm and an optical absorption coefficient of 0.5 × 10{sup 4 }cm{sup −1} without substrate heating. Room-temperature sputtered TiN layers are deposited that possess a resistivity (0.1 mΩ cm) of 3 orders of magnitude lower than, and a density (5.4 g/cm{sup 3}) up to 45% greater than, those obtained from layers grown using the standard process without a substrate plasma.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Angiotensin and biased analogs induce structurally distinct active conformations within a GPCR

Biased agonists of G protein–coupled receptors (GPCRs) preferentially activate a subset of downstream signaling pathways. In this work, we present crystal structures of angiotensin II type 1 receptor (AT1R) (2.7 to 2.9 angstroms) bound to three ligands with divergent bias profiles: the balanced endogenous agonist angiotensin II (AngII) and two strongly β-arrestin–biased analogs. Compared with other ligands, AngII promotes more-substantial rearrangements not only at the bottom of the ligand-binding pocket but also in a key polar network in the receptor core, which forms a sodium-binding site in most GPCRs. Divergences from the family consensus in this region, which appears to act as a biased signaling switch, may predispose the AT1R and certain other GPCRs (such as chemokine receptors) to adopt conformations that are capable of activating β-arrestin but not heterotrimeric G q protein signaling.

Wingler, Laura M. (ORCID:0000000236885088)↗
DOE OSTI2016

Impacts of Model Bias on the Climate Change Signal and Effects of Weighted Ensembles of Regional Climate Model Simulations: A Case Study over Southern Québec, Canada

This study examined the impact of model biases on climate change signals for daily precipitation and for minimum and maximum temperatures. Through the use of multiple climate scenarios from 12 regional climate model simulations, the ensemble mean, and three synthetic simulations generated by a weighting procedure, we investigated intermodel seasonal climate change signals between current and future periods, for both median and extreme precipitation/temperature values. A significant dependence of seasonal climate change signals on the model biases over southern Québec in Canada was detected for temperatures, but not for precipitation. This suggests that the regional temperature change signal is affected by local processes. Seasonally, model bias affects future mean and extreme values in winter and summer. In addition, potentially large increases in future extremes of temperature and precipitation values were projected. For three synthetic scenarios, systematically less bias and a narrow range of mean change for all variables were projected compared to those of climate model simulations. In addition, synthetic scenarios were found to better capture the spatial variability of extreme cold temperatures than the ensemble mean scenario. Finally, these results indicate that the synthetic scenarios have greater potential to reduce the uncertainty of future climate projections and capture the spatial variability of extreme climate events.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Evaluating Probabilistic Deep Learning Methods for Uncertainty Quantification of Precipitation Bias Correction

Climate models often exhibit biases in their precipitation predictions, particularly underestimating high-intensity events and overestimating low precipitation. Deep learning approaches offer promising solutions, but their epistemic uncertainty associated with a deep learning–based bias correction method has not previously been quantified for reliable downstream climate impact studies. While methods for capturing the epistemic uncertainty in deep learning frameworks exist, there is currently no consensus on the best method. In this work, we compare three uncertainty quantification (UQ) methods—Deep Ensembles (DEns), Monte Carlo Dropout (MCD), and Flipout—by assessing the reliability of their uncertainty estimates using standard measures such as sharpness and calibration. These UQ methods are applied to an existing deep learning precipitation bias correction model known as UFNet: a coupled U-Net and fully connected neural network. The methods utilized to assess the models’ uncertainties are 1) calibration, which ensures that the expected probabilities of the model align with reality and 2) sharpness, which is a measure of the precision of the model’s probabilistic predictions. Of the three UQ methods evaluated, the DEns and MCD methods demonstrated the best-calibrated performance (expected calibration error of 0.36 and 0.35, respectively), compared to Flipout (0.58). In contrast, Flipout had the sharpest predictions and the highest metric performance in bias correcting precipitation—especially for higher-order moments such as kurtosis with a spatial correlation of 72% compared to 32% and 55% spatial correlation for DEns and MCD, respectively. Of the three UQ methods, MCD was found to be the most suitable method for UQ purposes based on its calibration, sharpness, and computational requirements.

