Staff profile

Conference Paper

• Contracting to a longest path in H-free graphs
  Kern, W., & Paulusma, D. (2020, December). Contracting to a longest path in H-free graphs. Presented at ISAAC 2020, Hong Kong, China

Journal Article

• Efficient search for extremely metal-poor galaxies in the local universe using convolutional neural networks
  Cheng, T.-Y., & Cooke, R. J. (2025). Efficient search for extremely metal-poor galaxies in the local universe using convolutional neural networks. Monthly Notices of the Royal Astronomical Society, 540(1), 128-142. https://doi.org/10.1093/mnras/staf690
• OzDES Reverberation Mapping Program: Stacking analysis with Hβ, Mg ii, and C iv
  Malik, U., Sharp, R., Penton, A., Yu, Z., Martini, P., Tucker, B. E., Davis, T. M., Lewis, G. F., Lidman, C., Aguena, M., Alves, O., Annis, J., Asorey, J., Bacon, D., Brooks, D., Carnero Rosell, A., Carretero, J., Cheng, T. .-Y., da Costa, L. N., Pereira, M. E. S., …Wiseman, P. (2024). OzDES Reverberation Mapping Program: Stacking analysis with Hβ, Mg ii, and C iv. Monthly Notices of the Royal Astronomical Society, 531(1), 163-182. https://doi.org/10.1093/mnras/stae1154
• Dark Energy Survey Year 6 results: Intra-cluster light from redshift 0.2 to 0.5
  Zhang, Y., Golden-Marx, J. B., Ogando, R. L. C., Yanny, B., Rykoff, E. S., Allam, S., Aguena, M., Bacon, D., Bocquet, S., Brooks, D., Carnero Rosell, A., Carretero, J., Cheng, T. .-Y., Conselice, C., Costanzi, M., da Costa, L. N., Pereira, M. E. S., Davis, T. M., Desai, S., Diehl, H. T., …DES Collaboration. (2024). Dark Energy Survey Year 6 results: Intra-cluster light from redshift 0.2 to 0.5. Monthly Notices of the Royal Astronomical Society, 531(1), 510-529. https://doi.org/10.1093/mnras/stae1165
• Machine Learning methods in Astronomy
  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
• Lessons learned from the two largest Galaxy morphological classification catalogues built by convolutional neural networks
  Cheng, T.-Y., Domínguez Sánchez, H., Vega-Ferrero, J., Conselice, C., Siudek, M., Aragón-Salamanca, A., Bernardi, M., Cooke, R., Ferreira, L., Huertas-Company, M., Krywult, J., Palmese, A., Pieres, A., Plazas Malagón, A., Carnero Rosell, A., Gruen, D., Thomas, D., Bacon, D., Brooks, D., James, D., …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/stac3228
• Galaxy Morphological Classification Catalogue of the Dark Energy Survey Year 3 data with Convolutional Neural Networks
  Cheng, T.-Y., Conselice, C. J., Aragón-Salamanca, A., Aguena, M., Allam, S., Andrade-Oliveira, F., Annis, J., Bluck, A., Brooks, D., Burke, D., Carrasco Kind, M., Carretero, J., Choi, A., Costanzi, M., da Costa, L., Pereira, M., De Vicente, J., Diehl, H., Drlica-Wagner, A., Eckert, K., …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/stab2142
• Beyond the hubble sequence – exploring galaxy morphology with unsupervised machine learning
  Cheng, T.-Y., 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/stab734