Outputs (7)

SDSS-IV MaNGA: The link between bars and the early cessation of star formation in spiral galaxies (2020)
Journal Article
Fraser-McKelvie, A., Merrifield, M., Aragón-Salamanca, A., Peterken, T., Kraljic, K., Masters, K., Stark, D., Fragkoudi, F., Smethurst, R., Boardman, N. F., Drory, N., & Lane, R. R. (2020). SDSS-IV MaNGA: The link between bars and the early cessation of star formation in spiral galaxies. Monthly Notices of the Royal Astronomical Society, 499(1), 1116-1125. https://doi.org/10.1093/mnras/staa2866

Bars are common in low-redshift disc galaxies, and hence quantifying their influence on their host is of importance to the field of galaxy evolution. We determine the stellar populations and star formation histories of 245 barred galaxies from the Ma... Read More about SDSS-IV MaNGA: The link between bars and the early cessation of star formation in spiral galaxies.

SDSS-IV MaNGA: spatially resolved star formation in barred galaxies (2020)
Journal Article
Fraser-McKelvie, A., Aragón-Salamanca, A., Merrifield, M., Masters, K., Nair, P., Emsellem, E., Kraljic, K., Krishnarao, D., Andrews, B. H., Drory, N., & Neumann, J. (2020). SDSS-IV MaNGA: spatially resolved star formation in barred galaxies. Monthly Notices of the Royal Astronomical Society, 495(4), 4158-4169. https://doi.org/10.1093/mnras/staa1416

Bars inhabit the majority of local-Universe disc galaxies and may be important drivers of galaxy evolution through the redistribution of gas and angular momentum within discs. We investigate the star formation and gas properties of bars in galaxies s... Read More about SDSS-IV MaNGA: spatially resolved star formation in barred galaxies.

SDSS-IV MaNGA: Excavating the fossil record of stellar populations in spiral galaxies (2020)
Journal Article
Peterken, T., Merrifield, M., Aragón-Salamanca, A., Fraser-McKelvie, A., Avila-Reese, V., Riffel, R., Knapen, J., & Drory, N. (2020). SDSS-IV MaNGA: Excavating the fossil record of stellar populations in spiral galaxies. Monthly Notices of the Royal Astronomical Society, 495(3), 3387-3402. https://doi.org/10.1093/mnras/staa1303

We perform a 'fossil record' analysis for ≈800 low-redshift spiral galaxies, using starlight applied to integral field spectroscopic observations from the SDSS-IV MaNGA survey to obtain fully spatially resolved high-resolution star formation historie... Read More about SDSS-IV MaNGA: Excavating the fossil record of stellar populations in spiral galaxies.

Mapping and characterization of cosmic filaments in galaxy cluster outskirts: strategies and forecasts for observations from simulations (2020)
Journal Article
Kuchner, U., Aragón-Salamanca, A., Pearce, F. R., Gray, M. E., Rost, A., Mu, C., Welker, C., Cui, W., Haggar, R., Laigle, C., Knebe, A., Kraljic, K., Sarron, F., & Yepes, G. (2020). Mapping and characterization of cosmic filaments in galaxy cluster outskirts: strategies and forecasts for observations from simulations. Monthly Notices of the Royal Astronomical Society, 494(4), 5473-5491. https://doi.org/10.1093/mnras/staa1083

Upcoming wide-field surveys are well-suited to studying the growth of galaxy clusters by tracing galaxy and gas accretion along cosmic filaments. We use hydrodynamic simulations of volumes surrounding 324 clusters from The ThreeHundred project to dev... Read More about Mapping and characterization of cosmic filaments in galaxy cluster outskirts: strategies and forecasts for observations from simulations.

Identifying Strong Lenses with Unsupervised Machine Learning using Convolutional Autoencoder (2020)
Journal Article
Cheng, T.-Y., Li, N., Conselice, C. J., Aragón-Salamanca, A., Dye, S., & Metcalf, R. B. (2020). Identifying Strong Lenses with Unsupervised Machine Learning using Convolutional Autoencoder. Monthly Notices of the Royal Astronomical Society, 394(3), 3750–3765. https://doi.org/10.1093/mnras/staa1015

In this paper we develop a new unsupervised machine learning technique comprised of a feature extractor, a convolutional autoencoder (CAE), and a clustering algorithm consisting of a Bayesian Gaussian mixture model (BGM). We apply this technique to v... Read More about Identifying Strong Lenses with Unsupervised Machine Learning using Convolutional Autoencoder.

Optimizing automatic morphological classification of galaxies with machine learning and deep learning using Dark Energy Survey imaging (2020)
Journal Article
Cheng, T.-Y., Conselice, C. J., Aragón-Salamanca, A., Li, N., Bluck, A. F., Hartley, W. G., Annis, J., Brooks, D., Doel, P., García-Bellido, J., James, D. J., Kuehn, K., Kuropatkin, N., Smith, M., Sobreira, F., & Tarle, G. (2020). Optimizing automatic morphological classification of galaxies with machine learning and deep learning using Dark Energy Survey imaging. Monthly Notices of the Royal Astronomical Society, 493(3), 4209-4228. https://doi.org/10.1093/mnras/staa501

There are several supervised machine learning methods used for the application of automated morphological classification of galaxies; however, there has not yet been a clear comparison of these different methods using imaging data, or a investigation... Read More about Optimizing automatic morphological classification of galaxies with machine learning and deep learning using Dark Energy Survey imaging.

SDSS-IV MaNGA: when is morphology imprinted on galaxies? (2020)
Journal Article
Peterken, T., Merrifield, M., Aragón-Salamanca, A., Avila-Reese, V., Boardman, N. F., Drory, N., & Lane, R. R. (2020). SDSS-IV MaNGA: when is morphology imprinted on galaxies?. Monthly Notices of the Royal Astronomical Society: Letters, 500(1), L42-L46. https://doi.org/10.1093/mnrasl/slaa179

It remains an open question as to how long ago the morphology that we see in a present-day galaxy was typically imprinted. Studies of galaxy populations at different redshifts reveal that the balance of morphologies has changed over time, but such sn... Read More about SDSS-IV MaNGA: when is morphology imprinted on galaxies?.