Estimation Of Systematic Errors Research Articles

Control-Plate Regression (CPR) Normalization for High-Throughput Screens with Many Active Features

Systematic error is present in all high-throughput screens, lowering measurement accuracy. Because screening occurs at the early stages of research projects, measurement inaccuracy leads to following up inactive features and failing to follow up active features. Current normalization methods take advantage of the fact that most primary-screen features (e.g., compounds) within each plate are inactive, which permits robust estimates of row and column systematic-error effects. Screens that contain a majority of potentially active features pose a more difficult challenge because even the most robust normalization methods will remove at least some of the biological signal. Control plates that contain the same feature in all wells can provide a solution to this problem by providing well-by-well estimates of systematic error, which can then be removed from the treatment plates. We introduce the robust control-plate regression (CPR) method, which uses this approach. CPR’s performance is compared to a high-performing primary-screen normalization method in four experiments. These data were also perturbed to simulate screens with large numbers of active features to further assess CPR’s performance. CPR performs almost as well as the best performing normalization methods with primary screens and outperforms the Z-score and equivalent methods with screens containing a large proportion of active features.

Beam-size-free optics determination

A new method to measure the Twiss parameters in a beam transport line is presented. Usually these parameters are obtained based on the measured beam sizes. In the new method, in contrast, we determine them by finding quadrupole strengths that result in minimum beam sizes at a downstream measurement location. Therefore, systematic errors related to beam-size monitors do not propagate to the measured Twiss parameters. We describe the method together with a detailed estimation of statistical and systematic errors. It was examined with beam at the SwissFEL injector test facility at PSI, and these results are also presented.

On the accuracy of triple phase boundary lengths calculated from tomographic image data

The triple phase boundary (TPB) length is one of the most important quantities obtainable from three dimensional reconstructions of solid oxide fuel cells that utilize porous composite electrodes. However, the choice of TPB calculation method and the voxelation of the microstructures can lead to systematic errors in TPB estimates. Here, two approaches for calculating the TPB density are compared to investigate how different TPB aspects such as curvature, orientation, and phase contact angles affect the results. The first approach applies a correction factor to the TPB length calculated by simply summing voxel (volume element) edge lengths that are shared between voxels of three different phases. The second approach applies a smoothening technique to the TPB curves. The two methods are compared by calculations on different kinds of artificially generated microstructures and on a real SOFC electrode microstructure obtained by focused ion beam tomography. Results are presented showing how specific aspects of different microstructures affect the TPB length calculation error.

Self-calibration of BICEP1 three-year data and constraints on astrophysical polarization rotation

Cosmic Microwave Background (CMB) polarimeters aspire to measure the faint $B$-mode signature predicted to arise from inflationary gravitational waves. They also have the potential to constrain cosmic birefringence which would produce non-zero expectation values for the CMB's $TB$ and $EB$ spectra. However, instrumental systematic effects can also cause these $TB$ and $EB$ correlations to be non-zero. In particular, an overall miscalibration of the polarization orientation of the detectors produces $TB$ and $EB$ spectra which are degenerate with isotropic cosmological birefringence, while also introducing a small but predictable bias on the $BB$ spectrum. The \bicep three-year spectra, which use our standard calibration of detector polarization angles from a dielectric sheet, are consistent with a polarization rotation of $\alpha = -2.77^\circ \pm 0.86^\circ \text{(statistical)} \pm 1.3^\circ \text{(systematic)}$. We revise the estimate of systematic error on the polarization rotation angle from the two-year analysis by comparing multiple calibration methods. We investigate the polarization rotation for the \bicep 100 GHz and 150 GHz bands separately to investigate theoretical models that produce frequency-dependent cosmic birefringence. We find no evidence in the data supporting either these models or Faraday rotation of the CMB polarization by the Milky Way galaxy's magnetic field. If we assume that there is no cosmic birefringence, we can use the $TB$ and $EB$ spectra to calibrate detector polarization orientations, thus reducing bias of the cosmological $B$-mode spectrum from leaked $E$-modes due to possible polarization orientation miscalibration. After applying this "self-calibration" process, we find that the upper limit on the tensor-to-scalar ratio decreases slightly, from $r<0.70$ to $r<0.65$ at $95\%$ confidence.

Spectral Rate Theory for Two-State Kinetics.

