Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “biasing”

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 163 records · Page 9
All sourcesNASA NTRSDOE OSTI
NASA NTRS2012

Cognitive Bias in Systems Verification

Working definition of cognitive bias: Patterns by which information is sought and interpreted that can lead to systematic errors in decisions. Cognitive bias is used in diverse fields: Economics, Politics, Intelligence, Marketing, to name a few. Attempts to ground cognitive science in physical characteristics of the cognitive apparatus exceed our knowledge. Studies based on correlations; strict cause and effect is difficult to pinpoint. Effects cited in the paper and discussed here have been replicated many times over, and appear sound. Many biases have been described, but it is still unclear whether they are all distinct. There may only be a handful of fundamental biases, which manifest in various ways. Bias can effect system verification in many ways . Overconfidence -> Questionable decisions to deploy. Availability -> Inability to conceive critical tests. Representativeness -> Overinterpretation of results. Positive Test Strategies -> Confirmation bias. Debiasing at individual level very difficult. The potential effect of bias on the verification process can be managed, but not eliminated. Worth considering at key points in the process.

Cognitive bias↗
NASA NTRS2012

Estimating Climatological Bias Errors for the Global Precipitation Climatology Project (GPCP)

A procedure is described to estimate bias errors for mean precipitation by using multiple estimates from different algorithms, satellite sources, and merged products. The Global Precipitation Climatology Project (GPCP) monthly product is used as a base precipitation estimate, with other input products included when they are within +/- 50% of the GPCP estimates on a zonal-mean basis (ocean and land separately). The standard deviation s of the included products is then taken to be the estimated systematic, or bias, error. The results allow one to examine monthly climatologies and the annual climatology, producing maps of estimated bias errors, zonal-mean errors, and estimated errors over large areas such as ocean and land for both the tropics and the globe. For ocean areas, where there is the largest question as to absolute magnitude of precipitation, the analysis shows spatial variations in the estimated bias errors, indicating areas where one should have more or less confidence in the mean precipitation estimates. In the tropics, relative bias error estimates (s/m, where m is the mean precipitation) over the eastern Pacific Ocean are as large as 20%, as compared with 10%-15% in the western Pacific part of the ITCZ. An examination of latitudinal differences over ocean clearly shows an increase in estimated bias error at higher latitudes, reaching up to 50%. Over land, the error estimates also locate regions of potential problems in the tropics and larger cold-season errors at high latitudes that are due to snow. An empirical technique to area average the gridded errors (s) is described that allows one to make error estimates for arbitrary areas and for the tropics and the globe (land and ocean separately, and combined). Over the tropics this calculation leads to a relative error estimate for tropical land and ocean combined of 7%, which is considered to be an upper bound because of the lack of sign-of-the-error canceling when integrating over different areas with a different number of input products. For the globe the calculated relative error estimate from this study is about 9%, which is also probably a slight overestimate. These tropical and global estimated bias errors provide one estimate of the current state of knowledge of the planet's mean precipitation.

Global Precipitation Climatology Project (GPCP)↗
DOE OSTI2020

Minimal Experimental Bias on the Hydrogen Bond Greatly Improves Ab Initio Molecular Dynamics Simulations of Water

Experiment Directed Simulations (EDS) is a method within a class of techniques seeking to improve molecular simulations by minimally biasing the system Hamiltonian to reproduce certain experimental observables. In a previous application of EDS to ab initio molecular dynamics (AIMD) simulation based on electronic density functional theory (DFT), the AIMD simulations of water were biased to reproduce its experimentally derived solvation structure. In particular, by solely biasing the O-O pair correlation functions, other structural and dynamical properties that were not biased were improved. In this work, the hypothesis is tested that directly biasing the OH pair correlation (and hence the H-O∙∙∙H hydrogen bonding), will provide an even better improvement of DFT-based water properties in AIMD simulations. The logic behind this hypothesis is that for most electronic DFT descriptions of water the hydrogen bonding is known to be deficient due to anomalous charge transfer and over polarization in the DFT. Using recent advances to the EDS learning algorithm, we thus train a minimal bias on AIMD water that reproduces the O-H radial distribution function derived from the highly accurate MB-pol model of water. Finally, it is then confirmed that biasing the O-H pair correlation alone can lead to improved AIMD water properties, with structural and dynamical properties in even closer to experiment than the previous EDS-AIMD model.

