The Large-Scale Distribution of X-ray Clusters of Galaxies

Mathematics – Logic

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Scientific paper

We describe the development of an X-ray selected cluster sample, the SRCS, and its application to a quantitative study of large-scale structure. The SRCS has been constructed from data acquired during the ROSAT All Sky Survey. We have selected 345 cluster candidates, to a limiting flux of f_X =~ 1*E(-12) ergcm , from a 2800sqrdeg region of the survey centered around the South Galactic Pole (SGP). The criteria for selection include X-ray parameters (\eg extent information) and galaxy density. The galaxy data have been taken from the COSMOS/UKST digitised object catalogue. By combining optical spectroscopic data from our own observations and from the literature, we have been able to identify 154 of the 345 candidates as clusters with redshifts. An additional 36 candidates have been identified with non-cluster X-ray sources. Using the SRCS, we have been able to make the first determination of the spatial correlation function, xi_ {CC}, for an X-ray selected cluster sample. We have found that the clustering patterns of the SRCS are similar, on all scales, to those of automated digitised optical cluster samples, \eg the APM and EDCC samples. In contrast, when we compare the SRCS to samples of Abell clusters, clusters which were selected by eye from photographic plates, we measure a smaller correlation length (r_0(Xray) ≲16Mpc cf. r_0(Abell) gtrsim20 Mpc ) and less power on large (rgtrsim 40Mpc ) scales. We attribute these differences to artificial clustering in the Abell catalogue, as evidenced by the lack of anisotropy seen in the SRCS correlation function. Our data provides strong support for the claims of Sutherland 1988 that some of the clustering power measured from Abell cluster samples is due to projection effects. Comparisons between the SCRS xi_ {CC} and state of the art theoretical predictions seem to rule out low density, positive cosmological constant, CDM models. Larger X-ray cluster samples would be required to be able to distinguish between other, subtly different, models, \eg standard CDM, mixed CDM and tilted CDM models. Therefore, we plan to produce a larger, more complete, SRCS in the near future.

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