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DOE OSTI2025

Selection-bias effects on high-𝑝 𝑇 yield and correlation measurements in oxygen + oxygen collisions

Oxygen + oxygen (O+O) collisions at the BNL Relativistic Heavy Ion Collider (RHIC) and the CERN Large Hadron Collider (LHC) offer a unique experimental opportunity to observe the onset of jet quenching in intermediate relativistic collision systems. As with the smaller proton-nucleus or larger nucleus-nucleus systems, measurements of centrality-selected high-𝑝 T processes in O+O collisions are expected to be sensitive to selection bias effects, which will be necessary to quantify or mitigate before a definitive conclusion on the presence of jet quenching. Using two Monte Carlo heavy-ion event generators, we provide a survey of centrality bias effects on high-𝑝 T yield and correlation measurements. Some highlights of our findings include that (1) bias factors for the accessible kinematic range at RHIC show a nontrivial 𝑝 T dependence, compared to a negligible one at the LHC given the smaller accessible Bjorken-𝑥 range, (2) centrality definitions based on multiplicity are less sensitive to bias effects than those based on the transverse energy, (3) the Angantyr generator gives qualitatively similar but larger-magnitude bias factors than Hijing, and (4) correlation measurements have a much smaller sensitivity to bias effects than do yield measurements. Furthermore, the findings here are intended to guide the experimental design and interpretation of O+O jet quenching and other hard-process measurements.

Hard scattering↗

DOE OSTI2024

Reverse-Bias Resilience of Monolithic Perovskite/Silicon Tandem Solar Cells

Metal halide perovskites have rapidly enabled a range of high-performance photovoltaic technologies. However, catastrophic failure under reverse voltage bias hinders their commercialization. In this work, we demonstrate that by employing a monolithic perovskite/silicon tandem structure, the perovskite subcell can be effectively protected by the silicon subcell under reverse bias, owing to the low reverse-bias diode current of the silicon subcell. As a result, the tested perovskite/silicon tandem devices show superior reverse-bias resilience compared to perovskite single-junction devices in both long-term reverse voltage biasing tests at the single-cell level and partial shading tests at the module level. These results highlight that, compared to other perovskite technologies, monolithic perovskite/silicon tandems are at a higher technology readiness level in terms of tackling the reverse-bias and partial shading challenge, which is a considerable advantage towards commercialization.

commercialization↗

DOE OSTI2026

Bias-Modulated ALD of ZnO: Insights into Precursor-Surface Interactions for ZnO Films

Atomic layer deposition (ALD) is widely used to deposit conformal thin films but is often limited in the tunability of the resulting material’s properties. Substrate bias and electric fields alter precursor-surface interactions and provide means to tune material properties. To explore this, we performed zinc oxide (ZnO) ALD using diethylzinc (DEZ) and water on silicon native oxide substrates at 150 °C in a sample holder designed to create a static electrical field by biasing one plate of a parallel plate capacitor-style sample holder during deposition. ZnO films prepared in an electric field/on a biased sample holder were thinner, changed relative crystalline composition, and contained more carbon compared to samples grown in identical sample holders without bias. The thickness was independent of the magnitude of the eletric field between plates, indicating that the primary driver for the change was substrate biasing not the electric field between plates of the parallel plate capacitor-style sample holder. Density functional theory calculations showed enhanced electron migration between dissociatively adsorbed DEZ molecules and the ZnO (002) facet with increasing force from an electric field at the substrate surface, which strengthens the electronic interactions between the surface and the adsorbate. These models offer a compelling explanation for the inhibited growth, changes in the crystallinity, and increase in carbon content of films grown in an electric field/on biased plates.

