| Optimizing automatic morphological classification of galaxies with machine learning and deep learning using Dark Energy Survey imaging TY Cheng, CJ Conselice, A Aragón-Salamanca, N Li, AFL Bluck, ... Monthly Notices of the Royal Astronomical Society 493 (3), 4209-4228, 2020 | 102 | 2020 |
| Galaxy Merger Rates up to z∼ 3 Using a Bayesian Deep Learning Model: A Major-merger Classifier Using IllustrisTNG Simulation Data L Ferreira, CJ Conselice, K Duncan, TY Cheng, A Griffiths, A Whitney The Astrophysical Journal 895 (2), 115, 2020 | 81 | 2020 |
| Identifying strong lenses with unsupervised machine learning using convolutional autoencoder TY Cheng, N Li, CJ Conselice, A Aragón-Salamanca, S Dye, RB Metcalf Monthly Notices of the Royal Astronomical Society 494 (3), 3750-3765, 2020 | 65 | 2020 |
| Galaxy morphological classification catalogue of the Dark Energy Survey Year 3 data with convolutional neural networks TY Cheng, CJ Conselice, A Aragón-Salamanca, M Aguena, S Allam, ... Monthly Notices of the Royal Astronomical Society 507 (3), 4425-4444, 2021 | 50 | 2021 |
| Beyond the hubble sequence–exploring galaxy morphology with unsupervised machine learning TY Cheng, M Huertas-Company, CJ Conselice, A Aragon-Salamanca, ... Monthly Notices of the Royal Astronomical Society 503 (3), 4446-4465, 2021 | 42 | 2021 |
| Lessons learned from the two largest Galaxy morphological classification catalogues built by convolutional neural networks TY Cheng, H Domínguez Sánchez, J Vega-Ferrero, CJ Conselice, ... Monthly Notices of the Royal Astronomical Society 518 (2), 2794-2809, 2023 | 6 | 2023 |
| Marc Huertas-Company, Christopher J Conselice, Alfonso Aragon-Salamanca, Brant E Robertson, and Nesar Ramachandra. Beyond the hubble sequence–exploring galaxy morphology with … TY Cheng Monthly Notices of the Royal Astronomical Society 503 (3), 4446-4465, 2021 | 6 | 2021 |
| Harvesting the Ly α forest with convolutional neural networks TY Cheng, RJ Cooke, G Rudie Monthly Notices of the Royal Astronomical Society 517 (1), 755-775, 2022 | 4 | 2022 |
| Harvesting spectroscopic and time series data with machine learning and AI TY Cheng, R Cooke, A Puglisi Astronomy & Geophysics 65 (2), 2.35-2.41, 2024 | | 2024 |
| Exploring the Dark Side: Uncovering Low-Surface Brightness Galaxies in the Dark Energy Survey K Herron, A Drlica-Wagner, B Mutlu Pakdil, A Peter, A Pace, P Ferguson, ... American Astronomical Society Meeting Abstracts 56 (2), 140.01, 2024 | | 2024 |
| Environmental Quenching of Low Surface Brightness Galaxies near Milky Way mass Hosts J Bhattacharyya, AHG Peter, P Martini, B Mutlu-Pakdil, A Drlica-Wagner, ... TBD, 2023 | | 2023 |
| Dark Energy Survey Year 6 Results: Intra-Cluster Light from Redshift 0.2 to 0.5 Y Zhang, JB Golden-Marx, RLC Ogando, B Yanny, ES Rykoff, S Allam, ... arXiv preprint arXiv:2309.00671, 2023 | | 2023 |
| CNN Lesson Learned from Two Largest Galaxy Morphological Classification Catalogues TY Cheng, HD Sánchez, J Vega-Ferrero, CJ Conselice, M Siudek Memorie della Società Astronomica Italiana Journal of the Italian …, 2023 | | 2023 |
| Classifying Major Mergers in the CANDELS fields using a Deep Learning model trained with IllustrisTNG data L Ferreira, C Conselice, K Duncan, T Cheng, A Griffiths, A Whitney American Astronomical Society Meeting Abstracts# 236 236, 239.04, 2020 | | 2020 |
| A Blind Test of Supervised Machine Learning for Galaxy Classification A Aragon-Salamanca, TY Cheng, C Conselice American Astronomical Society Meeting Abstracts# 233 233, 2019 | | 2019 |
| A Blind Test of Supervised Machine Learning for Galaxy Classification C Conselice, TY Cheng, A Aragon-Salamanca American Astronomical Society Meeting Abstracts# 233 233, 230.08, 2019 | | 2019 |
