scholarly journals A Revised View of the Linear Polarization in the Subparsec Core of M87 at 7 mm

2021 ◽  
Vol 922 (2) ◽  
pp. 180
Author(s):  
Jongho Park ◽  
Keiichi Asada ◽  
Masanori Nakamura ◽  
Motoki Kino ◽  
Hung-Yi Pu ◽  
...  
Keyword(s):  
Linear Polarization ◽  
Elliptical Galaxy ◽  
Complex Structure ◽  
Synthetic Data ◽  
Data Sets ◽  
Generalized Polarization ◽  
Polarization Structure ◽  
The Core ◽  
Instrumental Polarization ◽  
Complex Polarization

Abstract The linear polarization images of the jet in the giant elliptical galaxy M87 have previously been observed with Very Long Baseline Array at 7 mm. They exhibit a complex polarization structure surrounding the optically thick and compact subparsec-scale core. However, given the low level of linear polarization in the core, it is required to verify that this complex structure does not originate from residual instrumental polarization signals in the data. We have performed a new analysis of the same data sets observed in four epochs by using the Generalized Polarization CALibration pipeline (GPCAL). This novel instrumental polarization calibration pipeline overcomes the limitations of LPCAL, a conventional calibration tool used in the previous M87 studies. The resulting images show a compact linear polarization structure with its peak nearly coincident with the total intensity peak, which is significantly different from the results of previous studies. The core linear polarization is characterized as fractional polarization of ∼0.2%–0.6% and polarization angles of ∼66°–92°, showing moderate variability. We demonstrate that, based on tests with synthetic data sets, LPCAL using calibrators having complex polarization structures cannot achieve sufficient calibration accuracy to obtain the true polarization image of M87 due to a breakdown of the “similarity approximation.” We find that GPCAL obtains more accurate D-terms than LPCAL by using observed closure traces of calibrators that are insensitive to both antenna gain and polarization leakage corruptions. This study suggests that polarization imaging of very weakly polarized sources has become possible with the advanced instrumental polarization calibration techniques.

scholarly journals Methods for estimating uncertainty in factor analytic solutions

2014 ◽  
Vol 7 (3) ◽  
pp. 781-797 ◽  
Author(s):  
P. Paatero ◽  
S. Eberly ◽  
S. G. Brown ◽  
G. A. Norris
Keyword(s):  
Environmental Protection Agency ◽  
Synthetic Data ◽  
Analytic Solutions ◽  
Data Sets ◽  
Random Errors ◽  
Data Set ◽  
Factor Analytic ◽  
Uncertainty Estimates ◽  
Multilinear Engine ◽  
Analytic Models

Abstract. The EPA PMF (Environmental Protection Agency positive matrix factorization) version 5.0 and the underlying multilinear engine-executable ME-2 contain three methods for estimating uncertainty in factor analytic models: classical bootstrap (BS), displacement of factor elements (DISP), and bootstrap enhanced by displacement of factor elements (BS-DISP). The goal of these methods is to capture the uncertainty of PMF analyses due to random errors and rotational ambiguity. It is shown that the three methods complement each other: depending on characteristics of the data set, one method may provide better results than the other two. Results are presented using synthetic data sets, including interpretation of diagnostics, and recommendations are given for parameters to report when documenting uncertainty estimates from EPA PMF or ME-2 applications.

Reverse time migration

Geophysics ◽  
1983 ◽  
Vol 48 (11) ◽  
pp. 1514-1524 ◽  
Author(s):  
Edip Baysal ◽  
Dan D. Kosloff ◽  
John W. C. Sherwood
Keyword(s):  
Wave Amplitude ◽  
Synthetic Data ◽  
Data Sets ◽  
Field Observations ◽  
Reverse Time ◽  
Reverse Time Migration ◽  
Time Migration ◽  
Migration Method ◽  
Zero Offset ◽  
Time Extrapolation

Migration of stacked or zero‐offset sections is based on deriving the wave amplitude in space from wave field observations at the surface. Conventionally this calculation has been carried out through a depth extrapolation. We examine the alternative of carrying out the migration through a reverse time extrapolation. This approach may offer improvements over existing migration methods, especially in cases of steeply dipping structures with strong velocity contrasts. This migration method is tested using appropriate synthetic data sets.

