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LSST and Euclid must address the daunting challenge of analyzing theunprecedented volumes of imaging and spectroscopic data that thesenext-generation instruments will generate. A promising approach to overcomingthis challenge involves rapid, automatic image processing using appropriatelytrained Deep Learning (DL) algorithms. However, reliable application of DLrequires large, accurately labeled samples of training data. Galaxy Zoo Express(GZX) is a recent experiment that simulated using Bayesian inference todynamically aggregate binary responses provided by citizen scientists via theZooniverse crowd-sourcing platform in real time. The GZX approach enablescollaboration between human and machine classifiers and provides rapidlygenerated, reliably labeled datasets, thereby enabling online training ofaccurate machine classifiers. We present selected results from GZX and show howthe Bayesian aggregation engine it uses can be extended to efficiently provideobject-localization and bounding-box annotations of two-dimensional data withquantified reliability. DL algorithms that are trained using these annotationswill facilitate numerous panchromatic data modeling tasks includingmorphological classification and substructure detection in direct imaging, aswell as decontamination and emission line identification for slitlessspectroscopy. Effectively combining the speed of modern computational analyseswith the human capacity to extrapolate from few examples will be critical ifthe potential of forthcoming large-scale surveys is to be realized.

Modeling with the Crowd: Optimizing the Human-Machine Partnership with Zooniverse