Bayesian methods↗
DOE OSTI2019

Evaluating the Bias of South China Sea Summer Monsoon Precipitation Associated with Fast Physical Processes Using a Climate Model Hindcast Approach

Abstract The present study aims to identify the precipitation bias associated with the interactions among fast physical processes in the Community Atmospheric Model, version 5 (CAM5), during the abrupt onset of the South China Sea (SCS) summer monsoon, a key precursor of the overall East Asia summer monsoon (EASM). The multiyear hindcast approach is utilized to obtain the well-constrained synoptic-scale horizontal circulation each year during the onset period from the years 1998 to 2012. In the pre-onset period, the ocean precipitation over the SCS is insufficiently suppressed in CAM5 hindcasts and thus weaker land–ocean precipitation contrasts. This is associated with the weaker and shallower convection simulated over the surrounding land, producing weaker local circulation within the SCS basin. In the post-onset period, rainfall of the organized convection over the Philippine coastal ocean is underestimated in the hindcasts, with overestimated upper-level heating. These biases are further elaborated as the underrepresentation of the convection diurnal cycle and coastal convection systems, as well as the issue of precipitation sensitivity to environmental moisture during the SCS onset period. The biases identified in hindcasts are consistent with the general bias of the EASM in the climate simulation of CAM5. The current results highlight that the appropriate representation of land–ocean–convection interactions over coastal areas can potentially improve the simulation of seasonal transition over the monsoon regions.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2014

Collaborative Project: The problem of bias in defining uncertainty in computationally enabled strategies for data-driven climate model development

The objective of the project is to develop strategies for better representing scientific sensibilities within statistical measures of model skill that then can be used within a Bayesian statistical framework for data-driven climate model development and improved measures of model scientific uncertainty. One of the thorny issues in model evaluation is quantifying the effect of biases on climate projections. While any bias is not desirable, only those biases that affect feedbacks affect scatter in climate projections. The effort at the University of Texas is to analyze previously calculated ensembles of CAM3.1 with perturbed parameters to discover how biases affect projections of global warming. The hypothesis is that compensating errors in the control model can be identified by their effect on a combination of processes and that developing metrics that are sensitive to dependencies among state variables would provide a way to select version of climate models that may reduce scatter in climate projections. Gabriel Huerta was responsible for leading the effort to develop statistical methods for evaluating these field dependencies. The UT effort will incorporate these developments into MECS, which is a set of python scripts being developed at the University of Texas for managing the workflow associated with data-driven climate model development over HPC resources. We regularly consult with Peter Gleckler (LLNL), Mark Taylor (SNL), and Rich Neale (NCAR) to provide additional insight into issues concerning CAM5 model development and multi-model evaluation of the AR5 archive. This grant was originally assigned to Indiana University where both the PI (Huerta) and the Postdoc (Nosedal) worked on the project from March 1, 2012-March 31, 2013.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2016

Collaborative Project: The problem of bias in defining uncertainty in computationally enabled strategies for data-driven climate model development. Final Technical Report.

The objective of the project is to develop strategies for better representing scientific sensibilities within statistical measures of model skill that then can be used within a Bayesian statistical framework for data-driven climate model development and improved measures of model scientific uncertainty. One of the thorny issues in model evaluation is quantifying the effect of biases on climate projections. While any bias is not desirable, only those biases that affect feedbacks affect scatter in climate projections. The effort at the University of Texas is to analyze previously calculated ensembles of CAM3.1 with perturbed parameters to discover how biases affect projections of global warming. The hypothesis is that compensating errors in the control model can be identified by their effect on a combination of processes and that developing metrics that are sensitive to dependencies among state variables would provide a way to select version of climate models that may reduce scatter in climate projections. Gabriel Huerta at the University of New Mexico is responsible for developing statistical methods for evaluating these field dependencies. The UT effort will incorporate these developments into MECS, which is a set of python scripts being developed at the University of Texas for managing the workflow associated with data-driven climate model development over HPC resources. This report reflects the main activities at the University of New Mexico where the PI (Huerta) and the Postdocs (Nosedal, Hattab and Karki) worked on the project.

58 GEOSCIENCES↗
DOE OSTI2024

California Offshore Wind Resource Bias Investigation and Correction [Slides]

This presentation highlights the findings of an investigation into the causes of high wind speed bias in offshore California's 20-year wind resource dataset, CA20, released by the National Renewable Energy Laboratory (NREL) in 2020. Utilizing observations from floating lidars deployed by the Pacific Northwest National Laboratory (PNNL), considerable bias in modeled hub-height winds was identified. The study reveals that the choice of planetary boundary layer (PBL) scheme significantly impacts wind speed accuracy, with the less common Yonsei University (YSU) scheme nearly eliminating bias compared to the widely used Mellor-Yamada-Nakanishi-Niino (MYNN) scheme. Furthermore, it shows that one of the key factors contributing to this modeled bias is the fact that MYNN overestimates the frequency of stable atmospheric conditions in offshore California. As a result, an updated dataset, NOW-23, incorporating YSU as the PBL scheme, has been produced.

17 WIND ENERGY↗