Cross-Identification Performance from Simulated Detections: GALEX and SDSS

TL;DR

This paper evaluates methods for cross-matching astronomical sources from GALEX and SDSS surveys using simulated data, proposing improved criteria to enhance reliability and reduce false associations.

Contribution

It introduces a general simulation-based approach for assessing cross-identification methods and recommends optimized criteria for reliable SDSS-GALEX source matching.

Findings

Bayesian probability thresholds reduce false associations

Refined simulations improve cross-matching accuracy

Recommended criteria minimize artifacts in catalogs

Abstract

We investigate the quality of associations of astronomical sources from multi-wavelength observations using simulated detections that are realistic in terms of their astrometric accuracy, small-scale clustering properties and selection functions. We present a general method to build such mock catalogs for studying associations, and compare the statistics of cross-identifications based on angular separation and Bayesian probability criteria. In particular, we focus on the highly relevant problem of cross-correlating the ultraviolet Galaxy Evolution Explorer (GALEX) and optical Sloan Digital Sky Survey (SDSS) surveys. Using refined simulations of the relevant catalogs, we find that the probability thresholds yield lower contamination of false associations, and are more efficient than angular separation. Our study presents a set of recommended criteria to construct reliable cross-match catalogs between SDSS and GALEX with minimal artifacts.