New Empirical Fitting Functions for the Lick/IDS Indices Using MILES

Bibcode
2010hsa5.conf..309M
DOI
10.1007/978-3-642-11250-8_54
Martín-Hernández, J. M.; Mármol-Queraltó, E.; Gorgas, J.; Cardiel, N.; Sánchez-Blázquez, P.; Cenarro, A. J.; Peletier, R. F.; Vazdekis, A.; Falcón-Barroso, J.
Referencia bibliográfica

Highlights of Spanish Astrophysics V, Astrophysics and Space Science Proceedings. ISBN 978-3-642-11249-2. Springer-Verlag Berlin Heidelberg, 2010, p. 309

Fecha de publicación:
2010
Número de autores
9
Número de autores del IAC
2
Número de citas
0
Número de citas referidas
0
Descripción
We are presenting new empirical fitting functions for the Lick/IDS line-strength indices as measured in MILES (Medium-resolution INT Library of Empirical Spectra). Following previous work in the field, these functions describe the empirical behaviour of the line-strength indices with the atmospheric stellar parameters (T eff, logg, [Fe/H]). In order to derive the fitting functions we have devised a new procedure which, being fully automatic, provides a better description of the line-strength index variations in the stellar parameter space.
Tipo
Sin árbitro

Te puede interesar

  • Sin árbitro
    Convection, rotation, and magnetic activity of solar-like stars from asteroseismology
    During the last decade, our understanding of stellar physics and evolution has undergone a tremendous revolution thanks to asteroseismology. Space missions such as CoRoT, Kepler, K2, and TESS have already been observing millions of stars providing high-precision photometric data. With these data, it is possible to study the convection of stars
    Mathur, Savita

    Fecha de publicación:

    0
    2024
    Bibcode
    2024IAUS..365..195M
    Número de citas
    0
  • Sin árbitro
    Tales of stellar & binary co-evolution, told by stellar oscillations
    Stellar binarity and stellar oscillations are mostly seen and studied as two separate phenomena. Sometimes, one process is even complicating the analysis of the other beyond the liking of one or the other research community. However, the complete picture painted by the physical constraints provided by the combination of both processes represents a
    Beck, Paul G.

    Fecha de publicación:

    10
    2024
    Bibcode
    2024bss..confE..35B
    Número de citas
    0
  • Sin árbitro
    The Multimodal Universe: 100 TB of Machine Learning Ready Astronomical Data
    We present the Multimodal Universe, a new framework collating over 100 TB of multimodal astronomical data for its first release, spanning images, spectra, time series, tabular and hyper-spectral data. This unified collection enables a wide variety of machine learning (ML) applications and research across astronomical domains. The dataset brings
    Angeloudi, Eirini et al.

    Fecha de publicación:

    12
    2024
    Bibcode
    2024RNAAS...8..301A
    Número de citas
    0