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The arrival of the Vera C. Rubin Observatory's Legacy Survey of Space andTime (LSST), Euclid-Wide and Roman wide area sensitive surveys will herald anew era in strong lens science in which the number of strong lenses known isexpected to rise from $\mathcal{O}(10^3)$ to $\mathcal{O}(10^5)$. However,current lens-finding methods still require time-consuming follow-up visualinspection by strong-lens experts to remove false positives which is only setto increase with these surveys. In this work we demonstrate a range of methodsto produce calibrated probabilities to help determine the veracity of any givenlens candidate. To do this we use the classifications from citizen science andmultiple neural networks for galaxies selected from the Hyper Suprime-Cam (HSC)survey. Our methodology is not restricted to particular classifier types andcould be applied to any strong lens classifier which produces quantitativescores. Using these calibrated probabilities, we generate an ensembleclassifier, combining citizen science and neural network lens finders. We findsuch an ensemble can provide improved classification over the individualclassifiers. We find a false positive rate of $10^{-3}$ can be achieved with acompleteness of $46\%$, compared to $34\%$ for the best individual classifier.Given the large number of galaxy-galaxy strong lenses anticipated in LSST, suchimprovement would still produce significant numbers of false positives, inwhich case using calibrated probabilities will be essential for populationanalysis of large populations of lenses.

A Bayesian Approach to Strong Lens Finding in the Era of Wide-area Surveys