Mapping the Galactic halo with main-sequence and RR Lyrae stars

We present an analysis of Galactic halo structure, substructure, and metallicity traced by mainsequence and RR Lyrae stars selected from the SDSS stripe 82 and CFHT Legacy Survey data sets. The main result of the study based on SDSS stripe 82 data is a 2D map of the Galactic halo that reaches distances of 100 kpc and traces previously known and new halo substructures, such as the Sagittarius and Pisces tidal streams. We present strong direct evidence, based on both RR Lyrae and main-sequence stars, that the halo stellar number density profile significantly steepens beyond 30 kpc from the Galactic center. The steepening of the density profile beyond 30 kpc is also evident in the distribution of main-sequence stars observed by the CFHT Legacy Survey along four Galactic lines of sight. In the two CFHT sightlines where we do not detect significant substructure, the median metallicity is found to be independent of distance within systematic uncertainties ([Fe/H] ∼ −1.5 ± 0.1 dex within 30 kpc of the Galactic Center)

Time Variability of Quasars: the Structure Function Variance

Significant progress in the description of quasar variability has been recently made by employing SDSS and POSS data. Common to most studies is a fundamental assumption that photometric observations at two epochs for a large number of quasars will reveal the same statistical properties as well-sampled light curves for individual objects. We critically test this assumption using light curves for a sample of $\sim$2,600 spectroscopically confirmed quasars observed about 50 times on average over 8 years by the SDSS stripe 82 survey. We find that the dependence of the mean structure function computed for individual quasars on luminosity, rest-frame wavelength and time is qualitatively and quantitatively similar to the behavior of the structure function derived from two-epoch observations of a much larger sample. We also reproduce the result that the variability properties of radio and X-ray selected subsamples are different. However, the scatter of the variability structure function for fixed values of luminosity, rest-frame wavelength and time is similar to the scatter induced by the variance of these quantities in the analyzed sample. Hence, our results suggest that, although the statistical properties of quasar variability inferred using two-epoch data capture some underlying physics, there is significant additional information that can be extracted from well-sampled light curves for individual objects.Comment: Presented at the "Classification and Discovery in Large Astronomical Surveys" meeting, Ringberg Castle, 14-17 October, 200

Efficient use of simultaneous multi-band observations for variable star analysis

The luminosity changes of most types of variable stars are correlated in the different wavelengths, and these correlations may be exploited for several purposes: for variability detection, for distinction of microvariability from noise, for period search or for classification. Principal component analysis is a simple and well-developed statistical tool to analyze correlated data. We will discuss its use on variable objects of Stripe 82 of the Sloan Digital Sky Survey, with the aim of identifying new RR Lyrae and SX Phoenicis-type candidates. The application is not straightforward because of different noise levels in the different bands, the presence of outliers that can be confused with real extreme observations, under- or overestimated errors and the dependence of errors on the magnitudes. These particularities require robust methods to be applied together with the principal component analysis. The results show that PCA is a valuable aid in variability analysis with multi-band data.Comment: 8 pages, 5 figures, Workshop on Astrostatistics and Data Mining in Astronomical Databases, May 29-June 4 2011, La Palm

Quasar Selection Based on Photometric Variability

We develop a method for separating quasars from other variable point sources using SDSS Stripe 82 light curve data for ~10,000 variable objects. To statistically describe quasar variability, we use a damped random walk model parametrized by a damping time scale, tau, and an asymptotic amplitude (structure function), SF_inf. With the aid of an SDSS spectroscopically confirmed quasar sample, we demonstrate that variability selection in typical extragalactic fields with low stellar density can deliver complete samples with reasonable purity (or efficiency, E). Compared to a selection method based solely on the slope of the structure function, the inclusion of the tau information boosts E from 60% to 75% while maintaining a highly complete sample (98%) even in the absence of color information. For a completeness of C=90%, E is boosted from 80% to 85%. Conversely, C improves from 90% to 97% while maintaining E=80% when imposing a lower limit on tau. With the aid of color selection, the purity can be further boosted to 96%, with C= 93%. Hence, selection methods based on variability will play an important role in the selection of quasars with data provided by upcoming large sky surveys, such as Pan-STARRS and the Large Synoptic Survey Telescope (LSST). For a typical (simulated) LSST cadence over 10 years and a photometric accuracy of 0.03 mag (achieved at i~22), C is expected to be 88% for a simple sample selection criterion of tau>100 days. In summary, given an adequate survey cadence, photometric variability provides an even better method than color selection for separating quasars from stars.Comment: (v2) 50 pages, accepted to Ap

Metode Urutan Parsial Untuk Menyelesaikan Masalah Program Linier Fuzzy Tidak Penuh

Not fully fuzzylinear programming problem have two shapes of objecyive function. that is triangular fuzzy number and trapezoidal fuzzy number. The decision variables and constants right segment only has a triangular fuzzy number. Partial order method can be used to solve not fully fuzzy linear programming problem with decision variables and constants right segment are triangular fuzzy number. The crisp optimal objective function value generated from the partial order method

Search for high-amplitude Delta Scuti and RR Lyrae stars in Sloan Digital Sky Survey Stripe 82 using principal component analysis

We propose a robust principal component analysis (PCA) framework for the exploitation of multi-band photometric measurements in large surveys. Period search results are improved using the time series of the first principal component due to its optimized signal-to-noise ratio.The presence of correlated excess variations in the multivariate time series enables the detection of weaker variability. Furthermore, the direction of the largest variance differs for certain types of variable stars. This can be used as an efficient attribute for classification. The application of the method to a subsample of Sloan Digital Sky Survey Stripe 82 data yielded 132 high-amplitude Delta Scuti variables. We found also 129 new RR Lyrae variables, complementary to the catalogue of Sesar et al., 2010, extending the halo area mapped by Stripe 82 RR Lyrae stars towards the Galactic bulge. The sample comprises also 25 multiperiodic or Blazhko RR Lyrae stars.Comment: 23 pages, 17 figure

Blueberry blight caused by Bipolaris cynodontis in Argentina

Blueberry (Vaccinium corymbosum) production in Argentina has grown remarkably in the last 8 years due to the high demand worldwide in the off-season fresh market. Since it is a new crop in Argentina, diseases are just starting to become problematic for farmers. Surveys have been conducted since 2000 to detect new pathogenic associations and to evaluate their distribution, incidence and severity in different blueberry varieties and localities.Facultad de Ciencias Agrarias y Forestale