Kavli Affiliate: Philip J. Marshall
| First 5 Authors: Philip Holloway, Philip J. Marshall, Aprajita Verma, Anupreeta More, Raoul Cañameras
| Summary:
The arrival of the Vera C. Rubin Observatory’s Legacy Survey of Space and
Time (LSST), Euclid-Wide and Roman wide area sensitive surveys will herald a
new era in strong lens science in which the number of strong lenses known is
expected to rise from $mathcal{O}(10^3)$ to $mathcal{O}(10^5)$. However,
current lens-finding methods still require time-consuming follow-up visual
inspection by strong-lens experts to remove false positives which is only set
to increase with these surveys. In this work we demonstrate a range of methods
to produce calibrated probabilities to help determine the veracity of any given
lens candidate. To do this we use the classifications from citizen science and
multiple neural networks for galaxies selected from the Hyper Suprime-Cam (HSC)
survey. Our methodology is not restricted to particular classifier types and
could be applied to any strong lens classifier which produces quantitative
scores. Using these calibrated probabilities, we generate an ensemble
classifier, combining citizen science and neural network lens finders. We find
such an ensemble can provide improved classification over the individual
classifiers. We find a false positive rate of $10^{-3}$ can be achieved with a
completeness of $46%$, compared to $34%$ for the best individual classifier.
Given the large number of galaxy-galaxy strong lenses anticipated in LSST, such
improvement would still produce significant numbers of false positives, in
which case using calibrated probabilities will be essential for population
analysis of large populations of lenses.
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