Galaxy And Mass Assembly (GAMA): Accurate Panchromatic Photometry from Optical Priors using LAMBDAR

TL;DR

The paper introduces LAMBDAR, a new algorithm for consistent, accurate multi-band photometry across diverse images, improving spectral energy distribution calculations and reducing outliers in galaxy property measurements.

Contribution

LAMBDAR is a novel code that enables robust panchromatic photometry from optical priors, handling images with varying resolution and confusion, and improves upon previous datasets.

Findings

LAMBDAR accurately recovers photometry in diverse regimes.

Photometry from LAMBDAR shows fewer outliers and lower scatter.

Increased detection of UV and mid-IR sources enhances analysis capabilities.

Abstract

We present the Lambda Adaptive Multi-Band Deblending Algorithm in R (LAMBDAR), a novel code for calculating matched aperture photometry across images that are neither pixel- nor PSF-matched, using prior aperture definitions derived from high resolution optical imaging. The development of this program is motivated by the desire for consistent photometry and uncertainties across large ranges of photometric imaging, for use in calculating spectral energy distributions. We describe the program, specifically key features required for robust determination of panchromatic photometry: propagation of apertures to images with arbitrary resolution, local background estimation, aperture normalisation, uncertainty determination and propagation, and object deblending. Using simulated images, we demonstrate that the program is able to recover accurate photometric measurements in both high-resolution, low-confusion, and low-resolution, high-confusion, regimes. We apply the program to the 21-band photometric dataset from the Galaxy And Mass Assembly (GAMA) Panchromatic Data Release (PDR; Driver et al. 2016), which contains imaging spanning the far-UV to the far-IR. We compare photometry derived from LAMBDAR with that presented in Driver et al. (2016), finding broad agreement between the datasets. Nonetheless, we demonstrate that the photometry from LAMBDAR is superior to that from the GAMA PDR, as determined by a reduction in the outlier rate and intrinsic scatter of colours in the LAMBDAR dataset. We similarly find a decrease in the outlier rate of stellar masses and star formation rates using LAMBDAR photometry. Finally, we note an exceptional increase in the number of UV and mid-IR sources able to be constrained, which is accompanied by a significant increase in the mid-IR colour-colour parameter-space able to be explored.