The Active Asteroids Citizen Science Project

Northern Arizona University, Flagstaff AZ

Investigators

Abstract

Active asteroids are crucial for understanding the distribution of volatiles, such as water, in the solar system. The population of active objects on the boundary between asteroids and comets (between the asteroid belt and Jupiter) is not well understood. The Citizen Science Active Asteroids project currently has over 8,500 volunteers who have classified nearly 500,000 archival images of asteroids and other small solar system bodies. Classifications made through the Citizen Science program are the primary method of new asteroid activity discovery in this proposed work. The project will examine the most promising candidate active asteroids with in-depth archival searches and follow-up telescope observations. This project will observe dozens of targets each month across several nights and at more than one telescope facility. The team will characterize the newly-discovered activity on these objects. Citizen Science is one of the most interactive forms of outreach and through the Zooniverse platform, the team share the exciting motivation behind the project, the mystery to be solved, and train the volunteers to classify tens of thousands of images each month. Discovery of activity on asteroids and other objects, such as comets and Centaurs, through the Active Asteroids project involves (1) retrieving and processing publicly available image data of minor planets, (2) preparing and uploading image cutouts to the Zooniverse Citizen Science platform, (3) determining candidate active objects using statistical metrics on classification data, and (4) performing archival searches for activity in images of candidate active objects. The project predicts that volunteers will classify more than 1.5 million additional images and should discover on the order of 30 new active asteroids. The classification data and discoveries from the Active Asteroids project will provide an ideal AI training set for the development of optimized detection algorithms in preparation for Vera C. Rubin Observatory Legacy Survey of Space and Time dataset. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.