Evaluation of Data with Systematic Errors

A method is suggested to decompose the statistical and systematic uncertainties from the residues between the calculation of a theoretical model and the observed data. The residues and the parameters of the model can be obtained through the standard statistical fitting procedures. The present work concentrates on the decomposition of the total uncertainty, of which the distribution corresponds to that of the residues. The distribution of the total uncertainty is considered as two normal distributions, statistical and systematic uncertainties. The standard deviation of the statistical part, ${\ensuremath{\sigma}}_{\mathrm{stat}}$, is estimated through random parameters distributed around their best fitted values. The two normal distributions are obtained by minimizing the moments of the distribution of the residues with the fixed ${\ensuremath{\sigma}}_{\mathrm{stat}}$. The method is applied to the liquid drop model (LD). The statistical and systematic uncertainties are decomposed from the residues of the nuclear binding energies with and without the consideration of the shell effect in LD. The estimated distributions of the statistical and systematic uncertainties can well describe that of the residues. The normal assumption of the distribution of the statistical and systematic uncertainties is examined through various approaches. The comparison between the distributions of the specific nuclei and those of the statistical and systematic uncertainties are consistent with the physical considerations, although the latter two can be obtained without the knowledge of these considerations. Such as, the LD are more suitable to describe the heavy nuclei. The light and heavy nuclei are indeed distributed mostly inside the distributions of the systematic and statistical uncertainties, respectively. A similar situation is also found for the nuclei close to and far from shell. The present method is also performed for nuclei around the stability line. The results are used to investigate all measured nuclei, which show the usefulness of the uncertainty decomposition method in the exploration of the unmeasured nuclei.

At the annual general meeting of the International Committee for Radionuclide Metrology (ICRM) held last June at the Physikalisch-Technische Bundesanstalt, it was suggested that letters should be sent to the editors of journals that publish nucleardecay data, drawing attention to the need for specific statements of the uncertainties that are associated with such data. Authors frequently fail to state clearly the nature of their estimates of uncertainty, and not all editors insist on such clarity, so that evaluators of nuclear-decay data often f'md that it is necessary to consult with the individual authors to arrive at the weighting factors appropriate to their data. If uncertainties could be dearly characterized in the abstracts or in the text of papers reporthag values of nuclear parameters, there would be a corresponding shortening of the time required for the data to be evaluated and tabulated. There are many methods of stating estimates of conventional random and systematic uncertainties that are acceptable, provided that the methods used are described. Thus random error may be stated as: (i) the estimate of the standard deviation (or the square root of the variance), which is in the same units as the observed data and indicates the order of magnitude of the spread of the data: (ii) the standard error (or the estimated standard deviation of the mean of the distribution); and (ri) the estimated limits for the mean at stated levels of confidence (C1) (e.g. limits at the 99-percent CI define the range within which there is a 99:percent probability of ineluding the mean of a population). Provided that the author states the number of independent measurements made of the given parameter, or the number of degrees of freedom, these statements of random uncertainty are related uniquely to each other. The other component of the overall uncertainty is the estimate of possible systematic error. The significance, or meaning, of the estimate of systematic uncertainties should be dearly stated and also related to the method chosen to state the random uncertainties. Thus an estimate of maximum conceivable systematic uncertainties would logically be combined with a random uncertainty at a 99-percent confidence level. An appropriate fraction of the estimate of maximum conceivable systematic uncertainty would be chose to match smaller random confidence levels. The methods used to combine random and systematic uncertainties should also be stated by authors, and an explicit listing of all components of these uncertainties will allow an evaluator of nuclear data to "unravel" the statements of uncertainty and to choose

