Staff profile

Conference Paper

• Contracting to a longest path in H-free graphs
  
  Kern, W., & Paulusma, D. (2020). Contracting to a longest path in H-free graphs. In Y. Cao, S. Cheng, & M. Li (Eds.), 31st International Symposium on Algorithms and Computation (ISAAC 2020) (pp. 22:1-22:18). Schloss Dagstuhl. https://doi.org/10.4230/lipics.isaac.2020.22

Journal Article

• 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., … 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., … 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., … 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., … To, C. (2021). Galaxy Morphological Classification Catalogue of the Dark Energy Survey Year 3 data with Convolutional Neural Networks. Monthly Notices of 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