Jones, Jessica C. (ORCID:0000000174754620)↗

DOE OSTI2024

Deep Learning-based Non-Stationary Bias Correction (NSBC)

This work develops the NSBC (non-stationary bias correction) methodology to correct temperature projection bias from E3SM. The NSBC deep learning framework consists of a three-part architecture: an auto-encoder for compressing the spatial information, an LSTM for predicting annual temperature mean, and a U-Net for capturing the residual bias in temperature. The non-stationary bias correction (NSBC) framework can correct the non-stationarity of the biases of the climate models, which significantly improves the accuracy of future temperature prediction and improves the overestimation of extreme high temperatures that many existing bias correction methods suffer from. Getting started 1. Obtain the historical climate simulation and observation data. The E3SM simulation data are available through https://aims2.llnl.gov/search/cmip6/. The pseudo observations, the Geophysical Fluid Dynamics Laboratory (GFDL)-ESM4 model (Krasting et al., 2018) are available through https://aims2.llnl.gov/search/cmip6/. The spatial resolution of E3SM and pseudo observation datasets are both regridded to a common 1° resolution grid using conservative interpolation. The regridded E3SM and pseudo observation with 1° resolution can be found throught ./data/. 2. Train the Auto-encoder model. Python 0-autoencoder.py 3. Train the LSTM Python 1-LSTM.py 4. Generate the annual mean temperature based on trained LSTM Python 2-generate_annual_mean_LSTM.py 5. Train the U-Net. Python 3-unet.py 6. Evaluation and compared with the baseline Python 4_evaluation.py Is there a deadline approaching that requires the release of yo

Lucas, Donald↗

NASA NTRS1988

Variance and bias confidence criteria for ERA modal parameter identification

For the ERA system identification algorithm, perturbation methods are used to develop expressions for variance and bias of the identified modal parameters. Based on the statistics of the measurement noise, the variance results serve as confidence criteria by indicating how likely the true parameters are to lie within any chosen interval about their identified values. This replaces the use of expensive and time-consuming Monte Carlo computer runs to obtain similar information. The bias estimates help guide the ERA user in his choice of which data points to use and how much data to use in order to obtain the best results, performing the trade-off between the bias and scatter. Also, when the uncertainty in the bias is sufficiently small, the bias information can be used to correct the ERA results. In addition, expressions for the variance and bias of the singular values serve as tools to help the ERA user decide the proper modal order.

Longman, Richard W.↗

NASA NTRS1992

The effect of small-wave modulation on the electromagnetic bias

The effect of the modulation of small ocean waves by large waves on the physical mechanism of the EM bias is examined by conducting a numerical scattering experiment which does not assume the applicability of geometric optics. The modulation effect of the large waves on the small waves is modeled using the principle of conservation of wave action and includes the modulation of gravity-capillary waves. The frequency dependence and magnitude of the EM bias is examined for a simplified ocean spectral model as a function of wind speed. These calculations make it possible to assess the validity of previous assumptions made in the theory of the EM bias, with respect to both scattering and hydrodynamic effects. It is found that the geometric optics approximation is inadequate for predictions of the EM bias at typical radar altimeter frequencies, while the improved scattering calculations provide a frequency dependence of the EM bias which is in qualitative agreement with observation. For typical wind speeds, the EM bias contribution due to small-wave modulation is of the same order as that due to modulation by the nonlinearities of the large-scale waves.

Rodriguez, Ernesto↗

NASA NTRS1991

Experimental and theoretical determination of sea-state bias in radar altimetry

The major unknown error in radar altimetry is due to waves on the sea surface which cause the mean radar-reflecting surface to be displaced from mean sea level. This is the electromagnetic bias. The primary motivation for the project was to understand the causes of the bias so that the error it produces in radar altimetry could be calculated and removed from altimeter measurements made from space by the Topex/Poseidon altimetric satellite. The goals of the project were: (1) observe radar scatter at vertical incidence using a simple radar on a platform for a wide variety of environmental conditions at the same time wind and wave conditions were measured; (2) calculate electromagnetic bias from the radar observations; (3) investigate the limitations of the present theory describing radar scatter at vertical incidence; (4) compare measured electromagnetic bias with bias calculated from theory using measurements of wind and waves made at the time of the radar measurements; and (5) if possible, extend the theory so bias can be calculated for a wider range of environmental conditions.

