Selection functions of strong lens finding neural networks
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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 12σ from 8σ results in 50 per cent of the selected strong lens systems having Einstein radii θ_E ≥ 1.04 arcsec from θ_E ≥ 0.879 arcsec, source radii R_S ≥ 0.194 arcsec from R_S ≥ 0.178 arcsec and source Sérsic indices n_Sc^S ≥ 2.62 from n_Sc^S ≥ 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.
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