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

Aridity-Dependent Land Surface Skin Temperature Biases in CMIP5/6

Land surface skin temperature, a critical indicator of climate change, connects the water and energy cycles between the land and the atmosphere. Here, in this study, we evaluate the simulations of land surface skin temperature from the Coupled Model Intercomparison Project Phase 5 (CMIP5) and CMIP6 models with satellite-based datasets and reanalysis. We find systematic cold skin temperature biases over arid regions in CMIP5/CMIP6 simulations. Over arid and semi-arid regions, latent heat biases drive skin temperature biases by evaporative cooling. Over humid regions, surface downward shortwave and albedo biases are relatively more critical. Spatial patterns of biases remain similar in the latest CMIP6 simulations, suggesting systematic biases in land-atmosphere interactions. These biases need to be corrected or considered while using models for future projections.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Improved Representations of Longwave Surface Emissivity to Reduce Surface and Atmospheric Heating Biases in Earth System Models

Abstract Many Earth system models (ESMs) approximate surface emissivity as a broadband constant. This approximation reduces the computational burden, yet omits the spectral structure of emissivity and atmospheric absorption. Neglecting spectral variation in surface emission introduces biases in longwave (LW) atmospheric fluxes and heating. Biases are strongest over surfaces with strongly varying emissivity and minimal atmospheric opacity. We examine these biases over water, ice, and snow surfaces. We partition spectral emissivity into the 16 spectral bands utilized by a single‐column atmospheric radiative transfer model (RRTMG_LW) commonly used in ESMs. We quantify flux and heating biases introduced by broadband assumptions relative to the spectrally resolved case for standard atmospheric profiles over each surface type. Current assumptions tend to overestimate upwelling surface fluxes; for example, the greybody assumption overestimates flux by 1.6 W/m 2 (0.52%) at the bottom of a mid‐latitude winter atmosphere over ice, and by 2.33 (1.0%) at the top of atmosphere. The blackbody assumption tends to artificially cool Earth's surface, stabilizing the lower troposphere. Interestingly, the optimal broadband emissivity can deviate from the Planck‐weighted mean by up to 3% depending on surface type and atmospheric profile. We investigate bias sensitivity to surface temperature, cloud water path, and atmospheric water vapor. Bias is most sensitive to water vapor content, and least sensitive to cloud water path. Lastly, we show that a modified greybody method with updated broadband values can reduce total surface flux bias up to 1.69 , comparable to a five‐band approach and at a fraction of the computational cost.

Manzo, L. [Department of Earth System Science Univ↗
DOE OSTI2014

Bias current dependence of the spin lifetime in insulating Al{sub 0.3}Ga{sub 0.7}As

The spin lifetime and Hanle signal amplitude dependence on bias current has been investigated in insulating Al{sub 0.3}Ga{sub 0.7}As:Si using a three-terminal Hanle effect geometry. The amplitudes of the Hanle signals are much larger for forward bias than for reverse bias, although the spin lifetimes found are statistically equivalent. The spin resistance-area product shows a strong increase with bias current for reverse bias and small forward bias until 150 μA, beyond which a weak dependence is observed. The spin lifetimes diminish substantially with increasing bias current. The dependence of the spin accumulation and lifetime diminish only moderately with temperature from 5 K to 30 K.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2014

Controllable exchange bias in Fe/metamagnetic FeRh bilayers

We report the studies of tuning the exchange bias at ferromagnetic Fe/metamagnetic FeRh bilayer interfaces. Fe/FeRh(111) bilayers show exchange bias in the antiferromagnetic state of FeRh while no exchange bias occurs at Fe/FeRh(001) interface. The contrasting results are attributed to the spin configurations of FeRh at the interface, i.e., the uncompensated ferromagnetic spin configuration of FeRh appears exclusively for (111) orientation. The exchange bias disappears as the bilayers are warmed above the antiferromagnetic-ferromagnetic transition temperature. The direction of the exchange bias for Fe/FeRh(111) is also found to be perpendicular to the cooling-field direction, in contrast to the commonly observed direction of exchange bias for ferromagnetic/antiferromagnetic interfaces. In view of these results, the exchange bias in Fe/FeRh bilayers with the (111) crystallographic orientation should be useful for the design of rapid writing technology for magnetic information devices.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2015

