Walmsley, Mike

Galaxy Zoo: probabilistic morphology through Bayesian CNNs and active learning

Walmsley, M., Smith, L., Lintott, C., Gal, Y., Bamford, S., Dickinson, H., Fortson, L., Kruk, S., Masters, K., Scarlata, C., et al. (2020) · Monthly Notices of the Royal Astronomical Society

Galaxies: Formation, Evolution, PhenomenaAdvanced Neural Network Applications

Cited by 160

Identification of low surface brightness tidal features in galaxies using convolutional neural networks

Walmsley, M., Ferguson, A. M. N., Mann, R. G., & Lintott, C. J. (2019) · Monthly Notices of the Royal Astronomical Society

Galaxies: Formation, Evolution, PhenomenaAdvanced Neural Network Applications

Cited by 66

Practical Galaxy Morphology Tools from Deep Supervised Representation Learning

Walmsley, M., Scaife, A. M. M., Lintott, C., Lochner, M., Etsebeth, V., Géron, T., Dickinson, H., Fortson, L., Kruk, S., Masters, K. L., et al. (2021) · arXiv

Galaxies: Formation, Evolution, PhenomenaAdvanced Neural Network Applications

Cited by 64

Zoobot: Adaptable Deep Learning Models for Galaxy Morphology

Walmsley, M., Allen, C., Aussel, B., Bowles, M., Gregorowicz, K., Slijepcevic, I. V., Lintott, C. J., Scaife, A. M. M., Jabłońska, M., Karchev, K., et al. (2023) · Journal of Open Source Software

Galaxies: Formation, Evolution, PhenomenaAdvanced Neural Network Applications

Cited by 45

Euclid : The Early Release Observations Lens Search Experiment

Acevedo Barroso, J. A., O’Riordan, C. M., Clément, B., Tortora, C., Collett, T. E., Courbin, F., Gavazzi, R., Metcalf, R. B., Busillo, V., Andika, I. T., et al. (2025) · Astronomy & Astrophysics

Galaxies: Formation, Evolution, PhenomenaAstrophysical Phenomena and Observations

Cited by 21

Euclid : Searches for strong gravitational lenses using convolutional neural nets in Early Release Observations of the Perseus field

Pearce-Casey, R., Nagam, B. C., Wilde, J., Busillo, V., Ulivi, L., Andika, I. T., Manjón-García, A., Leuzzi, L., Matavulj, P., Serjeant, S., et al. (2025) · Astronomy & Astrophysics

Galaxies: Formation, Evolution, PhenomenaAstrophysical Phenomena and Observations

Cited by 18

Euclid : A complete Einstein ring in NGC 6505

O’Riordan, C. M., Oldham, L. J., Nersesian, A., Li, T., Collett, T. E., Sluse, D., Altieri, B., Clément, B., Vasan, K. G. C., Rhoades, S., et al. (2025) · Astronomy & Astrophysics

Galaxies: Formation, Evolution, PhenomenaAstrophysical Phenomena and Observations

Cited by 17

Quantifying uncertainty in deep learning approaches to radio galaxy classification

Mohan, D., Scaife, A. M. M., Porter, F., Walmsley, M., & Bowles, M. (2022) · Monthly Notices of the Royal Astronomical Society

Galaxies: Formation, Evolution, PhenomenaRadio Astronomy Observations and Technology

Cited by 16

Euclid Quick Data Release (Q1): XXVIII. The Strong Lensing Discovery Engine C – Finding lenses with machine learning

Euclid Collaboration, Lines, N. E. P., Collett, T. E., Walmsley, M., Rojas, K., Li, T., Leuzzi, L., Manjón-García, A., Vincken, S. H., Wilde, J., et al. (2026) · Astronomy & Astrophysics

Galaxies: Formation, Evolution, PhenomenaAstrophysical Phenomena and Observations

Cited by 14

Euclid : Finding strong gravitational lenses in the early release observations using convolutional neural networks

Nagam, B. C., Acevedo Barroso, J. A., Wilde, J., Andika, I. T., Manjón-García, A., Pearce-Casey, R., Stern, D., Nightingale, J. W., Moustakas, L. A., McCarthy, K., et al. (2025) · Astronomy & Astrophysics

Galaxies: Formation, Evolution, PhenomenaAstrophysical Phenomena and Observations

Cited by 8

Scaling Laws for Galaxy Images

Walmsley, M., Bowles, M., Scaife, A. M. M., Makechemu, J. S., Gordon, A. J., Ferguson, A. M. N., Mann, R. G., Pearson, J., Popp, J. J., Bovy, J., et al. (2024) · arXiv

Domain Adaptation and Few-Shot LearningAdvanced Neural Network Applications

Cited by 2