A new approach to the optimization of the extraction of astrometric and photometric information from multi-wavelength images in cosmological fields

TL;DR

This paper introduces a novel pipeline combining Voronoi tessellation, Bayesian cross-matching, and active contours to optimize source extraction and spectral energy distribution creation from multi-wavelength cosmological images.

Contribution

It presents a new integrated framework for automatic source identification, tagging, and SED production in multi-wavelength astrophysical data, enhancing data analysis efficiency.

Findings

Effective source tagging and cross-matching in cosmological fields

Generation of high-quality SEDs for detected objects

Applicable to other astrophysical scenarios like star forming regions

Abstract

This paper describes a new approach to the optimization of information extraction in multi-wavelength image cubes of cosmological fields. The objective is to create a framework for the automatic identification and tagging of sources according to various criteria (isolated source, partially overlapped, fully overlapped, cross-matched, etc) and to set the basis for the automatic production of the SEDs (spectral energy distributions) for all objects detected in the many multi-wavelength images in cosmological fields.In order to do so, a processing pipeline is designed that combines Voronoi tessellation, Bayesian cross-matching, and active contours to create a graph-based representation of the cross-match probabilities. This pipeline produces a set of SEDs with quality tags suitable for the application of already-proven data mining methods. The pipeline briefly described here is also applicable to other astrophysical scenarios such as star forming regions.