Outputs (40)

A Robust Study of High-redshift Galaxies: Unsupervised Machine Learning for Characterizing Morphology with JWST up to z ∼ 8 (2024)
Journal Article
Tohill, C., Bamford, S. P., Conselice, C. J., Ferreira, L., Harvey, T., Adams, N., & Austin, D. (2024). A Robust Study of High-redshift Galaxies: Unsupervised Machine Learning for Characterizing Morphology with JWST up to z ∼ 8. Astrophysical Journal, 962(2), Article 164. https://doi.org/10.3847/1538-4357/ad17b8

Galaxy morphologies provide valuable insights into their formation processes, tracing the spatial distribution of ongoing star formation and encoding signatures of dynamical interactions. While such information has been extensively investigated at lo... Read More about A Robust Study of High-redshift Galaxies: Unsupervised Machine Learning for Characterizing Morphology with JWST up to z ∼ 8.

Galapagos-2/Galfitm/Gama – Multi-wavelength measurement of galaxy structure: Separating the properties of spheroid and disk components in modern surveys (2022)
Journal Article
Häußler, B., Vika, M., Bamford, S. P., Johnston, E. J., Brough, S., Casura, S., …Popescu, C. (2022). Galapagos-2/Galfitm/Gama – Multi-wavelength measurement of galaxy structure: Separating the properties of spheroid and disk components in modern surveys. Astronomy and Astrophysics, 664, Article A92. https://doi.org/10.1051/0004-6361/202142935

Aims. We present the capabilities of Galapagos-2 and Galfitm in the context of fitting two-component profiles - bulge- disk decompositions - to galaxies, with the ultimate goal of providing complete multi-band, multi-component fitting of large sample... Read More about Galapagos-2/Galfitm/Gama – Multi-wavelength measurement of galaxy structure: Separating the properties of spheroid and disk components in modern surveys.

Galaxy And Mass Assembly (GAMA): Data Release 4 and the z < 0.1 total and z < 0.08 morphological galaxy stellar mass functions (2022)
Journal Article
Driver, S. P., Bellstedt, S., Robotham, A. S., Baldry, I. K., Davies, L. J., Liske, J., …Wilkins, S. M. (2022). Galaxy And Mass Assembly (GAMA): Data Release 4 and the z < 0.1 total and z < 0.08 morphological galaxy stellar mass functions. Monthly Notices of the Royal Astronomical Society, 513(1), 439-467. https://doi.org/10.1093/mnras/stac472

In Galaxy And Mass Assembly Data Release 4 (GAMA DR4), we make available our full spectroscopic redshift sample. This includes 248 682 galaxy spectra, and, in combination with earlier surveys, results in 330 542 redshifts across five sky regions cove... Read More about Galaxy And Mass Assembly (GAMA): Data Release 4 and the z < 0.1 total and z < 0.08 morphological galaxy stellar mass functions.

Galaxy Zoo DECaLS: Detailed visual morphology measurements from volunteers and deep learning for 314 000 galaxies (2021)
Journal Article
Walmsley, M., Lintott, C., Géron, T., Kruk, S., Krawczyk, C., Willett, K. W., …MacMillan, C. (2022). Galaxy Zoo DECaLS: Detailed visual morphology measurements from volunteers and deep learning for 314 000 galaxies. Monthly Notices of the Royal Astronomical Society, 509(3), 3966-3988. https://doi.org/10.1093/mnras/stab2093

We present Galaxy Zoo DECaLS: detailed visual morphological classifications for Dark Energy Camera Legacy Survey images of galaxies within the SDSS DR8 footprint. Deeper DECaLS images (r = 23.6 versus r = 22.2 from SDSS) reveal spiral arms, weak bars... Read More about Galaxy Zoo DECaLS: Detailed visual morphology measurements from volunteers and deep learning for 314 000 galaxies.

Quantifying Non-parametric Structure of High-redshift Galaxies with Deep Learning (2021)
Journal Article
Tohill, C., Ferreira, L., Conselice, C. J., Bamford, S. P., & Ferrari, F. (2021). Quantifying Non-parametric Structure of High-redshift Galaxies with Deep Learning. Astrophysical Journal, 916(1), Article 4. https://doi.org/10.3847/1538-4357/ac033c

At high redshift, due to both observational limitations and the variety of galaxy morphologies in the early universe, measuring galaxy structure can be challenging. Non-parametric measurements such as the CAS system have thus become an important tool... Read More about Quantifying Non-parametric Structure of High-redshift Galaxies with Deep Learning.

