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All Outputs (7)

OzDES Reverberation Mapping Program: Stacking analysis with Hβ, Mg ii, and C iv (2024)
Journal Article
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

Reverberation mapping is the leading technique used to measure direct black hole masses outside of the local Universe. Additionally, reverberation measurements calibrate secondary mass-scaling relations used to estimate single-epoch virial black hole... Read More about OzDES Reverberation Mapping Program: Stacking analysis with Hβ, Mg ii, and C iv.

Dark Energy Survey Year 6 results: Intra-cluster light from redshift 0.2 to 0.5 (2024)
Journal Article
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

Using the full 6 years of imaging data from the Dark Energy Survey, we study the surface brightness profiles of galaxy cluster central galaxies and intra-cluster light. We apply a ‘stacking’ method to over 4000 galaxy clusters identified by the redMa... Read More about Dark Energy Survey Year 6 results: Intra-cluster light from redshift 0.2 to 0.5.

Lessons learned from the two largest Galaxy morphological classification catalogues built by convolutional neural networks (2022)
Journal Article
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

We compare the two largest galaxy morphology catalogues, which separate early- and late-type galaxies at intermediate redshift. The two catalogues were built by applying supervised deep learning (convolutional neural networks, CNNs) to the Dark Energ... Read More about Lessons learned from the two largest Galaxy morphological classification catalogues built by convolutional neural networks.

Harvesting the Lyα forest with convolutional neural networks (2022)
Journal Article
Cheng, T.-Y., Cooke, R. J., & Rudie, G. (2022). Harvesting the Lyα forest with convolutional neural networks. Monthly Notices of the Royal Astronomical Society, 517, 755-775. https://doi.org/10.1093/mnras/stac2631

We develop a machine learning based algorithm using a convolutional neural network (CNN) to identify low H I column density Lyα absorption systems (log NHI/cm−2 < 17) in the Lyα forest, and predict their physical properties, such as their H I column... Read More about Harvesting the Lyα forest with convolutional neural networks.

Galaxy Morphological Classification Catalogue of the Dark Energy Survey Year 3 data with Convolutional Neural Networks (2021)
Journal Article
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

We present in this paper one of the largest galaxy morphological classification catalogues to date, including over 20 million galaxies, using the Dark Energy Survey (DES) Year 3 data based on convolutional neural networks (CNNs). Monochromatic i-band... Read More about Galaxy Morphological Classification Catalogue of the Dark Energy Survey Year 3 data with Convolutional Neural Networks.

Beyond the hubble sequence – exploring galaxy morphology with unsupervised machine learning (2021)
Journal Article
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

We explore unsupervised machine learning for galaxy morphology analyses using a combination of feature extraction with a vector-quantized variational autoencoder (VQ-VAE) and hierarchical clustering (HC). We propose a new methodology that includes: (... Read More about Beyond the hubble sequence – exploring galaxy morphology with unsupervised machine learning.