  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.
