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

**Authors:** Emily O. Garvin, Sandor Kruk, Claude Cornen, Rachana Bhatawdekar, Raoul Cañameras, Bruno Merín  
**arXiv:** [2207.06997](https://arxiv.org/abs/2207.06997)  
**Date:** 2022-07-14  
**Summary page:** https://www.summarizepaper.com/en/paper/2207.06997v1/

## Key points

- The Hubble Space Telescope (HST) archives have high-resolution images that can be used to find strong gravitational lenses.
- Strong lenses can be found by coincidence in other HST observations, not just in targeted programs.
- Crowdsourcing on the Hubble Asteroid Hunter citizen science project was used to identify non-targeted strong gravitational lenses in almost two decades of images from the ESA it Hubble Space Telescope archive (eHST).
- After visually inspecting 2354 objects tagged by citizen scientists as strong lenses, the authors report the detection of 252 strong gravitational lens candidates that were not primary targets of the HST observations.
- Of these candidates, 198 are new and 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.
- Crowdsourcing is a robust method for performing visual detection of strong lenses and shows that volunteers can detect classical lenses as well as outliers such as exotic lens configurations after receiving proper training and knowledge transfer.
- Combining artificial intelligence methods and crowdsourcing for the detection of strong lenses has strong potential to produce reliable catalogs in shorter timescales and detect outliers such as exotic lenses.
- This study demonstrates the power of crowdsourcing in visually identifying strong lenses and benefits exploring large archival datasets spanning decades in time to expand research horizons further into future missions like Euclid or JWST.

## Summary

The Hubble Space Telescope (HST) archives provide a wealth of high-resolution images that can be mined for strong gravitational lenses. While many HST programs specifically target strong lenses, they can also be present by coincidence in other HST observations. In this study, the authors aimed 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. They 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. After visually inspecting 2354 objects tagged by citizen scientists as strong lenses to clean the sample and identify genuine lenses, the authors report the detection of 252 strong gravitational lens candidates that were not primary targets of the HST observations. Of these candidates, 198 are new and 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 study demonstrates that crowdsourcing is a robust method for performing visual detection of strong lenses and shows that volunteers can detect classical lenses as well as outliers such as exotic lens configurations after receiving proper training and knowledge transfer. Although inspecting large field-of-view images might lead to a decrease in completeness compared to postage stamps where pre-selection of targets is often necessary, this approach has the advantage of considerably speeding up the detection process. The authors note that while using color images for inspection might have led to a larger number of lenses being identified, multiple bands were only available for a fraction of the Hubble images in the archives. Nevertheless, volunteers demonstrated their ability to identify strong lenses in grayscale images which is an important lesson for Euclid where only a single optical band is available. This study also highlights 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. The authors suggest that combining artificial intelligence methods and crowdsourcing for the detection of strong lenses has strong potential to produce reliable catalogs in shorter timescales and detect outliers such as exotic lenses. Overall, this study demonstrates the power of crowdsourcing in visually identifying strong lenses and benefits exploring large archival datasets spanning decades in time to expand research horizons further into future missions like Euclid or JWST . The sample of 252 strong lenses with high-resolution HST imaging is ideal for follow-up with spectroscopy, lens modeling and scientific analyses making it useful benchmark for future lens searches with high resolution imaging such as those with Euclid , JWST or Roman .