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Paper: Multiple Regression Redshift Calibration for Clusters of Galaxies
Volume: 61, Astronomical Data Analysis Software and Systems III
Page: 263
Authors: Kalinkov, M.; Kuneva, I.; Valtchanov, I.
Abstract: A new procedure for calibration of distances to ACO (Abell et al.1989) clusters of galaxies has been developed. In the previous version of the Reference Catalog of ACO Clusters of Galaxies (Kalinkov & Kuneva 1992) an attempt has been made to compare various calibration schemes. For the Version 93 we have made some refinements. Many improvements from the early days of the photometric calibration have been made --- from Rowan-Robinson (1972), Corwin (1974), Kalinkov & Kuneva (1975), Mills Hoskins (1977) to more complicated --- Leir & van den Bergh (1977), Postman et al.(1985), Kalinkov Kuneva (1985, 1986, 1990), Scaramella et al.(1991), Zucca et al. (1993). It was shown that it is impossible to use the same calibration relation for northern (A) and southern (ACO) clusters of galaxies. Therefore the calibration have to be made separately for both catalogs. Moreover it is better if one could find relations for the 274 A-clusters, studied by the authors of ACO. We use the luminosity distance for H0=100km/s/Mpc and q0 = 0.5 and we have 1200 clusters with measured redshifts. The first step is to fit log(z) on m10 (magnitude of the tenth rank galaxy) for A-clusters and on m1, m3 and m10 for ACO clusters. The second step is to take into account the K-correction and the Scott effect (Postman et al.1985) with iterative process. To avoid the initial errors of the redshift estimates in A- and ACO catalogs we adopt Hubble's law for the apparent radial distribution of galaxies in clusters. This enable us to calculate a new cluster richness from preliminary redshift estimate. This is the third step. Further continues the study of the correlation matrix between log(z) and prospective predictors --- new richness groups, BM, RS and A types, radio and X-ray fluxes, apparent separations between the first three brightest galaxies, mean population (gal/sq.deg), Multiple linear as well as nonlinear regression estimators are found. Many clusters that deviate by more than 2.5 sigmas are rejected. Each case is examined for observational errors, substructuring, foreground and background. Some of the clusters are doubtful --- most probably they have to be excluded from the catalogs. The multiple regressions allow us to estimate redshift in the range 0.02 to 0.2 with an error of 7 percent.
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