Stewart, Robert H.↗

NASA NTRS1994

Temporal study of the current collected by a Langmuir probe biased to high voltages in the wake behind a conducting body

The temporal evolution of the voltage-current characteristic of a spherical Langmuir probe in the near-wake region of a conducting body is investigated in a pulsed plasma device. Measurements of the current collected by a sphere placed on axis behind a conducting disk are presented as a function of the bias voltage and time. The bias voltage on the collecting sphere is large enough, ranging from +500 to -3000 V, to attract ions or electrons across the wake region. In addition, plasma potential profiles as a function of sphere bias voltage and time throughout the nearwake and midwake region are presented. It is found that for a negative sphere bias a threshold voltage exists for current collection. This threshold voltage decreases during the formation of the wake behind the conducting disk. Potential measurements indicate that the sheath region around the negatively biased sphere contracts as the wake region establishes itself. This contraction of the sheath region increases the electric field attracting ions to the sphere and decreases the threshold voltage for current collection. For positive sphere bias voltage the current collected by the sphere does not exhibit a threshold voltage but is monotonically increasing.

Meassick, S.↗

NASA NTRS1994

Differential sea-state bias: A case study using TOPEX/POSEIDON data

We used selected data from the NASA altimeter TOPEX/POSEIDON to calculate differences in range measured by the C and Ku-band altimeters when the satellite overflew 5 to 15 m waves late at night. The range difference is due to free electrons in the ionosphere and to errors in sea-state bias. For the selected data the ionospheric influence on Ku range is less than 2 cm. Any difference in range over short horizontal distances is due only to a small along-track variability of the ionosphere and to errors in calculating the differential sea-state bias. We find that there is a barely detectable error in the bias in the geophysical data records. The wave-induced error in the ionospheric correction is less than 0.2% of significant wave height. The equivalent error in differential range is less than 1% of wave height. Errors in the differential sea-state bias calculations appear to be small even for extreme wave heights that greatly exceed the conditions on which the bias is based. The results also improved our confidence in the sea-state bias correction used for calculating the geophysical data records. Any error in the correction must influence Ku and C-band ranges almost equally.

Stewart, Robert H.↗

NASA NTRS1992

Symmetry as Bias: Rediscovering Special Relativity

This paper describes a rational reconstruction of Einstein's discovery of special relativity, validated through an implementation: the Erlanger program. Einstein's discovery of special relativity revolutionized both the content of physics and the research strategy used by theoretical physicists. This research strategy entails a mutual bootstrapping process between a hypothesis space for biases, defined through different postulated symmetries of the universe, and a hypothesis space for physical theories. The invariance principle mutually constrains these two spaces. The invariance principle enables detecting when an evolving physical theory becomes inconsistent with its bias, and also when the biases for theories describing different phenomena are inconsistent. Structural properties of the invariance principle facilitate generating a new bias when an inconsistency is detected. After a new bias is generated. this principle facilitates reformulating the old, inconsistent theory by treating the latter as a limiting approximation. The structural properties of the invariance principle can be suitably generalized to other types of biases to enable primal-dual learning.

Lowry, Michael R.↗

NASA NTRS1999

Publication Bias ( The "File-Drawer Problem") in Scientific Inference

Publication bias arises whenever the probability that a study is published depends on the statistical significance of its results. This bias, often called the file-drawer effect since the unpublished results are imagined to be tucked away in researchers' file cabinets, is potentially a severe impediment to combining the statistical results of studies collected from the literature. With almost any reasonable quantitative model for publication bias, only a small number of studies lost in the file-drawer will produce a significant bias. This result contradicts the well known Fail Safe File Drawer (FSFD) method for setting limits on the potential harm of publication bias, widely used in social, medical and psychic research. This method incorrectly treats the file drawer as unbiased, and almost always miss-estimates the seriousness of publication bias. A large body of not only psychic research, but medical and social science studies, has mistakenly relied on this method to validate claimed discoveries. Statistical combination can be trusted only if it is known with certainty that all studies that have been carried out are included. Such certainty is virtually impossible to achieve in literature surveys.

