May 15, 2024, 4:43 a.m. | P. Mas-Buitrago, A. Gonz\'alez-Marcos, E. Solano, V. M. Passegger, M. Cort\'es-Contreras, J. Ordieres-Mer\'e, A. Bello-Garc\'ia, J. A. Caballero, A. S

cs.LG updates on arXiv.org arxiv.org

arXiv:2405.08703v1 Announce Type: cross
Abstract: Deep learning (DL) techniques are a promising approach among the set of methods used in the ever-challenging determination of stellar parameters in M dwarfs. In this context, transfer learning could play an important role in mitigating uncertainties in the results due to the synthetic gap (i.e. difference in feature distributions between observed and synthetic data). We propose a feature-based deep transfer learning (DTL) approach based on autoencoders to determine stellar parameters from high-resolution spectra. Using …

abstract arxiv astro-ph.ep astro-ph.im astro-ph.sr autoencoders context cs.lg deep learning ever parameters role set transfer transfer learning type

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