Bernardi, Mariangela

THE SEVENTH DATA RELEASE OF THE SLOAN DIGITAL SKY SURVEY

Abazajian, K. N., Adelman-McCarthy, J. K., Agüeros, M. A., Allam, S. S., Prieto, C. A., An, D., Anderson, K. S. J., Anderson, S. F., Annis, J., Bahcall, N. A., et al. (2009) · The Astrophysical Journal Supplement Series

Galaxies: Formation, Evolution, PhenomenaAstronomy and Astrophysical Research

Cited by 5,341

Spectroscopic Target Selection in the Sloan Digital Sky Survey: The Main Galaxy Sample

Strauss, M. A., Weinberg, D. H., Lupton, R. H., Narayanan, V. K., Annis, J., Bernardi, M., Blanton, M., Burles, S., Connolly, A. J., Dalcanton, J., et al. (2002) · The Astronomical Journal

Galaxies: Formation, Evolution, PhenomenaAstronomy and Astrophysical Research

Cited by 1,909

The Sixth Data Release of the Sloan Digital Sky Survey

Adelman‐McCarthy, J. K., Agüeros, M. A., Allam, S. S., Allende Prieto, C., Anderson, K. S. J., Anderson, S. F., Annis, J., Bahcall, N. A., Bailer‐Jones, C. A. L., Baldry, I. K., et al. (2008) · The Astrophysical Journal Supplement Series

Galaxies: Formation, Evolution, PhenomenaAstronomy and Astrophysical Research

Cited by 1,486

Improving galaxy morphologies for SDSS with Deep Learning

Domínguez Sánchez, H., Huertas-Company, M., Bernardi, M., Tuccillo, D., & Fischer, J. L. (2018) · Monthly Notices of the Royal Astronomical Society

Galaxies: Formation, Evolution, PhenomenaAdvanced Neural Network Applications

Cited by 382

A CATALOG OF VISUAL-LIKE MORPHOLOGIES IN THE 5 CANDELS FIELDS USING DEEP LEARNING

Huertas-Company, M., Gravet, R., Cabrera-Vives, G., Pérez-González, P. G., Kartaltepe, J. S., Barro, G., Bernardi, M., Mei, S., Shankar, F., Dimauro, P., et al. (2015) · The Astrophysical Journal Supplement Series

Galaxies: Formation, Evolution, PhenomenaAdvanced Neural Network Applications

Cited by 285

Selection bias in dynamically measured supermassive black hole samples: its consequences and the quest for the most fundamental relation

Shankar, F., Bernardi, M., Sheth, R. K., Ferrarese, L., Graham, A. W., Savorgnan, G., Allevato, V., Marconi, A., Läsker, R., & Lapi, A. (2016) · Monthly Notices of the Royal Astronomical Society

Galaxies: Formation, Evolution, PhenomenaAstrophysical Phenomena and Observations

Cited by 263

Transfer learning for galaxy morphology from one survey to another

Domínguez Sánchez, H., Huertas-Company, M., Bernardi, M., Kaviraj, S., Fischer, J. L., Abbott, T. M. C., Abdalla, F. B., Annis, J., Avila, S., Brooks, D., et al. (2019) · Monthly Notices of the Royal Astronomical Society

Galaxies: Formation, Evolution, PhenomenaDomain Adaptation and Few-Shot Learning

Cited by 91

Detecting outliers in astronomical images with deep generative networks

Margalef-Bentabol, B., Huertas-Company, M., Charnock, T., Margalef-Bentabol, C., Bernardi, M., Dubois, Y., Storey-Fisher, K., & Zanisi, L. (2020) · Monthly Notices of the Royal Astronomical Society

Galaxies: Formation, Evolution, PhenomenaAnomaly Detection Techniques and Applications

Cited by 56

Identification of tidal features in deep optical galaxy images with convolutional neural networks

Sánchez, H. D., Martin, G., Damjanov, I., Buitrago, F., Huertas-Company, M., Bottrell, C., Bernardi, M., Knapen, J. H., Vega-Ferrero, J., Hausen, R., et al. (2023) · Monthly Notices of the Royal Astronomical Society

Galaxies: Formation, Evolution, PhenomenaAdvanced Neural Network Applications

Cited by 28