  Large and publicly available astronomical archives open up new possibilities
to search and study Solar System objects. However, advanced techniques are
required to deal with the large amounts of data. These unbiased surveys can be
used to constrain the size distribution of minor bodies, which represents a
piece of the puzzle for the formation models of the Solar System. We aim to
identify asteroids in archival images from the ESA Hubble Space Telescope (HST)
Science data archive using data mining. We developed a citizen science project
on the Zooniverse platform, Hubble Asteroid Hunter (www.asteroidhunter.org)
asking members of the public to identify asteroid trails in archival HST
images. We used the labels provided by the volunteers to train an automated
deep learning model built with Google Cloud AutoML Vision to explore the entire
HST archive to detect asteroids crossing the field-of-view. We report the
detection of 1701 new asteroid trails identified in archival HST data via our
citizen science project and the subsequent machine learning exploration of the
ESA HST science data archive. We detect asteroids to a magnitude of 24.5, which
are statistically fainter than the populations of asteroids identified from
ground-based surveys. The majority of asteroids are distributed near the
ecliptic plane, as expected, where we find an approximate density of 80
asteroids per square degree. We match 670 trails (39% of the trails found) with
454 known Solar System objects in the Minor Planet Center database, however, no
matches are found for 1031 (61%) trails. The unidentified asteroids are faint,
being on average 1.6 magnitudes fainter than the asteroids we succeeded to
identify. They probably correspond to previously unknown objects. This work
demonstrates that citizen science and machine learning are useful techniques
for the systematic search of SSOs in existing astronomy science archives.