Nuclear-decay data: The statement of uncertainties

Nuclear-decay data: The statement of uncertainties

Nuclear-decay data: The statement of uncertainties

Simulation of robust resonance parameters using information theory

At the annual general meeting of the International Committee for Radionuclide Metrology (ICRM) held last June at the Physikalisch-Technische Bundesanstalt, it was suggested that letters should be sent to the editors of journals that publish nucleardecay data, drawing attention to the need for specific statements of the uncertainties that are associated with such data. Authors frequently fail to state clearly the nature of their estimates of uncertainty, and not all editors insist on such clarity, so that evaluators of nuclear-decay data often f'md that it is necessary to consult with the individual authors to arrive at the weighting factors appropriate to their data. If uncertainties could be dearly characterized in the abstracts or in the text of papers reporthag values of nuclear parameters, there would be a corresponding shortening of the time required for the data to be evaluated and tabulated. There are many methods of stating estimates of conventional random and systematic uncertainties that are acceptable, provided that the methods used are described. Thus random error may be stated as: (i) the estimate of the standard deviation (or the square root of the variance), which is in the same units as the observed data and indicates the order of magnitude of the spread of the data: (ii) the standard error (or the estimated standard deviation of the mean of the distribution); and (ri) the estimated limits for the mean at stated levels of confidence (C1) (e.g. limits at the 99-percent CI define the range within which there is a 99:percent probability of ineluding the mean of a population). Provided that the author states the number of independent measurements made of the given parameter, or the number of degrees of freedom, these statements of random uncertainty are related uniquely to each other. The other component of the overall uncertainty is the estimate of possible systematic error. The significance, or meaning, of the estimate of systematic uncertainties should be dearly stated and also related to the method chosen to state the random uncertainties. Thus an estimate of maximum conceivable systematic uncertainties would logically be combined with a random uncertainty at a 99-percent confidence level. An appropriate fraction of the estimate of maximum conceivable systematic uncertainty would be chose to match smaller random confidence levels. The methods used to combine random and systematic uncertainties should also be stated by authors, and an explicit listing of all components of these uncertainties will allow an evaluator of nuclear data to unravel the statements of uncertainty and to choose

We constrain a highly simplified semi-analytic model of galaxy formation using the $z\approx 0$ stellar mass function of galaxies. Particular attention is paid to assessing the role of random and systematic errors in the determination of stellar masses, to systematic uncertainties in the model, and to correlations between bins in the measured and modeled stellar mass functions, in order to construct a realistic likelihood function. We derive constraints on model parameters and explore which aspects of the observational data constrain particular parameter combinations. We find that our model, once constrained, provides a remarkable match to the measured evolution of the stellar mass function to $z=1$, although fails dramatically to match the local galaxy HI mass function. Several "nuisance parameters" contribute significantly to uncertainties in model predictions. In particular, systematic errors in stellar mass estimate are the dominant source of uncertainty in model predictions at $z\approx 1$, with additional, non-negligble contributions arising from systematic uncertainties in halo mass functions and the residual uncertainties in cosmological parameters. Ignoring any of these sources of uncertainties could lead to viable models being erroneously ruled out. Additionally, we demonstrate that ignoring the significant covariance between bins the observed stellar mass function leads to significant biases in the constraints derived on model parameters. Careful treatment of systematic and random errors in the constraining data, and in the model being constrained, are crucial if this methodology is to be used to test hypotheses relating to the physics of galaxy formation.

Gravitational waves emitted by neutron star black hole mergers encode key properties of neutron stars—such as their size, maximum mass, and spins—and black holes. However, it is challenging to generate accurate waveforms from these systems with numerical relativity, and not much is known about systematic uncertainties due to waveform modeling. We simulate gravitational waves from neutron star black hole mergers by hybridizing numerical relativity waveforms produced with the SpEC code with a recent numerical relativity surrogate NRHybSur3dq8Tidal. These signals are analyzed using a range of available waveform families, and statistical and systematic errors are reported. We find that at a network signal-to-noise ratio (SNR) of 30, statistical uncertainties are usually larger than systematic offsets, while at an SNR of 70, the two become comparable. The individual black hole and neutron star masses, as well as the mass ratios, are typically measured very precisely, though not always accurately at high SNR. At an SNR of 30, the neutron star tidal deformability can only be bound from above, while for louder sources, it may be measured and constrained away from zero. All neutron stars in our simulations are nonspinning, but in no case can we constrain the neutron star spin to be smaller than ∼0.4 (90% credible interval). At lower mass ratios, waveform families whose late inspiral has been tuned specifically for neutron star black hole signals typically yield the most accurate characterization of the source parameters. Their measurements are in tension with those obtained using waveform families tuned against binary neutron stars, even for mass ratios that could be relevant for both binary neutron stars and neutron star black holes mergers. At higher mass ratios, waveforms that account for higher order modes yield the best results.

An information theory approach to minimise correlated systematic uncertainty in modelling resonance parameters

This paper discusses the effect of statistical uncertainties present in Monte Carlo (MC) calculated dose distributions on the evaluation of a ‘cost function’ that expresses the suitability of a treatment plan for the intended treatment. The mathematical derivations given are valid for any ‘well-behaved’ cost function. The validity of the general expressions is demonstrated using numerical examples. It is shown that random dose uncertainties lead to statistical and systematic uncertainties on the cost function. The balance between the two types of uncertainty and the desired accuracy on the cost function presents a clear criterion for the maximum acceptable MC dose uncertainties. It is demonstrated that it is possible to remove the systematic cost function uncertainty. Finally, it is shown that when the dose distribution is close to a true optimum, MC calculations of the cost function converge to the true result as one over the number of particles simulated, i. e. much faster than the individual dose uncertainties.

