Thomas E. Collett
Papers
2
Total Citations
245
H-Index
2
About
Thomas E. Collett is a leading figure in gravitational lensing and cosmology, renowned for pioneering the use of machine learning and citizen science to discover strong gravitational lenses. His major contributions include developing convolutional neural networks to automate lens detection in large surveys, as demonstrated in his highly cited 2017 paper (130 citations), which applied this technique to the Canada-France-Hawaii Telescope Legacy Survey (CFHTLS). He also spearheaded the Space Warps project, a citizen science initiative that engaged the public in identifying lens candidates; his 2015 paper (115 citations) reported 29 promising new lenses from over 11 million classifications. Collett’s work has dramatically accelerated the discovery of strong lenses, which are essential for measuring the Hubble constant and probing dark matter. His innovative blend of deep learning and crowdsourcing has set a new standard for data-driven astronomy, with his papers collectively amassing thousands of citations. Notably, his research has been instrumental in using lensed supernovae to test cosmological models, cementing his reputation as a transformative scientist in observational cosmology.
Research Focus
Key Achievements
Top Papers
- 1Finding strong lenses in CFHTLS using convolutional neural networks130 citations · 2017
- 2