Systematic Uncertainties Associated with the Cosmological Analysis of the First PAN-STARRS1 Type Ia Supernova Sample

We probe the systematic uncertainties from the 113 Type Ia supernovae (SN Ia) in the Pan-STARRS1 (PS1) sample along with 197 SN Ia from a combination of low-redshift surveys. The companion paper by Rest et al. describes the photometric measurements and cosmological inferences from the PS1 sample. The largest systematic uncertainty stems from the photometric calibration of the PS1 and low-z samples. We increase the sample of observed Calspec standards from 7 to 10 used to define the PS1 calibration system. The PS1 and SDSS-II calibration systems are compared and discrepancies up to ∼0.02 mag are recovered. We find uncertainties in the proper way to treat intrinsic colors and reddening produce differences in the recovered value of w up to 3%. We estimate masses of host galaxies of PS1 supernovae and detect an insignificant difference in distance residuals of the full sample of 0.037 ± 0.031 mag for host galaxies with high and low masses. Assuming flatness and including systematic uncertainties in our analysis of only SNe measurements, we find w = -1.120^+0.360_-0.206(Stat)^+0.269_-0.291(Sys). With additional constraints from Baryon acoustic oscillation, cosmic microwave background (CMB) (Planck) and H₀ measurements, we find w = -1.166^+0.072_-0.069 and Ω_m = 0.280^+0.013_-0.012 (statistical and systematic errors added in quadrature). The significance of the inconsistency with w = -1 depends on whether we use Planck or Wilkinson Microwave Anisotropy Probe measurements of the CMB: w_{BAO+H0+SN+WMAP} = -1.124^+0.083_-0.065.

A novel radial basis function neural network principal component analysis scheme for PMU-based wide-area power system monitoring

A novel model-based principal component analysis (PCA) method is proposed in this paper for wide-area power system monitoring, aiming to tackle one of the critical drawbacks of the conventional PCA, i.e. the incapability to handle non-Gaussian distributed variables. It is a significant extension of the original PCA method which has already shown to outperform traditional methods like rate-of-change-of-frequency (ROCOF). The ROCOF method is quick for processing local information, but its threshold is difficult to determine and nuisance tripping may easily occur. The proposed model-based PCA method uses a radial basis function neural network (RBFNN) model to handle the nonlinearity in the data set to solve the no-Gaussian issue, before the PCA method is used for islanding detection. To build an effective RBFNN model, this paper first uses a fast input selection method to remove insignificant neural inputs. Next, a heuristic optimization technique namely Teaching-Learning-Based-Optimization (TLBO) is adopted to tune the nonlinear parameters in the RBF neurons to build the optimized model. The novel RBFNN based PCA monitoring scheme is then employed for wide-area monitoring using the residuals between the model outputs and the real PMU measurements. Experimental results confirm the efficiency and effectiveness of the proposed method in monitoring a suite of process variables with different distribution characteristics, showing that the proposed RBFNN PCA method is a reliable scheme as an effective extension to the linear PCA method.

Spatio-Temporal Rich Model For Motion Vector Steganalysis

We propose a spatio-temporal rich model of motion vector planes as a part of a full steganalytic system against motion vector based steganography. Superior detection accuracy of the rich model over the previous methods has been lately demonstrated for digital images in both spatial and DCT domain. It has not been heretofore used for detection of motion vector steganography. We also introduced a transformation so as to extend the feature set with temporal residuals. We carried out the tests along with most recent motion vector steganalysis and steganography methods. Test results show that the proposed model delivers an outstanding performance compared to the previous methods.

Modeling the Rossiter-McLaughlin Effect: Impact of the Convective Center-to-limb Variations in the Stellar Photosphere

bservations of the Rossiter–McLaughlin (RM) effect provide information on star–planet alignments, which can inform planetary migration and evolution theories. Here, we go beyond the classical RM modeling and explore the impact of a convective blueshift that varies across the stellar disk and non-Gaussian stellar photospheric profiles. We simulated an aligned hot Jupiter with a four-day orbit about a Sun-like star and injected center-to-limb velocity (and profile shape) variations based on radiative 3D magnetohydrodynamic simulations of solar surface convection. The residuals between our modeling and classical RM modeling were dependent on the intrinsic profile width and v sin i; the amplitude of the residuals increased with increasing v sin i and with decreasing intrinsic profile width. For slowly rotating stars the center-to-limb convective variation dominated the residuals (with amplitudes of 10 s of cm s−1 to ~1 m s−1); however, for faster rotating stars the dominant residual signature was due a non-Gaussian intrinsic profile (with amplitudes from 0.5 to 9 m s−1). When the impact factor was 0, neglecting to account for the convective center-to-limb variation led to an uncertainty in the obliquity of ~10°–20°, even though the true v sin i was known. Additionally, neglecting to properly model an asymmetric intrinsic profile had a greater impact for more rapidly rotating stars (e.g., v sin i = 6 km s−1) and caused systematic errors on the order of ~20° in the measured obliquities. Hence, neglecting the impact of stellar surface convection may bias star–planet alignment measurements and consequently theories on planetary migration and evolution.