Principal component analysis-based inversion of effective temperatures for late-type stars

We show how the range of application of the principal component analysis-based inversion method of Paletou et al. (2015) can be extended to late-type stars data. Besides being an extension of its original application domain, for FGK stars, we also used synthetic spectra for our learning database. We discuss our results on effective temperatures against previous evaluations made available from Vizier and Simbad services at CDS.Comment: Accepted for publication in A&

Assessing extrema of empirical principal component functions

The difficulties of estimating and representing the distributions of functional data mean that principal component methods play a substantially greater role in functional data analysis than in more conventional finite-dimensional settings. Local maxima and minima in principal component functions are of direct importance; they indicate places in the domain of a random function where influence on the function value tends to be relatively strong but of opposite sign. We explore statistical properties of the relationship between extrema of empirical principal component functions, and their counterparts for the true principal component functions. It is shown that empirical principal component funcions have relatively little trouble capturing conventional extrema, but can experience difficulty distinguishing a ``shoulder'' in a curve from a small bump. For example, when the true principal component function has a shoulder, the probability that the empirical principal component function has instead a bump is approximately equal to 1/2. We suggest and describe the performance of bootstrap methods for assessing the strength of extrema. It is shown that the subsample bootstrap is more effective than the standard bootstrap in this regard. A ``bootstrap likelihood'' is proposed for measuring extremum strength. Exploratory numerical methods are suggested.Comment: Published at http://dx.doi.org/10.1214/009053606000000371 in the Annals of Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical Statistics (http://www.imstat.org

Removal of Spectro-Polarimetric Fringes by 2D Pattern Recognition

We present a pattern-recognition based approach to the problem of removal of polarized fringes from spectro-polarimetric data. We demonstrate that 2D Principal Component Analysis can be trained on a given spectro-polarimetric map in order to identify and isolate fringe structures from the spectra. This allows us in principle to reconstruct the data without the fringe component, providing an effective and clean solution to the problem. The results presented in this paper point in the direction of revising the way that science and calibration data should be planned for a typical spectro-polarimetric observing run.Comment: ApJ, in pres

Genetic Diversity of Selected Upland Rice Genotypes (Oryza sativa L.) for Grain Yield and Related Traits

Seventy-seven upland rice genotypes including popular cultivars in Nigeria and introduced varieties selected from across rice-growing regions of the world were evaluated under optimal upland ecology. These genotypes were characterised for 10 traits and the quantitative data subjected to Pearson correlation matrix, Principal Component Analysis and cluster analysis to determine the level of diversity and degree of association existing between grain yield and its related component traits. Yield and most related component traits exhibited higher PCV compared to growth parameters. Yield had the highest PCV (41.72%) while all other parameters had low to moderate GCV. Genetic Advance (GA) ranged from 9.88% for plant height at maturity to 41.08% for yield. High heritability estimates were recorded for 1000 grain weight (88.71%), days to 50% flowering (86.67%) and days to 85% maturity (71.98%). Furthermore, grain yield showed significant positive correlation with days to 50% flowering and number of panicles m-2. Three cluster groups were obtained based on the UPGMA and the first three principal components explained about 64.55% of the total variation among the 10 characters. The PCA results suggests that characters such as grain yield, days to flowering, leaf area and plant height at maturity were the principal discriminatory traits for this rice germplasm indicating that selection in favour of these traits might be effective in this population and environment

Evaluating Local Garlic (Allium sativum L ) Accessions using Multivariate Analysis Based on agro-morphological Characters in Southern Tigray, Ethiopia

To assess the diversity and trait association of eight local garlic accessions, a study was conducted in southern Tigray, Ethiopia using Randomized Complete Block Design with three replications during 2014 cropping season. Cluster analysis using Ward's method classified the eight accessions into three clusters. Cluster I and III were equally with three number of accessions while, cluster II contains two accessions. The three principal component analysis with Eigen value greater than one explained 81% of the variability in the data set. Using the first principal component and the second principal component indirect selection could be effective using all accessions except accessions three and eight. The accessions by trait biplot showed that traits under study have positive association signifying narrow angle between them. Key words: cluster analysis, principal component, garlic accessions

Optimized Principal Component Analysis on Coronagraphic Images of the Fomalhaut System

We present the results of a study to optimize the principal component analysis (PCA) algorithm for planet detection, a new algorithm complementing ADI and LOCI for increasing the contrast achievable next to a bright star. The stellar PSF is constructed by removing linear combinations of principal components, allowing the flux from an extrasolar planet to shine through. The number of principal components used determines how well the stellar PSF is globally modelled. Using more principal components may decrease the number of speckles in the final image, but also increases the background noise. We apply PCA to Fomalhaut VLT NaCo images acquired at 4.05 micron with an apodized phase plate. We do not detect any companions, with a model dependent upper mass limit of 13-18 M_Jup from 4-10 AU. PCA achieves greater sensitivity than the LOCI algorithm for the Fomalhaut coronagraphic data by up to 1 magnitude. We make several adaptations to the PCA code and determine which of these prove the most effective at maximizing the signal-to-noise from a planet very close to its parent star. We demonstrate that optimizing the number of principal components used in PCA proves most effective for pulling out a planet signal.Comment: Accepted for publication in ApJ, 7 pages, 9 figure