correlcalc: A `Generic' Recipe for Calculation of Two-point Correlation function

TL;DR

This paper introduces correlcalc, a Python-based method for rapid computation of galaxy two-point correlation functions applicable to any geometry or cosmology, suitable for quick validation and educational use.

Contribution

It presents a versatile, efficient Python recipe for calculating galaxy two-point correlation functions adaptable to arbitrary geometries and cosmologies.

Findings

Fast computation on low-spec computers

Applicable to any geometry or cosmology

Useful for model validation and teaching

Abstract

This article provides a method for quick computation of galaxy two-point correlation function(2pCF) from redshift surveys using python. One of the salient features of this approach is that it can be used for calculating galaxy clustering for any arbitrary geometry (or Cosmology) model. Being efficient enough to run fast on a low-spec desktop computer, this `recipe' can be used for quick validation of alternative models and for pedagogical purposes.