Influence of growth conditions on exchange bias of NiMn-based spin valves

As shown in previous investigations, a correlation between a NiMn-based spin valve's thermal stability and its inherent exchange bias exists, even if the blocking temperature of the antiferromagnet is clearly above the heating temperature and the reason for thermal degradation is mainly diffusion and not the loss of exchange bias. Samples with high exchange bias are thermally more stable than samples with low exchange bias. Those structures promoting a high exchange bias are seemingly the same suppressing thermally induced diffusion processes (A. Wienecke and L. Rissing, “Relationship between thermal stability and layer-stack/structure of NiMn-based GMR systems,” in IEEE Transaction on Magnetic Conference (EMSA 2014)). Many investigations were carried out on the influence of the sputtering parameters as well as the layer thickness on the magnetoresistive effect. The influence of these parameters on the exchange bias and the sample's thermal stability, respectively, was hardly taken into account. The investigation described here concentrates on the last named issue. The focus lies on the influence of the sputtering parameters and layer thickness of the “starting layers” in the stack and the layers forming the (synthetic) antiferromagnet. This paper includes a guideline for the evaluated sputtering conditions and layer thicknesses to realize a high exchange bias and presumably good thermal stability for NiMn-based spin valves with a synthetic antiferromagnet.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2023

A room temperature exchange bias effect caused by the coexisting martensitic phase structures in Ni50Mn38Sb12−xGax polycrystalline Heusler alloys

The exchange bias effect is the physical cornerstone of applications, such as spin valves, ultra-high-density data storage, and magnetic tunnel junctions. This work studied the room temperature exchange bias effect by constructing a Ni50Mn38Sb12−xGax alloy system with coexisting martensitic phase structures. The study found that the exchange bias effect shows a non-monotonic change with the variation of Ga composition at 300 K, and an obvious room temperature exchange bias effect appears in the alloys with coexisting phase structures of 4O and L10, which is due to the strong exchange coupling between ferromagnetic and antiferromagnetic. Further research on the exchange bias effect and temperature shows that the blocking temperature is 420 K, and the exchange bias can stably exist in a temperature range of ∼200 K around room temperature. This work provides a method to engineer exchange bias effects at room temperature.

Physics↗
DOE OSTI2014

Biases on cosmological parameter estimators from galaxy cluster number counts

Sunyaev-Zel'dovich (SZ) surveys are promising probes of cosmology — in particular for Dark Energy (DE) —, given their ability to find distant clusters and provide estimates for their mass. However, current SZ catalogs contain tens to hundreds of objects and maximum likelihood estimators may present biases for such sample sizes. In this work we study estimators from cluster abundance for some cosmological parameters, in particular the DE equation of state parameter w{sub 0}, the amplitude of density fluctuations σ{sub 8}, and the Dark Matter density parameter Ω{sub c}. We begin by deriving an unbinned likelihood for cluster number counts, showing that it is equivalent to the one commonly used in the literature. We use the Monte Carlo approach to determine the presence of bias using this likelihood and study its behavior with both the area and depth of the survey, and the number of cosmological parameters fitted. Our fiducial models are based on the South Pole Telescope (SPT) SZ survey. Assuming perfect knowledge of mass and redshift some estimators have non-negligible biases. For example, the bias of σ{sub 8} corresponds to about 40% of its statistical error bar when fitted together with Ω{sub c} and w{sub 0}. Including a SZ mass-observable relation decreases the relevance of the bias, for the typical sizes of current SZ surveys. Considering a joint likelihood for cluster abundance and the so-called ''distance priors'', we obtain that the biases are negligible compared to the statistical errors. However, we show that the biases from SZ estimators do not go away with increasing sample sizes and they may become the dominant source of error for an all sky survey at the SPT sensitivity. Finally, we compute the confidence regions for the cosmological parameters using Fisher matrix and profile likelihood approaches, showing that they are compatible with the Monte Carlo ones. The results of this work validate the use of the current maximum likelihood methods for present SZ surveys, but highlight the need for further studies for upcoming experiments. To perform the analyses of this work, we developed fast, accurate, and adaptable codes for cluster counts in the framework of the Numerical Cosmology Library.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2014