Scargle, Jeffrey D.↗

NASA NTRS2000

Innovative Liner Concepts: Experiments and Impedance Modeling of Liners Including the Effect of Bias Flow

The study of normal impedance of perforated plate acoustic liners including the effect of bias flow was studied. Two impedance models were developed by modeling the internal flows of perforate orifices as infinite tubes with the inclusion of end corrections to handle finite length effects. These models assumed incompressible and compressible flows, respectively, between the far field and the perforate orifice. The incompressible model was used to predict impedance results for perforated plates with percent open areas ranging from 5% to 15%. The predicted resistance results showed better agreement with experiments for the higher percent open area samples. The agreement also tended to deteriorate as bias flow was increased. For perforated plates with percent open areas ranging from 1% to 5%, the compressible model was used to predict impedance results. The model predictions were closer to the experimental resistance results for the 2% to 3% open area samples. The predictions tended to deteriorate as bias flow was increased. The reactance results were well predicted by the models for the higher percent open area, but deteriorated as the percent open area was lowered (5%) and bias flow was increased. A fit was done on the incompressible model to the experimental database. The fit was performed using an optimization routine that found the optimal set of multiplication coefficients to the non-dimensional groups that minimized the least squares slope error between predictions and experiments. The result of the fit indicated that terms not associated with bias flow required a greater degree of correction than the terms associated with the bias flow. This model improved agreement with experiments by nearly 15% for the low percent open area (5%) samples when compared to the unfitted model. The fitted model and the unfitted model performed equally well for the higher percent open area (10% and 15%).

Kelly, Jeff↗

NASA NTRS2000

Sound Absorption of a 2DOF Resonant Liner with Negative Bias Flow

This report describes an experimental study conducted to determine the effect of negative bias flow on the sound absorption of a two degree-of-freedom liner. The backwall for the liner was designed to act as a double-Helmholtz resonator so as to act as a hard wall at all frequencies except at its resonant frequencies. The effect of bias flow is investigated for a buried septum porosity of 2% and 19.5% for bias flow orifice Mach numbers up to 0.311. The bias flow appears to modify the resistance and reactance of the backwall alone at lower frequencies up to about 2 kHz, with marginal effects at higher frequencies. Absorption coefficients close to unity are achieved for a frequency range of 500 - 4000 Hz for the overall liner for a septum porosity of 2% and orifice Mach number of 0.128. Insertion loss tests performed in a flow duct facility for grazing flow Mach numbers up to 0.2 and septum Mach numbers up to 0.15 showed that negative bias flow can increase insertion loss by as much as 10 dB at frequencies in the range of 500 D 1400 Hz compared to no grazing flow. The effectiveness of the negative bias flow is diminished as the grazing flow velocity is increased.

Ahuja, K. K.↗

NASA NTRS2006

Skin Temperature Analysis and Bias Correction in a Coupled Land-Atmosphere Data Assimilation System

In an initial investigation, remotely sensed surface temperature is assimilated into a coupled atmosphere/land global data assimilation system, with explicit accounting for biases in the model state. In this scheme, an incremental bias correction term is introduced in the model's surface energy budget. In its simplest form, the algorithm estimates and corrects a constant time mean bias for each gridpoint; additional benefits are attained with a refined version of the algorithm which allows for a correction of the mean diurnal cycle. The method is validated against the assimilated observations, as well as independent near-surface air temperature observations. In many regions, not accounting for the diurnal cycle of bias caused degradation of the diurnal amplitude of background model air temperature. Energy fluxes collected through the Coordinated Enhanced Observing Period (CEOP) are used to more closely inspect the surface energy budget. In general, sensible heat flux is improved with the surface temperature assimilation, and two stations show a reduction of bias by as much as 30 Wm(sup -2) Rondonia station in Amazonia, the Bowen ratio changes direction in an improvement related to the temperature assimilation. However, at many stations the monthly latent heat flux bias is slightly increased. These results show the impact of univariate assimilation of surface temperature observations on the surface energy budget, and suggest the need for multivariate land data assimilation. The results also show the need for independent validation data, especially flux stations in varied climate regimes.

