2006
DOI: 10.1111/j.1365-2966.2006.10236.x
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A catalogue of RR Lyrae stars from the Northern Sky Variability Survey

Abstract: A search for RR Lyrae stars has been conducted in the publicly available data of the Northern Sky Variability Survey. Candidates have been selected by the statistical properties of their variation; the standard deviation, skewness and kurtosis with appropriate limits determined from a sample 314 known RRab and RRc stars listed in the General Catalogue of Variable Stars. From the period analysis and light-curve shape of over 3000 candidates 785 RR Lyrae have been identified of which 188 are previously unknown. … Show more

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Cited by 50 publications
(61 citation statements)
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“…Compared to incidences 5.1% (SF07) and 4.3% (Wils et al 2006), our percentage is significantly higher and roughly approximates the percentage based on precise measurements -48% ) based on Kepler data, or 47% (Jurcsik at al. 2009a), who utilized precise ground-base observations.…”
Section: Statistics Of the Blazhko Variables In The Samplementioning
confidence: 89%
See 1 more Smart Citation
“…Compared to incidences 5.1% (SF07) and 4.3% (Wils et al 2006), our percentage is significantly higher and roughly approximates the percentage based on precise measurements -48% ) based on Kepler data, or 47% (Jurcsik at al. 2009a), who utilized precise ground-base observations.…”
Section: Statistics Of the Blazhko Variables In The Samplementioning
confidence: 89%
“…Among others, Kinemuchi et al (2006) and Wils et al (2006) utilized data from Northern sky variability survey (NSVS, Woźniak et al 2004), e.g. Kovács (2005), Szczygieł et al (2009) studied RR Lyraes based on ASAS data, and Drake et al (2013) dealt with RR Lyraes measured in Catalina sky survey 1 .…”
Section: Introductionmentioning
confidence: 99%
“…Good examples in the field of classification, are the series of papers on MACHO, OGLE and ASAS data (for RR Lyrae stars, for example, see Alcock et al (2003), Soszynski et al (2003) and Wils et al (2006), for MACHO, OGLE and the NSVS respectively), where catalogues of distinct variability classes are compiled according to several selection criteria (rules) and, in some occasions, with human intervention. Basically, the problem of supervised classification of variable objects can be described as that of defining general boundaries (hard or fuzzy) in the hyperspace of the features that describe the classes, based on a set of examples of each class.…”
Section: Automatic Variability Classificationmentioning
confidence: 99%
“…This is partially due to the moderate number of known stars: at the moment of this writing we know 30 more or less bright RRd stars (V < 14 mag) in the Galactic field discovered by the NSVS and ASAS all-sky variability surveys (Wils et al 2006;Szczygiel & Fabrycky 2007;Pilecki & Szczygiel 2007;Khruslov 2007). Only two stars (GSC 4868-0831 and V372 Ser) are known with V < 11.5 mag.…”
Section: Introductionmentioning
confidence: 99%