Jump to ContentJump to Main Navigation
The Large-Scale Structure of the Universe$
Users without a subscription are not able to see the full content.

P. J. E. Peebles

Print publication date: 2020

Print ISBN-13: 9780691209838

Published to Princeton Scholarship Online: May 2021

DOI: 10.23943/princeton/9780691209838.001.0001

Show Summary Details
Page of

PRINTED FROM PRINCETON SCHOLARSHIP ONLINE (www.princeton.universitypressscholarship.com). (c) Copyright Princeton University Press, 2022. All Rights Reserved. An individual user may print out a PDF of a single chapter of a monograph in PRSO for personal use.date: 18 May 2022

n-Point Correlation Functions: Descriptive Statistics

n-Point Correlation Functions: Descriptive Statistics

(p.138) III n-Point Correlation Functions: Descriptive Statistics
The Large-Scale Structure of the Universe

P. J. E. Peebles

Princeton University Press

This chapter explores the statistical pattern of the galaxy distribution. It focuses on n-point correlation functions (analogs of the autocorrelation function and higher moments for a continuous function), the descriptive statistics that have proved useful. The approach has also proved useful in many other applications. Of considerable practical importance has been the fact that there is a simple linear equation relating the directly observable angular correlation function to the wanted spatial function. This means the translation from one to the other is fairly easy, and equally important it makes it easy to say how the statistical estimates ought to scale with the depth of the survey and hence to test for possible contamination of the estimates by systematic errors. A third useful result is that the dynamics of the galaxy distribution can be treated in terms of the mass correlation functions: the statistic that proves useful for the reduction of the data may also be useful for the analysis of the theory.

Keywords:   galaxy distribution, n-point correlation functions, autocorrelation function, linear equation, mass correlation functions, descriptive statistics

Princeton Scholarship Online requires a subscription or purchase to access the full text of books within the service. Public users can however freely search the site and view the abstracts and keywords for each book and chapter.

Please, subscribe or login to access full text content.

If you think you should have access to this title, please contact your librarian.

To troubleshoot, please check our FAQs , and if you can't find the answer there, please contact us.