Publications: AI and Machine Learning
2021
Z-Sequence: photometric redshift predictions for galaxy clusters with sequential random k-nearest neighbours, Chan et al. 2021, MNRAS, 503, 6078
2020
Galaxy morphological classification in deep-wide surveys via unsupervised machine learning, Martin et al. 2020, MNRAS, 491, 1408
Eigengalaxies: describing galaxy morphology using principal components in image space, Uzeirbegovic et al. 2020, arXiv:200406734
Galaxy Zoo: probabilistic morphology through Bayesian CNNs and active learning, Walmsley et al. 2020, MNRAS, 491. 1554
Morpheus: A Deep Learning Framework For Pixel-Level Analysis of Astronomical Image Data, Hausen et al. 2020, ApJS, 248, 20
Predicting galaxy spectra from images with hybrid convolutional neural networks, Wu & Peek 2020, Machine Learning and the Physical Sciences workshop at NeurIPS
2019
Optimising Automatic Morphological Classification of Galaxies with Machine Learning and Deep Learning using Dark Energy Survey Imaging, Cheng et al. 2019, arXiv:1908:03610
Deep-CEE I: fishing for galaxy clusters with deep neural nets, Chan & Stott 2019, MNRAS, 490, 5770
2018
The VIMOS Public Extragalactic Redshift Survey (VIPERS). The complexity of galaxy populations at 0.4 < z < 1.3 revealed with unsupervised machine-learning algorithms, Siudek et al. 2018, A&A, 617, 70