Bosilovich, Michael G.↗

NASA NTRS2011

Accommodating Sensor Bias in MRAC for State Tracking

The problem of accommodating unknown sensor bias is considered in a direct model reference adaptive control (MRAC) setting for state tracking using state feedback. Sensor faults can occur during operation, and if the biased state measurements are directly used with a standard MRAC control law, neither closed-loop signal boundedness, nor asymptotic tracking can be guaranteed and the resulting tracking errors may be unbounded or unacceptably large. A modified MRAC law is proposed, which combines a bias estimator with control gain adaptation, and it is shown that signal boundedness can be accomplished, although the tracking error may not go to zero. Further, for the case wherein an asymptotically stable sensor bias estimator is available, an MRAC control law is proposed to accomplish asymptotic tracking and signal boundedness. Such a sensor bias estimator can be designed if additional sensor measurements are available, as illustrated for the case wherein bias is present in the rate gyro and airspeed measurements. Numerical example results are presented to illustrate each of the schemes.

Patre, Parag↗

NASA NTRS2016

Foreground Bias from Parametric Models of Far-IR Dust Emission

We use simple toy models of far-IR dust emission to estimate the accuracy to which the polarization of the cosmic microwave background can be recovered using multi-frequency fits, if the parametric form chosen for the fitted dust model differs from the actual dust emission. Commonly used approximations to the far-IR dust spectrum yield CMB residuals comparable to or larger than the sensitivities expected for the next generation of CMB missions, despite fitting the combined CMB plus foreground emission to precision 0.1 percent or better. The Rayleigh-Jeans approximation to the dust spectrum biases the fitted dust spectral index by (Delta)(Beta)(sub d) = 0.2 and the inflationary B-mode amplitude by (Delta)(r) = 0.03. Fitting the dust to a modified blackbody at a single temperature biases the best-fit CMB by (Delta)(r) greater than 0.003 if the true dust spectrum contains multiple temperature components. A 13-parameter model fitting two temperature components reduces this bias by an order of magnitude if the true dust spectrum is in fact a simple superposition of emission at different temperatures, but fails at the level (Delta)(r) = 0.006 for dust whose spectral index varies with frequency. Restricting the observing frequencies to a narrow region near the foreground minimum reduces these biases for some dust spectra but can increase the bias for others. Data at THz frequencies surrounding the peak of the dust emission can mitigate these biases while providing a direct determination of the dust temperature profile.

