Kryoskopie

Simon, F.; Simon, A.; Fischer, O.; Glauner, R.; Ehling, L.; Onnes, H. Kamerlingh; Mathews, I. H.; Oddo, Zeit G.; Menzel, H.; Fromm, E.; Friedrich, A.; Schmid, L.; Becker, B.; Beckmann, Zeit E.; Platzmann, C.; Durand, J. F.; Roug�, E.; Henglein, F. A.; Richardson, H. L.; Robertson, P. W.; Plotnikow, W.; Hall, R. E.; Harkins, W. D.; Drapier, M. D.

doi:10.1007/bf01470033

Cited by 3 publications

(3 citation statements)

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“…Initially, we calculated the Fourier decomposition parameters with an 8th order fit to each light curve of our sample of 608 RRab stars using the method described in Simon (1979) and Simon & Teays (1982). Our Fourier parameters were found from a cosine Fourier series fit.…”

Section: Jurcsik and Kovacs Empirical Methodsmentioning

confidence: 99%

Analysis of RR Lyrae Stars in the Northern Sky Variability Survey

et al. 2006

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We use data from the Northern Sky Variability Survey (NSVS), obtained from the first generation Robotic Optical Transient Search Experiment (ROTSE-I), to identify and study RR Lyrae variable stars in the solar neighborhood. We initially identified 1197 RRab (RR0) candidate stars brighter than the ROTSE median magnitude V = 14. Periods, amplitudes, and mean V magnitudes are determined for a subset of 1188 RRab stars with well defined light curves. Metallicities are determined for 589 stars by the Fourier parameter method and by the relationship between period, amplitude, and [Fe/H]. We comment upon the difficulties of clearly classifying RRc (RR1) variables in the NSVS dataset. Distances to the RRab stars are calculated using an adopted luminosity-metallicity relation with corrections for interstellar extinction. The 589 RRab stars in our final sample are used to study the properties of the RRab population within 5 kpc of the Sun. The Bailey diagram of period versus amplitude shows that the largest component of this sample belongs to Oosterhoff type I. Metal-rich ([Fe/H] > -1) RRab stars appear to be associated with the Galactic disk. Our metal-rich RRab sample may include a thin disk as well as a thick disk population, although the uncertainties are too large to establish this. There is some evidence among the metal-rich RRab stars for a decline in scale height with increasing [Fe/H], as was found by Layden (1995). The distribution of RRab stars with -1 < [Fe/H] < -1.25 indicates that within this metallicity range the RRab stars are a mixture of stars belonging to halo and disk populations.Comment: 68 pages, 26 figures, 9 tables, accepted to A

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Section: Jurcsik and Kovacs Empirical Methodsmentioning

confidence: 99%

Analysis of RR Lyrae Stars in the Northern Sky Variability Survey

et al. 2006

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“…The central cube is designated as 555 and the centre of this cube is the standard colour. There is no dead space and also no overlapping portion between cubes [7][8][9][10].…”

Section: Background To Shade Sortingmentioning

confidence: 99%

Regression analysis to determine the optimum colour tolerance level for instrumental shade sorting

Yuen

Yeung

et al. 1999

Coloration Technology

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An investigation of the correlation between visual colour assessment and instrumental colour acceptance determination using regression analysis has been carried out. Three colour‐difference equations, CIELAB, CMC(2:1) and CIE94(2:1:1), were studied in order to determine which is the best for generating a uniform colour space/microspace for allocating the colour population in shade sorting. Determination of optimum colour tolerance for further shade sorting was also undertaken. Some 1320 pairs of dyed samples distributing around 20 shade standards were measured instrumentally and also evaluated visually by a panel of 32 observers. Percentage rejection was plotted against colour difference and different mathematical regression relationships were then imposed. As a result, both CMC and CIE94 showed better correlation between the two colour assessment methods than the CIELAB colour‐difference equation. Consequently, optimum colour tolerance limits were determined for subsequent development of shade sorting, with the findings being equally applicable to colour acceptance (shade passing).

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“…This makes the reliability of the CIELAB colour-difference equation low, especially in colour acceptance determination. It also makes it impossible to set up an optimum tolerance that is suitable for all colours in 555-shade sorting [6][7][8][9][10].…”

Section: Cielab Equationmentioning

confidence: 99%

Prediction of the best‐fit regression model to correlate instrumental colour measurement and visual assessment

Yuen

Yeung

et al. 1999

Coloration Technology

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The results of a study comparing instrumental colour measurement and visual assessment are reported. The study made use of a comprehensive set of visual colour-difference pairs including 240 dyed samples distributed around 20 colour standards and spread evenly throughout colour space. Regression analysis was then used to attempt to describe the relationship between the visual and instrumental results. This showed that linear and logistical regression gave the two best-fit models. This work also provided a useful data set for evaluation of colour-difference equations so that the optimum colour tolerance levels of various colour data sets could be determined.

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Kryoskopie

Cited by 3 publications

References 0 publications

Analysis of RR Lyrae Stars in the Northern Sky Variability Survey

Analysis of RR Lyrae Stars in the Northern Sky Variability Survey

Regression analysis to determine the optimum colour tolerance level for instrumental shade sorting

Prediction of the best‐fit regression model to correlate instrumental colour measurement and visual assessment

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