Fast generation of mock maps from realistic catalogs of astrophysical objects

TL;DR

This paper introduces a fast, model-independent method to generate synthetic sky maps that accurately replicate the clustering and angular properties of real astrophysical object distributions, useful for simulations and analysis.

Contribution

The novel method efficiently creates mock maps from real data, preserving clustering features and angular correlations, and can be extended to include spatial clustering inside a sphere.

Findings

Successfully mimics clustering features of real data

Preserves angular correlation function and power spectrum

Applicable to galaxy distribution data like 2MASS Redshift Survey

Abstract

We present a novel method to generate a synthetic distribution of objects (mock) on a spherical surface (i.e. a sky), by using a real distribution. The resulting surrogate map mimics the clustering features of the real data, including the effects of non-uniform exposure, if any. The method is model-independent, also preserving the angular correlation function, as well as the angular power spectrum, of the original data. It can be reliably adopted to mimic the angular clustering of objects distributed on a spherical surface and it can be easily extended to include further information, as the spatial clustering of objects distributed inside a sphere. Applications to real data are presented and discussed. In particular, we consider the distribution of galaxies recently presented in the 2MASS Redshift Survey (2MRS) catalog.