Hubble Asteroid Hunter: II. Identifying strong gravitational lenses in HST images with crowdsourcing

TL;DR

This study leverages crowdsourcing on the Hubble Asteroid Hunter project to identify 252 strong gravitational lens candidates in HST archival images, including 198 new detections, demonstrating the effectiveness of citizen science in astrophysical discovery.

Contribution

It introduces a novel crowdsourcing approach to find strong gravitational lenses in HST data without prior selection, revealing a diverse set of lenses, including many fainter and previously unreported ones.

Findings

Detected 252 strong lens candidates, 198 of which are new.

Identified a high variety of lens configurations, including exotic types.

Demonstrated crowdsourcing's effectiveness in astrophysical image analysis.

Abstract

The Hubble Space Telescope (HST) archives constitute a rich dataset of high resolution images to mine for strong gravitational lenses. While many HST programs specifically target strong lenses, they can also be present by coincidence in other HST observations. We aim to identify non-targeted strong gravitational lenses in almost two decades of images from the ESA it Hubble Space Telescope archive (eHST), without any prior selection on the lens properties. We used crowdsourcing on the Hubble Asteroid Hunter (HAH) citizen science project to identify strong lenses, alongside asteroid trails, in publicly available large field-of-view HST images. We visually inspected 2354 objects tagged by citizen scientists as strong lenses to clean the sample and identify the genuine lenses. We report the detection of 252 strong gravitational lens candidates, which were not the primary targets of the HST observations. 198 of them are new, not previously reported by other studies, consisting of 45 A grades, 74 B grades and 79 C grades. The majority are galaxy-galaxy configurations. The newly detected lenses are, on average, 1.3 magnitudes fainter than previous HST searches. This sample of strong lenses with high resolution HST imaging is ideal to follow-up with spectroscopy, for lens modelling and scientific analyses. This paper presents an unbiased search of lenses, which enabled us to find a high variety of lens configurations, including exotic lenses. We demonstrate the power of crowdsourcing in visually identifying strong lenses and the benefits of exploring large archival datasets. This study shows the potential of using crowdsourcing in combination with artificial intelligence for the detection and validation of strong lenses in future large-scale surveys such as ESA's future mission Euclid or in JWST archival images.