5,700 research outputs found
Effect due to charge symmetry violation on the Paschos-Wolfenstein relation
The modification of the Paschos-Wolfenstein relation is investigated when the
charge symmetry violations of valence and sea quark distributions in the
nucleon are taken into account. We also study qualitatively the impact of
charge symmetry violation (CSV) effect on the extraction of
from deep inelastic neutrino- and antineutrino-nuclei
scattering within the light-cone meson-baryon fluctuation model. We find that
the effect of CSV is too small to give a sizable contribution to the NuTeV
result with various choices of mass difference inputs, which is consistence
with the prediction that the strange-antistrange asymmetry can account for
largely the NuTeV deviation in this model. It is noticeable that the effect of
CSV might contribute to the NuTeV deviation when the larger difference between
the internal momentum scales, of the proton and of
the neutron, is considered.Comment: 15 Latex pages, no figure, final version to appear in PR
Nucleon sea in the effective chiral quark model
The asymmetries of both light-flavor antiquark and
strange-antistrange distributions of the nucleon sea are
considered with more details in the effective chiral quark model. We find that
the asymmetric distribution of light-flavor antiquarks
matches the experiment data well and that the asymmetry of strange and
antistrange distributions can bring about 60-100% correction to the NuTeV
anomaly of , which are three standard deviations from the
world average value measured in other electroweak processes. The results on the
correction to the NuTeV anomaly are insensitive to the inputs of the
constituent quark distributions and the cut-off parameters. The ratios of
and are also discussed, and it is
found that the ratio is compatible with the available
experiments with an additional symmetric sea contribution being considered
effectively.Comment: 24 Latex pages, 8 figure
Hyperspectral Imaging System Model Implementation and Analysis
In support of hyperspectral imaging system design and parameter trade-off research, an analytical end-to-end model to simulate the remote sensing system pipeline and to forecast remote sensing system performance has been implemented. It is also being made available to the remote sensing community through a website. Users are able to forecast hyperspectral imaging system performance by defining an observational scenario along with imaging system parameters.
For system modeling, the implemented analytical model includes scene, sensor and target characteristics as well as atmospheric features, background spectral reflectance statistics, sensor specifications and target class reflectance statistics. The sensor model has been extended to include the airborne ProspecTIR instrument. To validate the analytical model, experiments were designed and conducted. The predictive system model has been verified by comparing the forecast results to ones obtained using real world data collected during the RIT SHARE 2012 collection.
Results include the use of large calibration panels to show the predicted radiance consistent with the collected data. Grass radiance predicted from ground truth reflectance data also compare well with the real world collected data, and an eigenvector analysis also supports the validity of the predictions. Two examples of subpixel target detection scenario are presented. One is to detect subpixel wood yellow painted planks in an asphalt playground, and the other is to detect subpixel green painted wood planks in grass. To validate our system performance, the detection performance are analyzed using receiver operating characteristic (ROC) curves in a comprehensive scenario setting. The predicted ROC result of the yellow planks matches well the ROC derived from collected data. However, the predicted ROC curve of green planks differs from collected data ROC curve. Additional experiments were conducted and analyzed to discuss the possible reasons of the mismatch including scene characterization inaccuracy. Several subpixel target detection parameter trade-off analyses are given, including relative calibration error vs SNR, the relationship among probability of detection, meteorological range, pixel fill factor, relative calibration error and false alarm rate. These trade-off analyses explain the utility of this model for hyperspectral imaging system design and research
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