Selection functions of strong lens finding neural networks
A. Herle C. M. O'Riordan S. Vegetti
Convolution Neural Networks trained for the task of lens finding with similar
architecture and training data as is commonly found in the literature are
biased classifiers. An understanding of the selection function of lens finding
neural networks will be key to fully realising the potential of the large
samples of strong gravitational lens systems that will be found in upcoming
wide-field surveys. We use three training datasets, representative of those
used to train galaxy-galaxy and galaxy-quasar lens finding neural networks. The
networks preferentially select systems with larger Einstein radii and larger
sources with more concentrated source-light distributions. Increasing the
detection significance threshold to 12$\sigma$ from 8$\sigma$ results in 50 per
cent of the selected strong lens systems having Einstein radii
$\theta_\mathrm{E}$ $\ge$ 1.04 arcsec from $\theta_\mathrm{E}$ $\ge$ 0.879
arcsec, source radii $R_S$ $\ge$ 0.194 arcsec from $R_S$ $\ge$ 0.178 arcsec and
source S\'ersic indices $n_{\mathrm{Sc}}^{\mathrm{S}}$ $\ge$ 2.62 from
$n_{\mathrm{Sc}}^{\mathrm{S}}$ $\ge$ 2.55. The model trained to find lensed
quasars shows a stronger preference for higher lens ellipticities than those
trained to find lensed galaxies. The selection function is independent of the
slope of the power-law of the mass profiles, hence measurements of this
quantity will be unaffected. The lens finder selection function reinforces that
of the lensing cross-section, and thus we expect our findings to be a general
result for all galaxy-galaxy and galaxy-quasar lens finding neural networks.