A Data-driven Study of RR Lyrae Near-IR Light Curves

Principal Component Analysis, Robust Fits, and Metallicity Estimates

Gergely Hajdu, István Dékány, Márcio Catelan, Eva K. Grebel, Johanna Jurcsik

Resultado de la investigación: Article

4 Citas (Scopus)

Resumen

RR Lyrae variables are widely used tracers of Galactic halo structure and kinematics, but they can also serve to constrain the distribution of the old stellar population in the Galactic bulge. With the aim of improving their near-infrared photometric characterization, we investigate their near-infrared light curves, as well as the empirical relationships between their light curve and metallicities using machine learning methods. We introduce a new, robust method for the estimation of the light-curve shapes, hence the average magnitudes of RR Lyrae variables in the K S band, by utilizing the first few principal components (PCs) as basis vectors, obtained from the PC analysis of a training set of light curves. Furthermore, we use the amplitudes of these PCs to predict the light-curve shape of each star in the J-band, allowing us to precisely determine their average magnitudes (hence colors), even in cases where only one J measurement is available. Finally, we demonstrate that the K S-band light-curve parameters of RR Lyrae variables, together with the period, allow the estimation of the metallicity of individual stars with an accuracy of ∼0.2-0.25 dex, providing valuable chemical information about old stellar populations bearing RR Lyrae variables. The methods presented here can be straightforwardly adopted for other classes of variable stars, bands, or for the estimation of other physical quantities.

Idioma originalEnglish
Número de artículo55
PublicaciónAstrophysical Journal
Volumen857
N.º1
DOI
EstadoPublished - 10 abr 2018
Publicado de forma externa

Huella dactilar

principal components analysis
light curve
metallicity
principal component analysis
estimates
S band
near infrared
stars
galactic bulge
machine learning
galactic halos
variable stars
tracers
education
kinematics
tracer
color
method

ASJC Scopus subject areas

  • Astronomy and Astrophysics
  • Space and Planetary Science

Citar esto

Hajdu, Gergely ; Dékány, István ; Catelan, Márcio ; Grebel, Eva K. ; Jurcsik, Johanna. / A Data-driven Study of RR Lyrae Near-IR Light Curves : Principal Component Analysis, Robust Fits, and Metallicity Estimates. En: Astrophysical Journal. 2018 ; Vol. 857, N.º 1.
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abstract = "RR Lyrae variables are widely used tracers of Galactic halo structure and kinematics, but they can also serve to constrain the distribution of the old stellar population in the Galactic bulge. With the aim of improving their near-infrared photometric characterization, we investigate their near-infrared light curves, as well as the empirical relationships between their light curve and metallicities using machine learning methods. We introduce a new, robust method for the estimation of the light-curve shapes, hence the average magnitudes of RR Lyrae variables in the K S band, by utilizing the first few principal components (PCs) as basis vectors, obtained from the PC analysis of a training set of light curves. Furthermore, we use the amplitudes of these PCs to predict the light-curve shape of each star in the J-band, allowing us to precisely determine their average magnitudes (hence colors), even in cases where only one J measurement is available. Finally, we demonstrate that the K S-band light-curve parameters of RR Lyrae variables, together with the period, allow the estimation of the metallicity of individual stars with an accuracy of ∼0.2-0.25 dex, providing valuable chemical information about old stellar populations bearing RR Lyrae variables. The methods presented here can be straightforwardly adopted for other classes of variable stars, bands, or for the estimation of other physical quantities.",
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A Data-driven Study of RR Lyrae Near-IR Light Curves : Principal Component Analysis, Robust Fits, and Metallicity Estimates. / Hajdu, Gergely; Dékány, István; Catelan, Márcio; Grebel, Eva K.; Jurcsik, Johanna.

En: Astrophysical Journal, Vol. 857, N.º 1, 55, 10.04.2018.

Resultado de la investigación: Article

TY - JOUR

T1 - A Data-driven Study of RR Lyrae Near-IR Light Curves

T2 - Principal Component Analysis, Robust Fits, and Metallicity Estimates

AU - Hajdu, Gergely

AU - Dékány, István

AU - Catelan, Márcio

AU - Grebel, Eva K.

AU - Jurcsik, Johanna

PY - 2018/4/10

Y1 - 2018/4/10

N2 - RR Lyrae variables are widely used tracers of Galactic halo structure and kinematics, but they can also serve to constrain the distribution of the old stellar population in the Galactic bulge. With the aim of improving their near-infrared photometric characterization, we investigate their near-infrared light curves, as well as the empirical relationships between their light curve and metallicities using machine learning methods. We introduce a new, robust method for the estimation of the light-curve shapes, hence the average magnitudes of RR Lyrae variables in the K S band, by utilizing the first few principal components (PCs) as basis vectors, obtained from the PC analysis of a training set of light curves. Furthermore, we use the amplitudes of these PCs to predict the light-curve shape of each star in the J-band, allowing us to precisely determine their average magnitudes (hence colors), even in cases where only one J measurement is available. Finally, we demonstrate that the K S-band light-curve parameters of RR Lyrae variables, together with the period, allow the estimation of the metallicity of individual stars with an accuracy of ∼0.2-0.25 dex, providing valuable chemical information about old stellar populations bearing RR Lyrae variables. The methods presented here can be straightforwardly adopted for other classes of variable stars, bands, or for the estimation of other physical quantities.

AB - RR Lyrae variables are widely used tracers of Galactic halo structure and kinematics, but they can also serve to constrain the distribution of the old stellar population in the Galactic bulge. With the aim of improving their near-infrared photometric characterization, we investigate their near-infrared light curves, as well as the empirical relationships between their light curve and metallicities using machine learning methods. We introduce a new, robust method for the estimation of the light-curve shapes, hence the average magnitudes of RR Lyrae variables in the K S band, by utilizing the first few principal components (PCs) as basis vectors, obtained from the PC analysis of a training set of light curves. Furthermore, we use the amplitudes of these PCs to predict the light-curve shape of each star in the J-band, allowing us to precisely determine their average magnitudes (hence colors), even in cases where only one J measurement is available. Finally, we demonstrate that the K S-band light-curve parameters of RR Lyrae variables, together with the period, allow the estimation of the metallicity of individual stars with an accuracy of ∼0.2-0.25 dex, providing valuable chemical information about old stellar populations bearing RR Lyrae variables. The methods presented here can be straightforwardly adopted for other classes of variable stars, bands, or for the estimation of other physical quantities.

KW - methods: data analysis

KW - methods: observational

KW - methods: statistical

KW - stars: variables: RR Lyrae

KW - techniques: photometric

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