A maximum likelihood approach to estimating correlation functions

Eric Jones Baxter, Eduardo Rozo

Research output: Contribution to journalArticlepeer-review

2 Scopus citations


We define a maximum likelihood (ML for short) estimator for the correlation function, ξ, that uses the same pair counting observables (D, R, DD, DR, RR) as the standard Landy & Szalay (LS for short) estimator. The ML estimator outperforms the LS estimator in that it results in smaller measurement errors at any fixed random point density. Put another way, the ML estimator can reach the same precision as the LS estimator with a significantly smaller random point catalog. Moreover, these gains are achieved without significantly increasing the computational requirements for estimating ξ. We quantify the relative improvement of the ML estimator over the LS estimator and discuss the regimes under which these improvements are most significant. We present a short guide on how to implement the ML estimator and emphasize that the code alterations required to switch from an LS to an ML estimator are minimal.

Original languageEnglish (US)
Article number62
JournalAstrophysical Journal
Issue number1
StatePublished - Dec 10 2013


  • large-scale structure of universe

ASJC Scopus subject areas

  • Astronomy and Astrophysics
  • Space and Planetary Science


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