Recovering the origins of the lenticular galaxy NGC 3115 using multiband imaging (2021)
Journal Article
Buzzo, M. L., Cortesi, A., Hernandez-Jimenez, J. A., Coccato, L., Werle, A., Beraldo e Silva, L., …Mendes de Oliveira, C. (2021). Recovering the origins of the lenticular galaxy NGC 3115 using multiband imaging. Monthly Notices of the Royal Astronomical Society, 504(2), 2146-2167. https://doi.org/10.1093/mnras/stab941

A detailed study of the morphology of lenticular galaxies is an important way to understand how this type of galaxy is formed and evolves over time. Decomposing a galaxy into its components (disc, bulge, bar, ...) allows recovering the colour gradien... Read More about Recovering the origins of the lenticular galaxy NGC 3115 using multiband imaging.

Galaxy and Mass Assembly: A Comparison between Galaxy–Galaxy Lens Searches in KiDS/GAMA (2020)
Journal Article
Knabel, S., Steele, R. L., Holwerda, B. W., Bridge, J. S., Jacques, A., Hopkins, A. M., …Kielkopf, J. (2020). Galaxy and Mass Assembly: A Comparison between Galaxy–Galaxy Lens Searches in KiDS/GAMA. Astronomical Journal, 160(5), Article 223. https://doi.org/10.3847/1538-3881/abb612

Strong gravitational lenses are a rare and instructive type of astronomical object. Identification has long relied on serendipity, but different strategies—such as mixed spectroscopy of multiple galaxies along the line of sight, machine-learning algo... Read More about Galaxy and Mass Assembly: A Comparison between Galaxy–Galaxy Lens Searches in KiDS/GAMA.

Galaxy Zoo Builder: Four-component Photometric Decomposition of Spiral Galaxies Guided by Citizen Science (2020)
Journal Article
Lingard, T. K., Masters, K. L., Krawczyk, C., Lintott, C., Kruk, S., Simmons, B., …Baeten, E. (2020). Galaxy Zoo Builder: Four-component Photometric Decomposition of Spiral Galaxies Guided by Citizen Science. Astrophysical Journal, 900(2), 178. https://doi.org/10.3847/1538-4357/ab9d83

Multicomponent modeling of galaxies is a valuable tool in the effort to quantitatively understand galaxy evolution, yet the use of the technique is plagued by issues of convergence, model selection, and parameter degeneracies. These issues limit its... Read More about Galaxy Zoo Builder: Four-component Photometric Decomposition of Spiral Galaxies Guided by Citizen Science.

Multi-wavelength structure analysis of local cluster galaxies: The WINGS project (2020)
Journal Article
Psychogyios, A., Vika, M., Charmandaris, V., Bamford, S., Fasano, G., Häußler, B., …Vulcani, B. (2020). Multi-wavelength structure analysis of local cluster galaxies: The WINGS project. Astronomy and Astrophysics, 633, A104. https://doi.org/10.1051/0004-6361/201833522

© ESO 2020. We present a multi-wavelength analysis of the galaxies in nine clusters selected from the WINGS dataset, examining how galaxy structure varies as a function of wavelength and environment using the state of the art software GALAPAGOS-2. We... Read More about Multi-wavelength structure analysis of local cluster galaxies: The WINGS project.

Galaxy Zoo: probabilistic morphology through Bayesian CNNs and active learning (2019)
Journal Article
Walmsley, M., Smith, L., Lintott, C., Gal, Y., Bamford, S., Dickinson, H., …Wright, D. (2020). Galaxy Zoo: probabilistic morphology through Bayesian CNNs and active learning. Monthly Notices of the Royal Astronomical Society, 491(2), 1554-1574. https://doi.org/10.1093/mnras/stz2816

We use Bayesian convolutional neural networks and a novel generative model of Galaxy Zoo volunteer responses to infer posteriors for the visual morphology of galaxies. Bayesian CNN can learn from galaxy images with uncertain labels and then, for prev... Read More about Galaxy Zoo: probabilistic morphology through Bayesian CNNs and active learning.