Rigorous Formulation of Finite-Sample and Finite-Window Effects in Galaxy Clustering

TL;DR

This paper develops a statistical framework for analyzing galaxy clustering in finite surveys, clarifying how finite sample effects influence observed clustering statistics and their interpretation.

Contribution

It provides a rigorous formulation of finite-sample effects in galaxy clustering, revealing how finiteness alone can produce features traditionally attributed to physical correlations.

Findings

Finite sample size can produce non-zero higher-order correlations.

The integral constraint arises from finiteness, not estimation artifacts.

Counts-in-cells and environmental measures are statistically distinct.

Abstract

Galaxy surveys provide finite catalogs of objects observed within bounded volumes, yet clustering statistics are often interpreted using theoretical frameworks developed for infinite point processes. In this work, we formulate key statistical quantities directly for finite point processes and examine the structural consequences of finite-number and finite-window constraints. We show that several well-known features of galaxy survey analysis arise naturally from finiteness alone. In particular, non-vanishing higher-order connected correlations can occur even in statistically independent samples when the total number of points is fixed, and the integral constraint in two-point statistics appears as an exact identity implied by the finite-number condition rather than as an estimator artifact. We further demonstrate that counts-in-cells and point-centered environmental measures correspond to distinct statistical ensembles. Using Palm conditioning, we derive an exact relation between random-cell and point-centered statistics, showing that the latter probe a tilted version of the underlying distribution. These results provide a probabilistic framework for separating structural effects imposed by finite sampling from correlations reflecting genuine astrophysical processes. The formulation presented here remains valid for realistic survey geometries and finite data sets and clarifies the interpretation of commonly used clustering statistics in galaxy surveys.