Outputs (2)

Lessons learned from the two largest Galaxy morphological classification catalogues built by convolutional neural networks (2022)
Journal Article
Cheng, T.-Y., Domínguez Sánchez, H., Vega-Ferrero, J., Conselice, C., Siudek, M., Aragón-Salamanca, A., Bernardi, M., Cooke, R., Ferreira, L., Huertas-Company, M., Krywult, J., Palmese, A., Pieres, A., Plazas Malagón, A., Carnero Rosell, A., Gruen, D., Thomas, D., Bacon, D., Brooks, D., James, D., …Scarpine, V. (2023). Lessons learned from the two largest Galaxy morphological classification catalogues built by convolutional neural networks. Monthly Notices of the Royal Astronomical Society, 518(2), 2794-2809. https://doi.org/10.1093/mnras/stac3228

We compare the two largest galaxy morphology catalogues, which separate early- and late-type galaxies at intermediate redshift. The two catalogues were built by applying supervised deep learning (convolutional neural networks, CNNs) to the Dark Energ... Read More about Lessons learned from the two largest Galaxy morphological classification catalogues built by convolutional neural networks.

Harvesting the Lyα forest with convolutional neural networks (2022)
Journal Article
Cheng, T.-Y., Cooke, R. J., & Rudie, G. (2022). Harvesting the Lyα forest with convolutional neural networks. Monthly Notices of the Royal Astronomical Society, 517, 755-775. https://doi.org/10.1093/mnras/stac2631

We develop a machine learning based algorithm using a convolutional neural network (CNN) to identify low H I column density Lyα absorption systems (log NHI/cm−2 < 17) in the Lyα forest, and predict their physical properties, such as their H I column... Read More about Harvesting the Lyα forest with convolutional neural networks.