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Convolution Neural Networks trained for the task of lens finding with similararchitecture and training data as is commonly found in the literature arebiased classifiers. An understanding of the selection function of lens findingneural networks will be key to fully realising the potential of the largesamples of strong gravitational lens systems that will be found in upcomingwide-field surveys. We use three training datasets, representative of thoseused to train galaxy-galaxy and galaxy-quasar lens finding neural networks. Thenetworks preferentially select systems with larger Einstein radii and largersources with more concentrated source-light distributions. Increasing thedetection significance threshold to 12$\sigma$ from 8$\sigma$ results in 50 percent of the selected strong lens systems having Einstein radii$\theta_\mathrm{E}$ $\ge$ 1.04 arcsec from $\theta_\mathrm{E}$ $\ge$ 0.879arcsec, source radii $R_S$ $\ge$ 0.194 arcsec from $R_S$ $\ge$ 0.178 arcsec andsource 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 lensedquasars shows a stronger preference for higher lens ellipticities than thosetrained to find lensed galaxies. The selection function is independent of theslope of the power-law of the mass profiles, hence measurements of thisquantity will be unaffected. The lens finder selection function reinforces thatof the lensing cross-section, and thus we expect our findings to be a generalresult for all galaxy-galaxy and galaxy-quasar lens finding neural networks.

Selection functions of strong lens finding neural networks