A PCA-based automated finder for galaxy-scale strong lenses
Abstract
We present an algorithm using principal component analysis (PCA) to subtract galaxies
from imaging data and also two algorithms to find strong, galaxy-scale gravitational
lenses in the resulting residual image. The combined method is optimised to find full or
partial Einstein rings. Starting from a pre-selection of potential massive galaxies, we
first perform a PCA to build a set of basis vectors. The galaxy images are reconstructed
using the PCA basis and subtracted from the data. We then filter the residual image with
two different methods. The first uses a curvelet (curved wavelets) filter of the residual
images to enhance any curved/ring feature. The resulting image is transformed in polar
coordinates, centred on the lens galaxy. In these coordinates, a ring is turned into a
line, allowing us to detect very faint rings by taking advantage of the integrated
signal-to-noise in the ring (a line in polar coordinates). The second way of analysing the
PCA-subtracted images identifies structures in the residual images and assesses whether
they are lensed images according to their orientation, multiplicity, and elongation. We
applied the two methods to a sample of simulated Einstein rings as they would be observed
with the ESA Euclid satellite in the VIS band. The polar coordinate
transform allowed us to reach a completeness of 90% for a purity of 86%, as soon as the
signal-to-noise integrated in the ring was higher than 30 and almost independent of the
size of the Einstein ring. Finally, we show with real data that our PCA-based galaxy
subtraction scheme performs better than traditional subtraction based on model fitting to
the data. Our algorithm can be developed and improved further using machine learning and
dictionary learning methods, which would extend the capabilities of the method to more
complex and diverse galaxy shapes.
Origin : Publication funded by an institution
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