Cool Neighbors: Combining Artificial Intelligence and Citizen Science to Chart the Sun’s Cosmic Neighborhood

Abstract

In this case study, we describe the design and implementation of the Backyard Worlds: Cool Neighbors citizen science project, which combines image-level deep learning with Zooniverse-hosted online crowdsourcing to mine large astronomical sky maps for rare celestial objects called “brown dwarfs.” Specifically, Cool Neighbors uses machine learning to pre-select the sky images shown to volunteers. Cool Neighbors represents an excellent opportunity to interrogate the effects of incorporating artificial intelligence into a citizen science project; its sibling project, Backyard Worlds: Planet 9, uses no artificial intelligence, providing a natural point of comparison for participant engagement metrics. Through analysis of more than 10 million total Zooniverse classifications from the combination of Cool Neighbors and Backyard Worlds: Planet 9, among other results, we find (1) Cool Neighbors volunteers perform ~3x more classifications per unit of time invested than Backyard Worlds: Planet 9 volunteers, and (2) each registered Cool Neighbors participant performs ~2–5x more classifications than each registered Backyard Worlds: Planet 9 participant. We also discuss our measured approach to presenting the complementarity of machine learning and citizen science in volunteer-facing Cool Neighbors materials. Finally, we present a survey of advanced Backyard Worlds participants, which indicates that these citizen scientists are by and large not dissuaded from participating in Cool Neighbors because of its usage of artificial intelligence.

Keywords

• astronomy
• citizen science
• neural networks
• image analysis
• zooniverse
• brown dwarfs