Full Title: Hubble Asteroid Hunter: Finding Asteroids in HST Archival Images and Analysing Their Properties Using Parallax Abstract: The Hubble Space Telescope (HST) archive hides many unexpected treasures, such as asteroid trails serendipitously imaged by HST during its tireless and prolific scientific career. We have combined the power of a citizen science project with a machine learning algorithm to identify 1,701 asteroid trails from the whole HST archive (almost 20 years for the two considered instruments). 1,031 of them do not match any entries in the MPC database, being potential unknown asteroids. As a byproduct, we also unveiled 252 strong gravitational lens candidates and an insightful statistical analysis about the increasing artificial satellite trails photobombing HST images. Asteroids appear as curved trails in HST images due to parallax induced by the fast orbital motion of the spacecraft. Taking into account its trajectory, this effect can be computed to obtain the distance to the asteroids. Using distance, we can obtain the object’s absolute magnitude and size estimation assuming an albedo value, along with some boundaries for its orbital parameters. After an accuracy assessment and filtering process, we selected 632 asteroids as meaningful results from our parallax analysis. We obtain a sample dominated by potential Main Belt objects featuring absolute magnitudes (H) mostly between 15 and 22 mag. The absolute magnitude cumulative distribution [logN(H > H0) ∝ α·log(H0) ] confirms the previously reported slope change for 15 < H < 18, from α ≈ 0.56 to α ≈ 0.26, maintained in our case down to absolute magnitudes around H ≈ 20, hence expanding previous results by approximately two magnitudes. HST archival observations, taken over a long time span, can be effectively used as a sample of small bodies in the Main Asteroid Belt since the telescope’s pointings are essentially random in the sky. They allow us to expand the current best samples of astronomical objects at no extra cost regarding telescope time.