In thirty-one years of observations the Hubble Space Telescope (HST) has produced a vast archive of thousands of targeted observations. Occasionally, closer objects such as Solar System bodies or artificial satellites cross the telescope’s field of view during the observations leaving trails in the images. This is a great opportunity to study the science of small bodies in the Solar System, considering the already existing images from the huge HST Archive, containing more than 100 Tb of data and spanning three decades.
Our project is focused on studying serendipitous observed trails appearing in archival HST images. We used images from two instruments, namely the Advanced Camera for Surveys and Wide Field Camera 3, the ultraviolet and visible channels. These images were acquired between 2002 and 2021. We built an online citizen science project on the Zooniverse platform, Hubble Asteroid Hunter (www.asteroidhunter.org). It launched on International Asteroid Day 2019, with the aim to identify asteroid trails in the HST images (Kruk et al., in prep.). This project involved more than 11,000 volunteers in the search for asteroids, providing 2 million classifications of 150,000 images, conducted over the period of one year. The labels provided by the volunteers were used to train an automated classifier based on a deep learning algorithm, Google Cloud AutoML Vision. In total, we recovered 2,400 trails in the HST images.
We further analysed the asteroid trails in order to obtain astrometry and photometry with a customised algorithm. We validated the trails visually, finding 1,700 trails presumably of Solar System objects.
We only matched 300 trails with already known asteroids, taking into account the orbital uncertainties and their apparent motion. Most of the new objects will correspond to main belt objects with diameters <1 km, thus will help us characterise the distribution of small size asteroids in the main belt, a population poorly explored by current studies.
This project demonstrates the power of combining novel tools such as citizen science and artificial intelligence to efficiently explore archival images and obtain important results, with the invaluable help of Zooniverse volunteers, beyond the original scope of the Hubble observations.