Classical rate theories often fail in cases where the observable(s) or order parameter(s) used is a poor reaction coordinate or the observed signal is deteriorated by noise, such that no clear separation between reactants and products is possible. Here, we present a general spectral two-state rate theory for ergodic dynamical systems in thermal equilibrium that explicitly takes into account how the system is observed. The theory allows the systematic estimation errors made by standard rate theories to be understood and quantified. We also elucidate the connection of spectral rate theory with the popular Markov state modeling approach for molecular simulation studies. An optimal rate estimator is formulated that gives robust and unbiased results even for poor reaction coordinates and can be applied to both computer simulations and single-molecule experiments. No definition of a dividing surface is required. Another result of the theory is a model-free definition of the reaction coordinate quality. The reaction coordinate quality can be bounded from below by the directly computable observation quality, thus providing a measure allowing the reaction coordinate quality to be optimized by tuning the experimental setup. Additionally, the respective partial probability distributions can be obtained for the reactant and product states along the observed order parameter, even when these strongly overlap. The effects of both filtering (averaging) and uncorrelated noise are also examined. The approach is demonstrated on numerical examples and experimental single-molecule force-probe data of the p5ab RNA hairpin and the apo-myoglobin protein at low pH, focusing here on the case of two-state kinetics.

Formulation of error propagation and estimation in grating reconstruction by a dual-rotating compensator Mueller matrix polarimeter

Recently, the Mueller matrix polarimeter (MMP) has been introduced for critical dimension and overlay metrology. In practice, the measurement process invariably has errors. These errors, which can be generally categorized into random and systematic errors, have great influences on the final precision and accuracy of the extracted structural parameters. In this paper, we present detailed formulations for the propagation and estimation of random and systematic errors in grating reconstruction using a dual-rotating compensator MMP (DRC-MMP). We derive a generalized first-order error propagating formula, which reveals the mechanism of error propagation in grating reconstruction using the DRC-MMP. According to this first-order error propagating formula and the measurement principle of the DRC-MMP, we then derive detailed formulations for the estimation of random and systematic errors that are propagated into the final extracted structural parameters. Simulations performed on silicon grating samples have demonstrated the validity of the present theoretical derivations.

Determination of the Rate Constant for the OH(X2Π) + OH(X2Π) → H2O + O(3P) Reaction Over the Temperature Range 295 to 701 K

The rate constant for the radical-radical reaction OH(X(2)Π) + OH(X(2)Π) → H2O + O((3)P) has been measured over the temperature and pressure ranges 295-701 K and 2-12 Torr, respectively, in mixtures of CF4, N2O, and H2O. The OH radical was produced by the 193 nm laser photolysis of N2O. The resulting O((1)D) atoms reacted rapidly with H2O to produce the OH radical. The OH radical was detected by high-resolution time-resolved infrared absorption spectroscopy using a single Λ-doublet component of the OH(1,0) P1e/f(4.5) fundamental vibrational transition. A detailed kinetic model was used to determine the reaction rate constant as a function of temperature. These experiments were conducted in a new temperature controlled reaction chamber. The values of the measured rate constants are quite similar to the previous measurements from this laboratory of Bahng and Macdonald (J. Phys. Chem. A 2007 , 111 , 3850 - 3861); however, they cover a much larger temperature range. The results of the present work do not agree with recent measurements of Sangwan and Krasnoperov (J. Phys. Chem. A 2012 , 116 , 11817 - 11822). At 295 K the rate constant of the title reaction was found to be (2.52 ± 0.63) × 10(-12) cm(3) molecule(-1) s(-1), where the uncertainty includes both experimental scatter and an estimate of systematic errors at the 95% confidence limit. Over the temperature range of the experiments, the rate constant can be represented by k1a = 4.79 × 10(-18)T(1.79) exp(879.0/T) cm(3) molecule(-1) s(-1) with a uncertainty of ±24% at the 2σ level, including experimental scatter and systematic error.

THE COYOTE UNIVERSE EXTENDED: PRECISION EMULATION OF THE MATTER POWER SPECTRUM

Modern sky surveys are returning precision measurements of cosmological statistics such as weak lensing shear correlations, the distribution of galaxies, and cluster abundance. To fully exploit these observations, theorists must provide predictions that are at least as accurate as the measurements, as well as robust estimates of systematic errors that are inherent to the modeling process. In the nonlinear regime of structure formation, this challenge can only be overcome by developing a large-scale, multi-physics simulation capability covering a range of cosmological models and astrophysical processes. As a first step to achieving this goal, we have recently developed a prediction scheme for the matter power spectrum (a so-called emulator), accurate at the 1% level out to k~1/Mpc and z=1 for wCDM cosmologies based on a set of high-accuracy N-body simulations. It is highly desirable to increase the range in both redshift and wavenumber and to extend the reach in cosmological parameter space. To make progress in this direction, while minimizing computational cost, we present a strategy that maximally re-uses the original simulations. We demonstrate improvement over the original spatial dynamic range by an order of magnitude, reaching k~10 h/Mpc, a four-fold increase in redshift coverage, to z=4, and now include the Hubble parameter as a new independent variable. To further the range in k and z, a new set of nested simulations run at modest cost is added to the original set. The extension in h is performed by including perturbation theory results within a multi-scale procedure for building the emulator. This economical methodology still gives excellent error control, ~5% near the edges of the domain of applicability of the emulator. A public domain code for the new emulator is released as part of the work presented in this paper.

