Estimating the large-scale structure of the universe using QSO carbon IV absorbers

Details Estimating the large-scale structure of the universe using QSO carbon IV absorbers Estimating the large-scale structure of the universe using QSO carbon IV absorbers

Aug 2001

adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2001phdt........16l&link_type=abstract

Thesis (PhD). THE UNIVERSITY OF CHICAGO, Source DAI-B 62/07, p. 3261, Jan 2002, 110 pages.

Statistics

Computation

3

Scientific paper

This work is motivated by a data catalog consisting of information on lines of sight from the Earth to distant quasi-stellar objects (QSOs) and the Carbon IV absorbers that lie on them. We are interested in the large-scale structure of the universe, and the absorber catalog provides a unique and interesting way to examine this structure. On large scales, previous studies have only used pairs of absorbers on the same line of sight to obtain estimates describing the clustering of absorbers. It is clear that absorbers on different lines of sight contain information about the degree of clustering. We describe a method to estimate the reduced second moment function for the absorbers that takes into account this across-line-of- sight information. Simulations suggest that the reduction in standard errors of estimates may be substantial with data similar to those being collected by the ongoing Sloan Digital Sky Survey. With the current absorber catalog, we find evidence of clustering on scales up to 100 h-1 Mpc. The across-line-of-sight information strengthens the evidence for clustering on scales from 100 to 150 h-1 Mpc found in earlier studies (Quashnock and Stein (1999)) using only along-line-of-sight information. We also look at methods to obtain confidence intervals for the reduced second moment function, mainly for point processes observed in a single contiguous window, but also for line-of-sight data. We use a number of resampling schemes and find the marked point method to be the best method in terms of empirical coverage, normalized interval width and computation. Furthermore, the marked point method is the least sensitive to the size of the subregions used for resampling. We also find the simple approximation method of dividing the sample and treating the subsamples as independent replicates to work reasonably well.

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