Virtual Effects of Split SUSY in Higgs Productions at Linear Colliders

In split supersymmetry the gauginos and higgsinos are the only supersymmetric particles possibly accessible at foreseeable colliders like the CERN Large Hadron Collider (LHC) and the International Linear Collider (ILC). In order to account for the cosmic dark matter measured by WMAP, these gauginos and higgsinos are stringently constrained and could be explored at the colliders through their direct productions and/or virtual effects in some processes. The clean environment and high luminosity of the ILC render the virtual effects of percent level meaningful in unraveling the new physics effects. In this work we assume split supersymmetry and calculate the virtual effects of the WMAP-allowed gauginos and higgsinos in Higgs productions e+e- -> Z h and e+e- -> \nu_e \bar_\nu_e h through WW fusion at the ILC. We find that the production cross section of e+e- -> Zh can be altered by a few percent in some part of the WMAP-allowed parameter space, while the correction to the WW-fusion process e+e- -> \nu_e \bar_\nu_e h is below 1%. Such virtual effects are correlated with the cross sections of chargino pair productions and can offer complementary information in probing split supersymmetry at the colliders.Comment: more discussions added (7 pages, 10 figs

A high-resolution photon-counting breast CT system with tensor-framelet based iterative image reconstruction for radiation dose reduction

Both computer simulations and experimental phantom studies were carried out to investigate the radiation dose reduction with tensor framelet based iterative image reconstruction (TFIR) for a dedicated high-resolution spectral breast computed tomography (CT) based on a silicon strip photon-counting detector. The simulation was performed with a 10 cm-diameter water phantom including three contrast materials (polyethylene, 8 mg/ml iodine and B-100 bone-equivalent plastic). In the experimental study, the data were acquired with a 1.3 cm-diameter polymethylmethacrylate (PMMA) phantom containing iodine in three concentrations (8, 16 and 32 mg/ml) at various radiation doses (1.2, 2.4 and 3.6 mGy) and then CT images were reconstructed using filtered-back-projection (FBP) technique and TFIR technique, respectively. The image quality between these two techniques was evaluated by the quantitative analysis on contrast-to-noise ratio (CNR) and spatial resolution that was evaluated using the tasked-based modulation transfer function (MTF). Both simulation and experimental results indicated that the task-based MTF obtained from TFIR reconstruction with one-third of the radiation dose was comparable to that from FBP reconstruction for low contrast target. For high contrast target, TFIR was substantially superior to FBP reconstruction in term of spatial resolution. In addition, TFIR was able to achieve a factor of 1.6 to 1.8 increase in CNR depending on the target contrast level. This study demonstrates that TFIR can reduce the required radiation dose by a factor of two-third for a CT image reconstruction compared to FBP technique. It achieves much better CNR and spatial resolution for high contrast target in addition to retaining similar spatial resolution for low contrast target. This TFIR technique has been implemented with a graphic processing unit (GPU) system and it takes approximately 10 seconds to reconstruct a single-slice CT image, which can be potentially used in a future multi-slit multi-slice (MSMS) spiral CT system

Tunability and Sensing Properties of Plasmonic/1D Photonic Crystal

Gold/one-dimensional photonic crystal (Au/1D-PC) is fabricated and applied for sensitive sensing of glucose and different chemical molecules of various refractive indices. The Au layer thickness is optimized to produce surface plasmon resonance (SPR) at the right edge of the photonic band gap (PBG). As the Au deposition time increased to 60 sec, the PBG width is increased from 46 to 86 nm in correlation with the behavior of the SPR. The selectivity of the optimized Au/1D-PC sensor is tested upon the increase of the environmental refractive index of the detected molecules. The resonance wavelength and the PBG edges increased linearly and the transmitted intensity increased nonlinearly as the environment refractive index increased. The SPR splits to two modes during the detection of chloroform molecules based on the localized capacitive coupling of Au particles. Also, this structure shows high sensitivity at different glucose concentrations. The PBG and SPR are shifted to longer wavelengths, and PBG width is decreased linearly with a rate of 16.04 Å/(μg/mm(3)) as the glucose concentration increased. The proposed structure merits; operation at room temperature, compact size, and easy fabrication; suggest that the proposed structure can be efficiently used for the biomedical and chemical application

Development of a national VNIR soil-spectral library for soil classification and prediction of organic matter concentrations

Soil visible-near infrared diffuse reflectance spectroscopy (vis-NIR DRS) has become an important area of research in the fields of remote and proximal soil sensing. The technique is considered to be particularly useful for acquiring data for soil digital mapping, precision agriculture and soil survey. In this study, 1581 soil samples were collected from 14 provinces in China, including Tibet, Xinjiang, Heilongjiang, and Hainan. The samples represent 16 soil groups of the Genetic Soil Classification of China. After air-drying and sieving, the diffuse reflectance spectra of the samples were measured under laboratory conditions in the range between 350 and 2500 nm using a portable vis-NIR spectrometer. All the soil spectra were smoothed using the Savitzky-Golay method with first derivatives before performing multivariate data analyses. The spectra were compressed using principal components analysis and the fuzzy k-means method was used to calculate the optimal soil spectral classification. The scores of the principal component analyses were classified into five clusters that describe the mineral and organic composition of the soils. The results on the classification of the spectra are comparable to the results of other similar research. Spectroscopic predictions of soil organic matter concentrations used a combination of the soil spectral classification with multivariate calibration using partial least squares regression (PLSR). This combination significantly improved the predictions of soil organic matter (R 2 = 0.899; RPD = 3.158) compared with using PLSR alone (R 2 = 0.697; RPD = 1.817)