Renormalized halo bias

This paper provides a systematic study of renormalization in models of halo biasing. Building on work of McDonald, we show that Eulerian biasing is only consistent with renormalization if non-local terms and higher-derivative contributions are included in the biasing model. We explicitly determine the complete list of required bias parameters for Gaussian initial conditions, up to quartic order in the dark matter density contrast and at leading order in derivatives. At quadratic order, this means including the gravitational tidal tensor, while at cubic order the velocity potential appears as an independent degree of freedom. Our study naturally leads to an effective theory of biasing in which the halo density is written as a double expansion in fluctuations and spatial derivatives. We show that the bias expansion can be organized in terms of Galileon operators which aren't renormalized at leading order in derivatives. Finally, we discuss how the renormalized bias parameters impact the statistics of halos.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2015

Is there scale-dependent bias in single-field inflation?

Scale-dependent halo bias due to local primordial non-Gaussianity provides a strong test of single-field inflation. While it is universally understood that single-field inflation predicts negligible scale-dependent bias compared to current observational uncertainties, there is still disagreement on the exact level of scale-dependent bias at a level that could strongly impact inferences made from future surveys. In this paper, we clarify this confusion and derive in various ways that there is exactly zero scale-dependent bias in single-field inflation. Much of the current confusion follows from the fact that single-field inflation does predict a mode coupling of matter perturbations at the level of f{sub NL}{sup local}; ≈ −5/3, which naively would lead to scale-dependent bias. However, we show explicitly that this mode coupling cancels out when perturbations are evaluated at a fixed physical scale rather than fixed coordinate scale. Furthermore, we show how the absence of scale-dependent bias can be derived easily in any gauge. This result can then be incorporated into a complete description of the observed galaxy clustering, including the previously studied general relativistic terms, which are important at the same level as scale-dependent bias of order f{sub NL}{sup local} ∼ 1. This description will allow us to draw unbiased conclusions about inflation from future galaxy clustering data.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2017

Large-scale assembly bias of dark matter halos

We present precise measurements of the assembly bias of dark matter halos, i.e. the dependence of halo bias on other properties than the mass, using curved 'separate universe' N-body simulations which effectively incorporate an infinite-wavelength matter overdensity into the background density. This method measures the LIMD (local-in-matter-density) bias parameters b {sub n} in the large-scale limit. We focus on the dependence of the first two Eulerian biases b {sup E} {sup {sub 1}} and b {sup E} {sup {sub 2}} on four halo properties: the concentration, spin, mass accretion rate, and ellipticity. We quantitatively compare our results with previous works in which assembly bias was measured on fairly small scales. Despite this difference, our findings are in good agreement with previous results. We also look at the joint dependence of bias on two halo properties in addition to the mass. Finally, using the excursion set peaks model, we attempt to shed new insights on how assembly bias arises in this analytical model.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2020

Can assembly bias explain the lensing amplitude of the BOSS CMASS sample in a Planck cosmology?

ABSTRACT In this paper, we investigate whether galaxy assembly bias can reconcile the 20–40 ${{\ \rm per\ cent}}$ disagreement between the observed galaxy projected clustering signal and the galaxy–galaxy lensing signal in the Baryon Oscillation Spectroscopic Survey CMASS galaxy sample. We use the suite of abacuscosmos lambda cold dark matter simulations at Planck best-fitting cosmology and two flexible implementations of extended halo occupation distribution (HOD) models that incorporate galaxy assembly bias to build forward models and produce joint fits of the observed galaxy clustering signal and the galaxy–galaxy lensing signal. We find that our models using the standard HODs without any assembly bias generalizations continue to show a 20–40 ${{\ \rm per\ cent}}$ overprediction of the observed galaxy–galaxy lensing signal. We find that our implementations of galaxy assembly bias do not reconcile the two measurements at Planck best-fitting cosmology. In fact, despite incorporating galaxy assembly bias, the satellite distribution parameter, and the satellite velocity bias parameter into our extended HOD model, our fits still strongly suggest a $\sim \! 34{{\ \rm per\ cent}}$ discrepancy between the observed projected clustering and galaxy–galaxy lensing measurements. It remains to be seen whether a combination of other galaxy assembly bias models, alternative cosmological parameters, or baryonic effects can explain the amplitude difference between the two signals.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

Evidence for galaxy assembly bias in BOSS CMASS redshift-space galaxy correlation function