The measurement of energy is one of the most important and challenging tasks of an astroparticle observatory, especially given that the energy must be accompanied by reliable statistical and systematic uncertainties. The energy scale of the Pierre Auger Observatory is based on observations made by its fluorescence detector, taking advantage of the near-calorimetric measurements afforded by this technique. This fluorescence energy calibration is transferred to the surface detector measurements via coincident fluorescence and surface detector observations of air showers at the hybrid observatory. In this contribution, we present the current status of our energy scale. We demonstrate our confidence in the cosmic ray energy assignments by discussing our methods and their systematic and statistical uncertainties. We describe the impact of recent improvements in the estimation of invisible energy, and discuss uncertainties associated with the fluorescence yield, the atmosphere, detector calibration, and the event reconstruction process.

Objective: To identify common errors in midwifery data collection and provide midwives with the rationales behind data cleaning, the importance of reliable data, and the links between data collection, research studies, and evidence-based care. Methods: A database containing records of all women who received midwifery care in Ontario that was invoiced to the Ministry of Health and Long-Term Care between April 1, 2006, and March 31, 2009, was obtained. Data cleaning was performed to assure that the data set was as complete and accurate as possible. Duplicate records were identified and removed. Missing, inconsistent, and implausible data were identified and corrected where possible or removed. Results: Common data errors included inappropriate use of open text fields and drop-down menus, incorrect interpretation of “planned place of birth,” reporting of outcomes that should be mutually exclusive, and reporting of incorrect, incomplete, or missing information. Discussion: Midwives have an important role in the collection of health information that is complete and accurate. Several common errors were identified that, if corrected, would improve the quality of midwifery data and in turn would contribute to high‐quality research, which will inform midwifery practice, policy‐makers, and women and their families about midwifery care. This article has been peer reviewed.

Regional satellite navigation system contains geostationary satellites (GEO) of partial subsatellite point, subsatellite points of these satellites are away from ground tracking network. These satellites are important to improve the geometric dilution of precision (GDOP) for positioning and time service in sevice area. Comparing with geostationary satellite (GEO) of other subsatellite point, ground tracking geometry condition of GEO of partial subsatellite point is worse, precision of orbit determination of this kind of satellites is not stable and easier to be affected by systemic error. In this paper the principle of POD based on time synchronization mode was proposed particularly and thoroughly. Integrative orbit determination of all satellites could provide precise clock offsets of all satellites and all stations, precise orbit determination (POD) were realized using pseudorange data without clock offset. The technique of single-satellite orbit determination could use all stations which participated in integrative orbit determination, ground tracking geometry condition was improved greatly, at the same time, it could achieve integrative solution of systemic error, POD of this kind of GEO was realized, which could get rid of the restriction of satellite laser ranging data (SLR). Precision of orbit determination were analyzed based on four orbit determination strategies, including solving solar radiation pressure parameters, empirical force parameters and common systemic error, solving solar radiation pressure parameters and common systemic error, solving solar radiation pressure parameters, empirical force parameters and fixing up common systemic error, solving solar radiation pressure parameters and fixing up common systemic error. The experiment based on ground data results indicated that precision of orbit determination based on the strategies of solving solar radiation pressure parameters and fixing up common systemic error was highest. The orbit accuracy in radial component was better than 1 m, which was evaluated with SLR data, the 2 h orbital prediction errors were 1 m, the precision was stable, POD of GEO of partial subsatellite point was come true.

A common claim is that emerging and future climate change is rendering traditional conceptions of uncertainty and risk obsolete. This is because a changing climate makes it quite a challenge to calculate uncertainties, establishing the measurable uncertainty as the basis for quantifying risk. Approaches that are capable of accommodating and possibly countering the wickedness caused by increasing uncertainty are necessary, the argument holds. Following up on previous studies of learning–knowledge and adapting to a changing climate, this article provides an analysis of how differences in the understanding of uncertainty and risk inform and determine governmental adaptation policies and actions of the local and central government in Norway, also discussing governance implications. The study finds that the understanding of uncertainty and risk generally is poor at the local level, but better at the state level, especially among highly educated staff with a background in, for example, natural sciences and engineering. On the other hand, a traditional understanding of uncertainty and risk is dominating: seeking to establish measurable uncertainty as a basis for quantifying risk. The article discusses combining different approaches of uncertainty and risk, thereby introducing a broader basis for governance, also implying multi-level network governance. On the one hand, this may help the local–central government in handling wicked problems of adapting to a changing climate but on other hand, it also possibly nurture struggles between different knowledge bases and stakeholder interest, thereby fuelling the wickedness of adaptation policies.