Neural network prediction of model parameters for strong lensing samples from Hyper Suprime-Cam Survey
arxiv(2024)
摘要
Galaxies that cause the strong gravitational lensing of background galaxies
provide us crucial information about the distribution of matter around them.
Traditional modelling methods that analyse such strong lenses are both time and
resource consuming, require sophisticated lensing codes and modelling
expertise. To study the large lens population expected from imaging surveys
such as LSST, we need fast and automated analysis methods. In this work, we
build and train a simple convolutional neural network with an aim to rapidly
predict model parameters of gravitational lenses. We focus on the most
important lens mass model parameters, namely, the Einstein radius, the axis
ratio and the position angle of the major axis of the mass distribution. The
network is trained on a variety of simulated data with an increasing degree of
realism and shows satisfactory performance on simulated test data. The trained
network is then applied to the real sample of galaxy-scale candidate lenses
from the Subaru HSC, a precursor survey to LSST. Unlike the simulated lenses,
we do not have the ground truth for the real lenses. Therefore, we have
compared our predictions with those from YattaLens, a lens modelling pipeline.
Additionally, we also compare the parameter predictions for 10 HSC lenses that
were also studied by other conventional modelling methods. These comparisons
show a fair quantitative agreement on the Einstein radius, although the axis
ratio and the position angle from the network as well as the individual
modelling methods, seem to have systematic uncertainties beyond the quoted
errors.
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