Machine Learning methods in Astronomy
(2024)
Journal Article
Lieu, M., & Cheng, T.-Y. (2024). Machine Learning methods in Astronomy. Astronomy and computing, 47, Article 100830. https://doi.org/10.1016/j.ascom.2024.100830
All Outputs (5)
Lessons learned from the two largest Galaxy morphological classification catalogues built by convolutional neural networks (2022)
Journal Article
Cheng, T., Domínguez Sánchez, H., Vega-Ferrero, J., Conselice, C., Siudek, M., Aragón-Salamanca, A., …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/stac3228We 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., 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/stac2631We 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.
Galaxy Morphological Classification Catalogue of the Dark Energy Survey Year 3 data with Convolutional Neural Networks (2021)
Journal Article
Cheng, T., Conselice, C. J., Aragón-Salamanca, A., Aguena, M., Allam, S., Andrade-Oliveira, F., …To, C. (2021). Galaxy Morphological Classification Catalogue of the Dark Energy Survey Year 3 data with Convolutional Neural Networks. Monthly Notices of the Royal Astronomical Society, 507(3), 4425-4444. https://doi.org/10.1093/mnras/stab2142We present in this paper one of the largest galaxy morphological classification catalogues to date, including over 20 million galaxies, using the Dark Energy Survey (DES) Year 3 data based on convolutional neural networks (CNNs). Monochromatic i-band... Read More about Galaxy Morphological Classification Catalogue of the Dark Energy Survey Year 3 data with Convolutional Neural Networks.
Beyond the hubble sequence – exploring galaxy morphology with unsupervised machine learning (2021)
Journal Article
Cheng, T., Huertas-Company, M., Conselice, C. J., Aragón-Salamanca, A., Robertson, B. E., & Ramachandra, N. (2021). Beyond the hubble sequence – exploring galaxy morphology with unsupervised machine learning. Monthly Notices of the Royal Astronomical Society, 503(3), 4446-4465. https://doi.org/10.1093/mnras/stab734We explore unsupervised machine learning for galaxy morphology analyses using a combination of feature extraction with a vector-quantized variational autoencoder (VQ-VAE) and hierarchical clustering (HC). We propose a new methodology that includes: (... Read More about Beyond the hubble sequence – exploring galaxy morphology with unsupervised machine learning.