The RAdial Velocity Experiment (RAVE): Parameterisation of RAVE spectra based on convolutional neural networks

[Context] Data-driven methods play an increasingly important role in the field of astrophysics In the context of large spectroscopic surveys of stars, data-driven methods are key in deducing physical parameters for millions of spectra in a short time. Convolutional neural networks (CNNs) enable us t...

Descripción completa

Detalles Bibliográficos
Autores: Guiglion, Guillaume, Matijevic, E., Queiroz, Anna Bárbara de Andrade, Valentini, M., Steinmetz, Matthias, Chiappini, Cristina, Grebel, E.K., McMillan, Paul J., Kordopatis, G., Kunder, A., Zwitter, T., Khalatyan, A., Anders, Friedrich, Enke, H., Minchev, Ivan, Monari, Giacomo, Wyse, R. F. G., Bienaymé, O., Bland-Hawthorn, J., Gibson, Brad K., Navarro, Julio F., Parker, Quentin A., Reid, Warrenk A., Seabroke, G. M., Siebert, A.
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2020
País:España
Institución:Consejo Superior de Investigaciones Científicas (CSIC)
Repositorio:DIGITAL.CSIC. Repositorio Institucional del CSIC
OAI Identifier:oai:digital.csic.es:10261/234355
Acceso en línea:http://hdl.handle.net/10261/234355
Access Level:acceso abierto
Palabra clave:Galaxy: abundances
Galaxy: stellar content
Methods: data analysis
Stars: abundances
Techniques: spectroscopic
Descripción
Sumario:[Context] Data-driven methods play an increasingly important role in the field of astrophysics In the context of large spectroscopic surveys of stars, data-driven methods are key in deducing physical parameters for millions of spectra in a short time. Convolutional neural networks (CNNs) enable us to connect observables (e.g. spectra, stellar magnitudes) to physical properties (atmospheric parameters, chemical abundances, or labels in general).

Similares en LA Referencia