Machine-learned Identification of RR Lyrae Stars from Sparse, Multi-band Data: The PS1 Sample

Branimir Sesar, Nina Hernitschek, Sandra Mitrović, Željko Ivezić, Hans‐Walter Rix, Judith G. Cohen, Edouard J. Bernard, E. K. Grebel, Nicolas F. Martin, Edward F. Schlafly, W. S. Burgett, P. W. Draper, H. Flewelling, Nick Kaiser, Rolf P. Kudritzki, E. A. Magnier, N. Metcalfe, J. Tonry, C. Waters

The Astronomical Journal, 2017

Max Planck Institute for Astronomy, University of Washington, California Institute of Technology, Centre National de la Recherche Scientifique, Université Côte d'Azur, Observatoire de la Côte d’Azur, LaGrange College, Heidelberg University, Observatoire astronomique de Strasbourg, Université de Strasbourg, Lawrence Berkeley National Laboratory, TMT Observatory, Durham University, University of Hawaiʻi at Mānoa

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Abstract

This research paper explore the methodology and findings associated with The Astronomical Journal. The study delves into the core aspects of the research field, providing significant data and citation impact. (Full abstract processing is available via the OpenAlex API).

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Machine-learned Identification of RR Lyrae Stars from Sparse, Multi-band Data: The PS1 Sample

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