Quasi-2D inversion of DCR and TDEM data for shallow investigations

Geophysics ◽  
2011 ◽  
Vol 76 (4) ◽  
pp. F239-F250 ◽  
Author(s):  
Fernando A. Monteiro Santos ◽  
Hesham M. El-Kaliouby
Keyword(s):  
Joint Inversion ◽  
Synthetic Data ◽  
Data Sets ◽  
Complex Environments ◽  
Inversion Algorithm ◽  
2D Inversion ◽  
New Approach ◽  
Earth Models ◽  
Time Domain Electromagnetic ◽  
Inversion Techniques

Joint or sequential inversion of direct current resistivity (DCR) and time-domain electromagnetic (TDEM) data commonly are performed for individual soundings assuming layered earth models. DCR and TDEM have different and complementary sensitivity to resistive and conductive structures, making them suitable methods for the application of joint inversion techniques. This potential joint inversion of DCR and TDEM methods has been used by several authors to reduce the ambiguities of the models calculated from each method separately. A new approach for joint inversion of these data sets, based on a laterally constrained algorithm, was found. The method was developed for the interpretation of soundings collected along a line over a 1D or 2D geology. The inversion algorithm was tested on two synthetic data sets, as well as on field data from Saudi Arabia. The results show that the algorithm is efficient and stable in producing quasi-2D models from DCR and TDEM data acquired in relatively complex environments.

A Statistical Test for the Stability of Covariance Structure

2013 ◽  
Vol 63 (2) ◽  
Author(s):  
Nur Syahidah Yusoff ◽  
Maman Abdurachman Djauhari
Keyword(s):  
Covariance Matrix ◽  
Complex Structure ◽  
Covariance Structure ◽  
Statistical Test ◽  
Dispersion Measure ◽  
Data Sets ◽  
Scalar Representation ◽  
Multivariate Dispersion ◽  
Sample Covariance ◽  
The Stability

The stability of covariance matrix is a major issue in multivariate analysis. As can be seen in the literature, the most popular and widely used tests are Box M-test and Jennrich J-test introduced by Box in 1949 and Jennrich in 1970, respectively. These tests involve determinant of sample covariance matrix as multivariate dispersion measure. Since it is only a scalar representation of a complex structure, it cannot represent the whole structure. On the other hand, they are quite cumbersome to compute when the data sets are of high dimension since they do not only involve the computation of determinant of covariance matrix but also the inversion of a matrix. This motivates us to propose a new statistical test which is computationally more efficient and, if it is used simultaneously with M-test or J-test, we will have a better understanding about the stability of covariance structure. An example will be presented to illustrate its advantage

Bayesian Classifier for Sparsity-Promoting Feature Selection

2015 ◽  
Vol 29 (06) ◽  
pp. 1550022 ◽  
Author(s):  
Danlei Xu ◽  
Lan Du ◽  
Hongwei Liu ◽  
Penghui Wang
Keyword(s):  
Feature Selection ◽  
Synthetic Data ◽  
Original Data ◽  
Radar Data ◽  
Bayesian Classifier ◽  
Classification Model ◽  
Data Sets ◽  
Data Set ◽  
Classification Boundary ◽  
Nonlinear Mappings

A Bayesian classifier for sparsity-promoting feature selection is developed in this paper, where a set of nonlinear mappings for the original data is performed as a pre-processing step. The linear classification model with such mappings from the original input space to a nonlinear transformation space can not only construct the nonlinear classification boundary, but also realize the feature selection for the original data. A zero-mean Gaussian prior with Gamma precision and a finite approximation of Beta process prior are used to promote sparsity in the utilization of features and nonlinear mappings in our model, respectively. We derive the Variational Bayesian (VB) inference algorithm for the proposed linear classifier. Experimental results based on the synthetic data set, measured radar data set, high-dimensional gene expression data set, and several benchmark data sets demonstrate the aggressive and robust feature selection capability and comparable classification accuracy of our method comparing with some other existing classifiers.

