William Pearson

Research focus: 

galaxy formation, galaxy evolution, galaxy mergers

ML expertise: 

Artificial Neural Networks, Convolutional Neural Networks, Auto-Encoders, Generative Adversarial Networks

Relevant publications: 
  1. Towards a consistent framework of comparing galaxy mergers in observations and simulations”, Astronomy & Astrophysics, Volume 644, id.A87, 12 pp. 202 https://ui.adsabs.harvard.edu/abs/2020A%26A...644A..87W/abstract 
  2. Pearson, W. J.; Wang, L.; Alpaslan, M.; Baldry, I.; Bilicki, M.; Brown, M. J. I.; Grootes, M. W.; Holwerda, B. W.; Kitching, T. D.; Kruk, S.; van der Tak, F. F. S., “Effect of galaxy mergers on star-formation rates”, Astronomy & Astrophysics, Volume 631, id.A51, 19 pp. 2019 https://ui.adsabs.harvard.edu/abs/2019A%26A...631A..51P/abstrac

  3. Pearson, W. J.; Wang, L.; Trayford, J. W.; Petrillo, C. E.; van der Tak, F. F. S., “Identifying galaxy mergers in observations and simulations with deep learning”, Astronomy & Astrophysics, Volume 626, id.A49, 18 pp. 2019 https://ui.adsabs.harvard.edu/abs/2019A%26A...626A..49P/abstract

  4. Pearson, William J.; Wang, Lingyu; Trayford, James; Petrillo, Carlo E.; van der Tak, Floris F. S., “Deep learning for galaxy mergers in the galaxy main sequence”, Challenges in Panchromatic Modelling with Next Generation Facilities. Proceedings of the International Astronomical Union, Volume 341, pp. 104-108, 2020 https://ui.adsabs.harvard.edu/abs/2020IAUS..341..104P/abstract