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Modeling with the crowd: Optimizing the human-machine partnership with Zooniverse

Published online by Cambridge University Press:  10 June 2020

Hugh Dickinson Open the ORCID record for Hugh Dickinson [Opens in a new window] ,
Lucy Fortson ,
Claudia Scarlata ,
Melanie Beck  and
Mike Walmsley
Hugh Dickinson
Affiliation:
School of Physics and Astronomy, University of Minnesota, 116 Church Street SE, Minneapolis, MN 55455, USA
Lucy Fortson
Affiliation:
School of Physics and Astronomy, University of Minnesota, 116 Church Street SE, Minneapolis, MN 55455, USA
Claudia Scarlata
Affiliation:
School of Physics and Astronomy, University of Minnesota, 116 Church Street SE, Minneapolis, MN 55455, USA
Melanie Beck
Affiliation:
School of Physics and Astronomy, University of Minnesota, 116 Church Street SE, Minneapolis, MN 55455, USA
Mike Walmsley
Affiliation:
Oxford Astrophysics, Denys Wilkinson Building, Keble Road, Oxford OX1 3RH, UK
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Abstract

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LSST and Euclid must address the daunting challenge of analyzing the unprecedented volumes of imaging and spectroscopic data that these next-generation instruments will generate. A promising approach to overcoming this challenge involves rapid, automatic image processing using appropriately trained Deep Learning (DL) algorithms. However, reliable application of DL requires large, accurately labeled samples of training data. Galaxy Zoo Express (GZX) is a recent experiment that simulated using Bayesian inference to dynamically aggregate binary responses provided by citizen scientists via the Zooniverse crowd-sourcing platform in real time. The GZX approach enables collaboration between human and machine classifiers and provides rapidly generated, reliably labeled datasets, thereby enabling online training of accurate machine classifiers. We present selected results from GZX and show how the Bayesian aggregation engine it uses can be extended to efficiently provide object-localization and bounding-box annotations of two-dimensional data with quantified reliability. DL algorithms that are trained using these annotations will facilitate numerous panchromatic data modeling tasks including morphological classification and substructure detection in direct imaging, as well as decontamination and emission line identification for slitless spectroscopy. Effectively combining the speed of modern computational analyses with the human capacity to extrapolate from few examples will be critical if the potential of forthcoming large-scale surveys is to be realized.

Keywords

SurveysMorphologyCitizen ScienceMachine Learning
Type
Contributed Papers
Information
Proceedings of the International Astronomical Union , Volume 15 , Symposium S341: Challenges in Panchromatic Modelling with Next Generation Facilities , November 2019 , pp. 99 - 103
Copyright
© International Astronomical Union 2020

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