Sparse reflectivity inversion for nonstationary seismic data with surface-related multiples: Numerical and field-data experiments

Geophysics ◽  
2017 ◽  
Vol 82 (3) ◽  
pp. R199-R217 ◽  
Author(s):  
Xintao Chai ◽  
Shangxu Wang ◽  
Genyang Tang
Keyword(s):  
Seismic Data ◽  
Resolution Enhancement ◽  
Synthetic Data ◽  
Data Sets ◽  
Data Set ◽  
Anelastic Attenuation ◽  
Seismic Resolution ◽  
Text Filtering ◽  
The Stability ◽  
Reflectivity Inversion

Seismic data are nonstationary due to subsurface anelastic attenuation and dispersion effects. These effects, also referred to as the earth’s [Formula: see text]-filtering effects, can diminish seismic resolution. We previously developed a method of nonstationary sparse reflectivity inversion (NSRI) for resolution enhancement, which avoids the intrinsic instability associated with inverse [Formula: see text] filtering and generates superior [Formula: see text] compensation results. Applying NSRI to data sets that contain multiples (addressing surface-related multiples only) requires a demultiple preprocessing step because NSRI cannot distinguish primaries from multiples and will treat them as interference convolved with incorrect [Formula: see text] values. However, multiples contain information about subsurface properties. To use information carried by multiples, with the feedback model and NSRI theory, we adapt NSRI to the context of nonstationary seismic data with surface-related multiples. Consequently, not only are the benefits of NSRI (e.g., circumventing the intrinsic instability associated with inverse [Formula: see text] filtering) extended, but also multiples are considered. Our method is limited to be a 1D implementation. Theoretical and numerical analyses verify that given a wavelet, the input [Formula: see text] values primarily affect the inverted reflectivities and exert little effect on the estimated multiples; i.e., multiple estimation need not consider [Formula: see text] filtering effects explicitly. However, there are benefits for NSRI considering multiples. The periodicity and amplitude of the multiples imply the position of the reflectivities and amplitude of the wavelet. Multiples assist in overcoming scaling and shifting ambiguities of conventional problems in which multiples are not considered. Experiments using a 1D algorithm on a synthetic data set, the publicly available Pluto 1.5 data set, and a marine data set support the aforementioned findings and reveal the stability, capabilities, and limitations of the proposed method.

scholarly journals EVALUATION OF THE INFLUENCE OF SOCIAL AND ECONOMIC DETERMINANTS ON THE STATE OF PUBLIC HEALTH ON THE BASIS OF MICROIMITATION MODELING

2017 ◽  
pp. 79-90
Author(s):  
Dmytro Shushpanov ◽  
Volodymyr Sarioglo
Keyword(s):  
Research Policy ◽  
Synthetic Data ◽  
Simulation Models ◽  
The State ◽  
Data Sets ◽  
Economic Determinants ◽  
Micro Simulation ◽  
State Employment ◽  
Health Research Policy ◽  
Main Determinants

In the article the essence and peculiarities of microimitational modeling are considered. The advantages of microimitational models over the statistics models are substantiated. Micro-simulation models, that prognosticate somehow dynamic changes in health and which are most appropriate to use in development and health research policy, such as POHEM, CORSIM and Sife Paths, are outlined. It is proposed to use elements of statistical and dynamic microimitation modeling, agent modeling and the concept of a life course for the estimation of the influence social and economic determinants. The synthetic model of population which has been formed on the basis of representative data sets of sample surveys of living conditions of households and economic activity of the population of the State Employment Service of Ukraine, as well as microdata of the Multicultural Survey of the Population of Ukraine (2012) and the Medical and Demographic Survey (2013). The generalized scheme of the method of microimulation modeling of the influence of social and economic determinants on the health status of the population of Ukraine has been developed. The influence of the main determinants on the health of certain age, gender and social and economic groups of the population is estimated on the basis of the methodology of synthetic data.

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