1. Beyond the Hubble Sequence -- Exploring Galaxy Morphology with Unsupervised Machine Learning. Ting-Yun Cheng, Marc Huertas-Company, Christopher J. Conselice, Alfonso Aragón-Salamanca, Brant E. Robertson, Nesar Ramachandra. Submitted.

  2. Identifying Strong Lenses with Unsupervised Machine Learning using Convolutional Autoencoder. Ting-Yun Cheng, Nan Li, Christopher J. Conselice, Alfonso Aragón-Salamanca, Simon Dye, Robert B. Metcalf. Published on MNRAS, Volume 494, Issue 3, May 2020, Pages 3750–3765.

  3. Optimising Automatic Morphological Classification of Galaxies with Machine Learning and Deep Learning using Dark Energy Survey Imaging. Ting-Yun Cheng, Christopher J. Conselice, Alfonso Aragón-Salamanca, Nan Li, Asa F. L. Bluck, Will G. Hartley, James Annis, David Brooks, Peter Doel, Juan García-Bellido, David J. James, Kyler Kuehn, Nikolay Kuropatkin, Mathew Smith, Flavia Sobreira, Gregory Tarle. Published on MNRAS, Volume 493, Issue 3, April 2020, Pages 4209–4228.


  1. Galaxy Merger Rates up to z ∼ 3 Using a Bayesian Deep Learning Model: A Major-merger Classifier Using IllustrisTNG Simulation Data. Leonardo Ferreira, Christopher J. Conselice, Kenneth Duncan, Ting-Yun Cheng, Alex Griffiths, Amy Whitney. Published on ApJ, Volume 895, Number 2, June 2020, Pages 115.

  • PhD Thesis 

The Narrative of Galaxy Morphological Classification Told Through Machine Learning. Ting-Yun Cheng, School of Physics and Astronomy, University of Nottingham (2020). Supervisors: Christopher J. Conselice, Alfonso Aragón-Salamanca.

​鄭婷筠Ting-Yun CHENG

Astrophysicist & Adventurer

Lulin Observatory @ Taiwan

© 2019 by T.-Y. Cheng. Proudly created with