Systematic errors in temperature estimates from MODIS data covering the western Palearctic and their impact on a parasite development model

The modelling of habitat suitability for parasites is a growing area of research due to its association with climate change and ensuing shifts in the distribution of infectious diseases. Such models depend on remote sensing data and require accurate, high-resolution temperature measurements. The temperature is critical for accurate estimation of development rates and potential habitat ranges for a given parasite. The MODIS sensors aboard the Aqua and Terra satellites provide high-resolution temperature data for remote sensing applications. This paper describes comparative analysis of MODIS-derived temperatures relative to ground records of surface temperature in the western Palaearctic. The results show that MODIS overestimated maximum temperature values and underestimated minimum temperatures by up to 5-6 °C. The combined use of both Aqua and Terra datasets provided the most accurate temperature estimates around latitude 35-44° N, with an overestimation during spring-summer months and an underestimation in autumn-winter. Errors in temperature estimation were associated with specific ecological regions within the target area as well as technical limitations in the temporal and orbital coverage of the satellites (e.g. sensor limitations and satellite transit times). We estimated error propagation of temperature uncertainties in parasite habitat suitability models by comparing outcomes of published models. Error estimates reached 36% of annual respective measurements depending on the model used. Our analysis demonstrates the importance of adequate image processing and points out the limitations of MODIS temperature data as inputs into predictive models concerning parasite lifecycles.

항로표지 정보를 이용한 해상감시레이더의 시스템 오차 보정

Vessel traffic system uses multiple sea surveillance radars as a primary sensor to obtain maritime traffic information like as ship's position, speed, course. The systematic errors such as the range bias and the azimuth bias of the two-dimensional radar system can significantly degrade the accuracy of the radar image and target tracking information. Therefore, the systematic errors of the radar system should be corrected precisely in order to provide the accurate target information in the vessel traffic system. In this paper, it is proposed that the method compensates the range bias and the azimuth bias using AtoN information installed at VTS coverage. The radar measurement residual error model is derived from the standard error model of two-dimensional radar measurements and the position information of AtoN, and then the linear Kalman filter is designed for estimation of the systematic errors of the radar system. The proposed method is validated via Monte-Carlo runs. Also, the convergence characteristics of the designed filter and the accuracy of the systematic error estimates according to the number of AtoN information are analyzed.

Kinetic Study of the OH + Glyoxal Reaction: Experimental Evidence and Quantification of Direct OH Recycling

The kinetics of the OH + glyoxal, (HCO)2, reaction have been studied in N2 and N2/O2 bath gas from 5-80 Torr total pressure and 212-295 K, by monitoring the OH decay via laser induced fluorescence (LIF) in excess (HCO)2. The following rate coefficients, kOH+(HCO)2 = (9.7 ± 1.2), (12.2 ± 1.6), and (15.4 ± 2.0) × 10(-12) cm(3) molecule(-1) s(-1) (where errors represent a combination of statistical errors at the 2σ level and estimates of systematic errors) were measured in nitrogen at temperatures of 295, 250, and 212 K, respectively. Rate coefficient measurements were observed to be independent of total pressure but decreased following the addition of O2 to the reaction cell, consistent with direct OH recycling. OH yields, ΦOH, for this reaction were quantified experimentally for the first time as a function of total pressure, temperature, and O2 concentration. The experimental results have been parametrized using a chemical scheme where a fraction of the HC(O)CO population promptly dissociates to HCO + CO, the remaining HC(O)CO either dissociates thermally or reacts with O2 to give CO2, CO, and regenerate OH. A maximum ΦOH of (0.38 ± 0.02) was observed at 212 K, independent of total pressure, suggesting that ∼60% of the HC(O)CO population promptly dissociates upon formation. Qualitatively similar behavior is observed at 250 K, with a maximum ΦOH of (0.31 ± 0.03); at 295 K, the maximum ΦOH decreased further to (0.29 ± 0.03). From the parametrization, an OH yield of ΦOH = 0.19 is calculated for 295 K and 1 atm of air. It is shown that the proposed mechanism is consistent with previous chamber studies. While the fits are robust, experimental evidence suggests that the system is influenced by chemical activation and cannot be fully described by thermal rate coefficients. The atmospheric implications of the measurements are briefly discussed.

