Multi-Gaussian fitting Algorithm to determine multi-band photometry and photometric redshifts of LABOCA and Herschel sources in proto-cluster environments

TL;DR

This paper presents a Multi-Gaussian fitting algorithm for multi-band photometry and photometric redshift estimation of high-redshift galaxy candidates in proto-cluster environments using LABOCA and Herschel data.

Contribution

The study introduces a novel Multi-Gaussian fitting algorithm for cross-matching and deriving photometry and redshifts from LABOCA and SPIRE observations in proto-cluster fields.

Findings

Successful cross-matching of LABOCA and SPIRE sources.

Effective photometric redshift estimation for high-redshift galaxies.

Development of a Python-based code for the analysis.

Abstract

This research focuses on identifying high redshift galaxies from LABOCA(LArge APEX BOlometer CAmera) and SPIRE(The Spectral and Photometric Imaging Receiver) maps towards proto-cluster candidates initially selected from the SPT (South pole telescope) survey. Based on the Multi-Gaussian fitting algorithm, we cross-match all significant LABOCA sources at SPIRE wavelengths based on their coordinates and signal to noise ratio to derive their photometry at 250, 350, 500 and 870 $\mu m$. We use this information to calculate a photometric redshift for SPT sources towards cluster fields. The code was developed in the Python programming environment.