Bottrell, Connor

Deep learning predictions of galaxy merger stage and the importance of observational realism

Bottrell, C., Hani, M. H., Teimoorinia, H., Ellison, S. L., Moreno, J., Torrey, P., Hayward, C. C., Thorp, M., Simard, L., & Hernquist, L. (2019) · Monthly Notices of the Royal Astronomical Society

Galaxies: Formation, Evolution, PhenomenaAdvanced Neural Network Applications

Cited by 113

Convolutional neural network identification of galaxy post-mergers in UNIONS using IllustrisTNG

Bickley, R. W., Bottrell, C., Hani, M. H., Ellison, S. L., Teimoorinia, H., Yi, K. M., Wilkinson, S., Gwyn, S., & Hudson, M. J. (2021) · Monthly Notices of the Royal Astronomical Society

Galaxies: Formation, Evolution, PhenomenaAdvanced Neural Network Applications

Cited by 84

A deep learning approach to test the small-scale galaxy morphology and its relationship with star formation activity in hydrodynamical simulations

Zanisi, L., Huertas-Company, M., Lanusse, F., Bottrell, C., Pillepich, A., Nelson, D., Rodriguez-Gomez, V., Shankar, F., Hernquist, L., Dekel, A., et al. (2021) · Monthly Notices of the Royal Astronomical Society

Galaxies: Formation, Evolution, PhenomenaAstrophysics and Star Formation Studies

Cited by 65

Identification of tidal features in deep optical galaxy images with convolutional neural networks

Sánchez, H. D., Martin, G., Damjanov, I., Buitrago, F., Huertas-Company, M., Bottrell, C., Bernardi, M., Knapen, J. H., Vega-Ferrero, J., Hausen, R., et al. (2023) · Monthly Notices of the Royal Astronomical Society

Galaxies: Formation, Evolution, PhenomenaAdvanced Neural Network Applications

Cited by 28