cosmic microwave background↗

NASA NTRSDate not supplied

Characterization of OCO-2 and ACOS-GOSAT biases and errors for flux estimates

We characterize the magnitude of seasonally and spatially varying biases in the National Aeronautics and Space Administration (NASA) Orbiting Carbon Observatory-2 (OCO-2) Version 8 (v8) and the Atmospheric CO2 Observations from Space (ACOS) Greenhouse Gas Observing SATellite (GOSAT) version 7.3 (v7.3) satellite CO2 retrievals by comparisons to measurements collected by the Total Carbon Column Observing Network (TCCON), Atmospheric Tomography (ATom) experiment, and National Oceanic and Atmospheric Administration (NOAA) Earth System Research Laboratory (ESRL) and U. S. Department of Energy (DOE) aircraft, and surface stations. Although the ACOS-GOSAT estimates of the column averaged carbon dioxide (CO2) dry air mole fraction (XCO2) have larger random errors than the OCO-2 XCO2 estimates, and the space-based estimates over land have larger random errors than those over ocean, the systematic errors are similar across both satellites and surface types, 0.6 ± 0.1 ppm. We find similar estimates of systematic error whether dynamic versus geometric coincidences or ESRL/DOE aircraft versus TCCON are used for validation (over land), once validation and co-location errors are accounted for. We also find that areas with sparse throughput of good quality data (due to quality flags and preprocessor selection) over land have ~double the error of regions of high-throughput of good quality data. We characterize both raw and bias-corrected results, finding that bias correction improves systematic errors by a factor of 2 for land observations and improves errors by ~0.2 ppm for ocean. We validate the lowermost tropospheric (LMT) product for OCO-2 and ACOS-GOSAT by comparison to aircraft and surface sites, finding systematic errors of ~1.1 ppm, while having 2-3 times the variability of XCO2. We characterize the time and distance scales of correlations for OCO-2 XCO2 errors, and find error correlations on scales of 0.3 degrees, 5-10 degrees, and 60 days. We find comparable scale lengths for the bias correction term. Assimilation of the OCO-2 bias correction term is used to estimate flux errors resulting from OCO-2 seasonal biases, finding annual flux errors on the order of 0.3 and 0.4 PgC/yr for Transcom-3 ocean and land regions, respectively.

Susan S Kulawik↗

NASA NTRS2022

Satellite-Based Evaluation of AeroCom Model Bias in Biomass Burning Regions

Global models are widely used to simulate biomass burning aerosol (BBA). Exhaustive evaluations on model representation of aerosol distributions and properties are fundamental to assess health and climate impacts of BBA. Here we conducted a comprehensive comparison of Aerosol Comparisons between Observations and Models (AeroCom) project model simulations with satellite observations. A total of 59 runs by 18 models from three AeroCom Phase-III experiments (i.e., biomass burning emissions, CTRL16, and CTRL19) and 14 satellite products of aerosols were used in the study. Aerosol optical depth (AOD) at 550 nm was investigated during the fire season over three key fire regions reflecting different fire dynamics (i.e., deforestation-dominated Amazon, Southern Hemisphere Africa where savannas are the key source of emissions, and boreal forest burning in boreal North America). The 14 satellite products were first evaluated against AErosol RObotic NETwork (AERONET) observations, with large uncertainties found. But these uncertainties had small impacts on the model evaluation that was dominated by modeling bias. Through a comparison with Polarization and Directionality of the Earth’s Reflectances measurements with the Generalized Retrieval of Aerosol and Surface Properties algorithm (POLDER-GRASP), we found that the modeled AOD values were biased by −93 % to 152 %, with most models showing significant underestimations even for the state-of-the-art aerosol modeling techniques (i.e., CTRL19). By scaling up BBA emissions, the negative biases in modeled AOD were significantly mitigated, although it yielded only negligible improvements in the correlation between models and observations, and the spatial and temporal variations in AOD biases did not change much. For models in CTRL16 and CTRL19, the large diversity in modeled AOD was in almost equal measures caused by diversity in emissions, lifetime, and the mass extinction coefficient (MEC). We found that in the AeroCom ensemble, BBA lifetime correlated significantly with particle deposition (as expected) and in turn correlated strongly with precipitation. Additional analysis based on Cloud-Aerosol LIdar with Orthogonal Polarization (CALIOP) aerosol profiles suggested that the altitude of the aerosol layer in the current models was generally too low, which also contributed to the bias in modeled lifetime. Modeled MECs exhibited significant correlations with the Ångström exponent (AE, an indicator of particle size). Comparisons with the POLDER-GRASP-observed AE suggested that the models tended to overestimate the AE (underestimated particle size), indicating a possible underestimation of MECs in models. The hygroscopic growth in most models generally agreed with observations and might not explain the overall underestimation of modeled AOD. Our results imply that current global models contain biases in important aerosol processes for BBA (e.g., emissions, removal, and optical properties) that remain to be addressed in future research.

biomass burning aerosols↗