Analysis of the electrothermal technique for thermal property characterization of thin fibers

The transient electrothermal technique has been developed to measure the thermal conductivity and thermal diffusivity of electrically conductive or non-conductive nano-to-microscale fibers. In this work, a full theoretical model is developed in detail including the effects of radiation heat loss and non-constant heating as a result of sample temperature rise during measurement, and is compared to the more commonly used reduced model, which neglects these effects. Non-dimensional parameters are derived representing radiation heat loss and non-constant heating to identify the true parameter dependences on these effects. A numerical model is used to perform parametric analyses on the experimental setup providing results that were fitted with the full and reduced models to find thermal conductivity and thermal diffusivity. Additionally, the numerical model was used to investigate nonlinear radiation heat losses and spatially non-uniform heating effects resulting from uneven coating of the conductive layer on electrically non-conductive samples. As a result, these influences are shown to require careful consideration in the application of this technique. A clear linear relationship was found between the non-dimensional parameters and measurement error, which provides a measure for the proper estimation of systematic error induced by these effects. Using the reduced model for data reduction results in measurement percentage error equal to ten times the radiation and non-constant heating dimensionless parameters under the assumption of linear radiation heat losses (small sample temperature rise compared to ambient temperature).

Robust radio interferometric calibration using the t-distribution

A major stage of radio interferometric data processing is calibration or the estimation of systematic errors in the data and the correction for such errors. A stochastic error (noise) model is assumed, and in most cases, this underlying model is assumed to be Gaussian. However, outliers in the data due to interference or due to errors in the sky model would have adverse effects on processing based on a Gaussian noise model. Most of the shortcomings of calibration such as the loss in flux or coherence, and the appearance of spurious sources, could be attributed to the deviations of the underlying noise model. In this paper, we propose to improve the robustness of calibration by using a noise model based on Student's t distribution. Student's t noise is a special case of Gaussian noise when the variance is unknown. Unlike Gaussian noise model based calibration, traditional least squares minimization would not directly extend to a case when we have a Student's t noise model. Therefore, we use a variant of the Expectation Maximization (EM) algorithm, called the Expectation-Conditional Maximization Either (ECME) algorithm when we have a Student's t noise model and use the Levenberg-Marquardt algorithm in the maximization step. We give simulation results to show the robustness of the proposed calibration method as opposed to traditional Gaussian noise model based calibration, especially in preserving the flux of weaker sources that are not included in the calibration model.

On the information content of stellar spectra

AbstractWith the increasing quality of asteroseismic observations it is important to minimize the random and systematic errors in mode parameter estimates. To this end it is important to understand how the oscillations relate to the directly observed quantities, such as intensities and spectra, and to derived quantities, such as Doppler velocity. Here I list some of the effects we need to take into account and show an example of the impact of some of them.

Substellar multiplicity in the Hyades cluster

We present the first high-angular resolution survey for multiple systems among very low-mass stars and brown dwarfs in the Hyades open cluster. Using the Keck\,II adaptive optics system, we observed a complete sample of 16 objects with estimated masses $\lesssim$0.1 Msun. We have identified three close binaries with projected separation $\lesssim$0.11", or $\lesssim$5 AU. A number of wide, mostly faint candidate companions are also detected in our images, most of which are revealed as unrelated background sources based on astrometric and/or photometric considerations. The derived multiplicity frequency, 19+13/-6 % over the 2-350 AU range, and the rarity of systems wider than 10 AU are both consistent with observations of field very low-mass objects. In the limited 3-50 AU separation range, the companion frequency is essentially constant from brown dwarfs to solar-type stars in the Hyades cluster, which is also in line with our current knowledge for field stars. Combining the binaries discovered in this surveys with those already known in the Pleiades cluster reveals that very low-mass binaries in open clusters, as well as in star-forming regions, are skewed toward lower mass ratios ($0.6 \lesssim q \lesssim 0.8$) than are their field counterparts, a result that cannot be accounted for by selection effects. Although the possibility of severe systematic errors in model-based mass estimates for very low-mass stars cannot be completely excluded, it is unlikely to explain this difference. We speculate that this trend indicates that surveys among very low-mass field stars may have missed a substantial population of intermediate mass ratio systems, implying that these systems are more common and more diverse than previously thought.

