statmorph-lsst: Quantifying and correcting morphological biases in galaxy surveys, Sazonova et al., 2026, submitted to OJAp, arXiv:2511.09644
Galaxy image simplification using Generative AI, Erukude et al. 2025, A&C, 53. 100900
Morphological classification of galaxies through structural and star formation parameters using machine learning, Aguilar-Argüello et al., 2025, MNRAS, 537, 876
RMS asymmetry: a robust metric of galaxy shapes in images with varied depth and resolution, Sazonova et al. 2024, OJAp, 7, 77
Galaxy morphological classification in deep-wide surveys via unsupervised machine learning, Martin et al. 2020, MNRAS, 491, 1408
Galaxy Zoo: probabilistic morphology through Bayesian CNNs and active learning, Walmsley et al. 2020, MNRAS, 491. 1554
Non-parametric morphologies of galaxies in the EAGLE simulation, Bignone et al. 2020, MNRAS, 491, 3624
Optimising Automatic Morphological Classification of Galaxies with Machine Learning and Deep Learning using Dark Energy Survey Imaging, Cheng et al. 2020, MNRAS, 493, 4209
Morpheus: A Deep Learning Framework For Pixel-Level Analysis of Astronomical Image Data, Hausen et al. 2020, ApJS, 248, 20
A galaxy classification grid that better recognizes early-type galaxy morphology, Graham 2019, MNRAS, 487, 4995
R_e. I. Understanding galaxy sizes, associated luminosity densities, and the artificial division of the early-type galaxy population, Graham 2019, PASA, 36, 35