Breaking the self-averaging properties of spatial galaxy fluctuations in the Sloan Digital Sky Survey - Data Release Six
Francesco Sylos Labini, Nikolay L. Vasilyev, Yurij V. Baryshev

TL;DR
This study reveals that galaxy fluctuations in SDSS data are not self-averaging on large scales, challenging standard cosmological models and indicating inhomogeneity up to 100 Mpc/h.
Contribution
It demonstrates the breakdown of self-averaging properties in galaxy distributions at large scales in SDSS data, questioning assumptions of homogeneity in cosmology.
Findings
PDF of fluctuations varies systematically in different sub-volumes at scales >30 Mpc/h
Galaxy structures exhibit power-law correlations up to 30 Mpc/h
Large amplitude fluctuations are inconsistent with LCDM predictions
Abstract
Statistical analyses of finite sample distributions usually assume that fluctuations are self-averaging, i.e. that they are statistically similar in different regions of the given sample volume. By using the scale-length method, we test whether this assumption is satisfied in several samples of the Sloan Digital Sky Survey Data Release Six. We find that the probability density function (PDF) of conditional fluctuations, filtered on large enough spatial scales (i.e., r>30 Mpc/h), shows relevant systematic variations in different sub-volumes of the survey. Instead for scales r<30 Mpc/h the PDF is statistically stable, and its first moment presents scaling behavior with a negative exponent around one. Thus while up to 30 Mpc/h galaxy structures have well-defined power-law correlations, on larger scales it is not possible to consider whole sample average quantities as meaningful and useful…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