Joint systematic error estimation algorithm for radar and automatic dependent surveillance broadcasting

Systematic error of radar cannot be accurately determined solely with the use of an automatic dependent surveillance broadcasting (ADS-B) device. This is largely, because of the device's inability to obtain the exact transmitting time of ADS-B data packets. In order to study and establish an efficient and accurate error registration algorithm for radars based on the ADS-B system, a joint systematic error estimation model of the radar and ADS-B was built. The authors showed that when receiving time is known, the time difference between radar measuring time and ADS-B data transmitting time is consistently biased, a feature that can be used to calculate the systematic error of the ADS-B receiving system. Then, they established an error estimation model based on the joint systematic error of the radar and ADS-B. The generalised least squares estimation method was adopted to solve the model. Finally, data samples verified the accuracy and effectiveness of the error registration algorithm.

HUBBLE PARAMETER MEASUREMENT CONSTRAINTS ON DARK ENERGY

We use 21 Hubble parameter versus redshift data points, from Gazta\~{n}aga et al. (2009), Stern et al. (2010), and Moresco et al. (2012), to place constraints on model parameters of constant and time-evolving dark energy cosmologies. This is the largest set of H(z) data considered to date. The inclusion of the 8 new Moresco et al. (2012) measurements results in H(z) constraints more restrictive than those derived by Chen & Ratra (2011b). These constraints are now almost as restrictive as those that follow from current Type Ia supernova (SNIa) apparent magnitude versus redshift data (Suzuki et al. 2012), which now more carefully account for systematic uncertainties. This is a remarkable result. We emphasize however that SNIa data have been studied for a longer time than the H(z) data, possibly resulting in a better estimate of potential systematic errors in the SNIa case. A joint analysis of the H(z), baryon acoustic oscillation peak length scale, and SNIa data favors a spatially-flat cosmological model currently dominated by a time-independent cosmological constant but does not exclude slowly-evolving dark energy.

Identification of orbital measurements of spacecraft

A new approach to identifying orbital measurements of spacecraft is proposed that does not require estimation of systematic errors. Here the monitoring discrepancy includes the difference of two successive measurements and extrapolation estimates. As a result, the unknown systematic errors are mutually eliminated, so it is possible to identify the measurements without knowing the magnitudes and signs of the systematic errors.

On the estimation of systematic error in regression-based predictions of climate sensitivity

An extension of a regression-based methodology for constraining climate forecasts using a multi-thousand member ensemble of perturbed climate models is presented, using the multi-model CMIP-3 ensemble to estimate the systematic model uncertainty in the prediction, with the caveat that systematic biases common to all models are not accounted for. It is shown that previous methodologies for estimating the systematic uncertainty in predictions of climate sensitivity are dependent on arbitrary choices relating to ensemble sampling strategy. Using a constrained regression approach, a multivariate predictor may be derived based upon the mean climatic state of each ensemble member, but components of this predictor are excluded if they cannot be validated within the CMIP-3 ensemble. It is found that the application of the CMIP-3 constraint serves to decrease the upper bound of likelihood for climate sensitivity when compared with previous studies, with 10th and 90th percentiles of probability at 1.5 K and 4.3 K respectively.

RETIRED A STARS: THE EFFECT OF STELLAR EVOLUTION ON THE MASS ESTIMATES OF SUBGIANTS

Doppler surveys have shown that the occurrence rate of Jupiter-mass planets appears to increase as a function of stellar mass. However, this result depends on the ability to accurately measure the masses of evolved stars. Recently, Lloyd (2011) called into question the masses of subgiant stars targeted by Doppler surveys. Lloyd argues that very few observable subgiants have masses greater than 1.5 Msun, and that most of them have masses in the range 1.0-1.2 Msun. To investigate this claim, we use Galactic stellar population models to generate an all-sky distribution of stars. We incorporate the effects that make massive subgiants less numerous, such as the initial mass function and differences in stellar evolution timescales. We find that these effects lead to negligibly small systematic errors in stellar mass estimates, in contrast to the roughly 50% errors predicted by Lloyd. Additionally, our simulated target sample does in fact include a significant fraction of stars with masses greater than 1.5 Msun, primarily because the inclusion of an apparent magnitude limit results in a Malmquist-like bias toward more massive stars, in contrast to the volume-limited simulations of Lloyd. The magnitude limit shifts the mean of our simulated distribution toward higher masses and results in a relatively smaller number of evolved stars with masses in the range 1.0-1.2 Msun. We conclude that, within the context of our present-day understanding of stellar structure and evolution, many of the subgiants observed in Doppler surveys are indeed as massive as main-sequence A stars.