ABSTRACT Building accurate and flexible galaxy–halo connection models is crucial in modelling galaxy clustering on non-linear scales. Recent studies have found that halo concentration by itself cannot capture the full galaxy assembly bias effect and that the local environment of the halo can be an excellent indicator of galaxy assembly bias. In this paper, we propose an extended halo occupation distribution (HOD) model that includes both a concentration-based assembly bias term and an environment-based assembly bias term. We use this model to achieve a good fit (χ2/degrees of freedom = 1.35) on the 2D redshift-space two-point correlation function (2PCF) of the Baryon Oscillation Spectroscopic Survey (BOSS) CMASS galaxy sample. We find that the inclusion of both assembly bias terms is strongly favoured by the data and the standard five-parameter HOD model is strongly rejected. More interestingly, the redshift-space 2PCF drives the assembly bias parameters in a way that preferentially assigns galaxies to lower mass haloes. This results in galaxy–galaxy lensing predictions that are within 1σ agreement with the observation, alleviating the perceived tension between galaxy clustering and lensing. We also showcase a consistent 3σ–5σ preference for a positive environment-based assembly bias that persists over variations in the fit. We speculate that the environmental dependence might be driven by underlying processes such as mergers and feedback, but might also be indicative of a larger halo boundaries such as the splashback radius. Regardless, this work highlights the importance of building flexible galaxy–halo connection models and demonstrates the extra constraining power of the redshift-space 2PCF.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

Evidence of galaxy assembly bias in SDSS DR7 galaxy samples from count statistics

We present observational constraints on the galaxy–halo connection, focusing particularly on galaxy assembly bias from a novel combination of counts-in-cylinders statistics, P(N CIC ), with the standard measurements of the projected two-point correlation function w p (r p ), and number density n gal of galaxies. We measure n gal , w p (r p ), and P(N CIC ) for volume-limited, luminosity-threshold samples of galaxies selected from SDSS DR7, and use them to constrain halo occupation distribution (HOD) models, including a model in which galaxy occupation depends upon a secondary halo property, namely halo concentration. We detect significant positive central assembly bias for the M r < -20.0 and M r <-19.5 samples. Central galaxies preferentially reside within haloes of high concentration at fixed mass. Positive central assembly bias is also favoured in the M r < -20.5 and M r < -19.0 samples. We find no evidence of central assembly bias in the M r < -21.0 sample. We observe only a marginal preference for negative satellite assembly bias in the M r < -20.0 and M r < -19.0 samples, and non-zero satellite assembly bias is not indicated in other samples. Our findings underscore the necessity of accounting for galaxy assembly bias when interpreting galaxy survey data, and demonstrate the potential of count statistics in extracting information from the spatial distribution of galaxies, which could be applied to both galaxy–halo connection studies and cosmological analyses.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2019

The effect of dust bias on the census of neutral gas and metals in the high-redshift Universe due to SDSS-II quasar colour selection

ABSTRACT We present a systematic study of the impact of a dust bias on samples of damped Ly α absorbers (DLAs). This bias arises as an effect of the magnitude and colour criteria utilized in the Sloan Digital Sky Survey (SDSS) quasar target selection up until data release 7 (DR7). The bias has previously been quantified assuming only a contribution from the dust obscuration. In this work, we apply the full set of magnitude and colour criteria used up until SDSS-DR7 in order to quantify the full impact of dust biasing against dusty and metal-rich DLAs. We apply the quasar target selection algorithm on a modelled population of intrinsic colours, and by exploring the parameter space consisting of redshift, ($z_{\rm{\small QSO}}$and zabs), optical extinction, and H i column density, we demonstrate how the selection probability depends on these variables. We quantify the dust bias on the following properties derived for DLAs at z ≈ 3: the incidence rate, the mass density of neutral hydrogen and metals, and the average metallicity. We find that all quantities are significantly affected. When considering all uncertainties, the mass density of neutral hydrogen is underestimated by 10–50 per cent, and the mass density in metals is underestimated by 30–200 per cent. Lastly, we find that the bias depends on redshift. At redshift z = 2.2, the mass density of neutral hydrogen and metals might be underestimated by up to a factor of 2 and 5, respectively. Characterizing such a bias is crucial in order to accurately interpret and model the properties and metallicity evolution of absorption-selected galaxies.

Krogager, Jens-Kristian↗
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↗