Subjects: Astronomy >> Astrophysical processes submitted time 2023-02-19
H. -B. Yuan
L. Yang
P. Cruz
F. Jiménez-Esteban
S. Daflon
V. M. Placco
S. Akras
E. J. Alfaro
C. Andrés Galarza
D. R. Gonçalves
F. -Q. Duan
J. -F. Liu
J. Laur
E. Solano
M. Borges Fernandes
A. J. Cenarro
A. Marín-Franch
J. Varela
A. Ederoclite
Carlos López-Sanjuan
Abstract： With a unique set of 54 overlapping narrow-band and two broader filters covering the entire optical range, the incoming Javalambre-Physics of the Accelerating Universe Astrophysical Survey (J-PAS) will provide a great opportunity for stellar physics and near-field cosmology. In this work, we use the miniJPAS data in 56 J-PAS filters and 4 complementary SDSS-like filters to explore and prove the potential of the J-PAS filter system in characterizing stars and deriving their atmospheric parameters. We obtain estimates for the effective temperature with a good precision (<150 K) from spectral energy distribution fitting. We have constructed the metallicity-dependent stellar loci in 59 colours for the miniJPAS FGK dwarf stars, after correcting certain systematic errors in flat-fielding. The very blue colours, including uJAVA-r, J0378-r, J0390-r, uJPAS-r, show the strongest metallicity dependence, around 0.25 mag/dex. The sensitivities decrease to about 0.1 mag/dex for the J0400-r, J0410-r, and J0420-r colours. The locus fitting residuals show peaks at the J0390, J0430, J0510, and J0520 filters, suggesting that individual elemental abundances such as [Ca/Fe], [C/Fe], and [Mg/Fe] can also be determined from the J-PAS photometry. Via stellar loci, we have achieved a typical metallicity precision of 0.1 dex. The miniJPAS filters also demonstrate strong potential in discriminating dwarfs and giants, particularly the J0520 and J0510 filters. Our results demonstrate the power of the J-PAS filter system in stellar parameter determinations and the huge potential of the coming J-PAS survey in stellar and Galactic studies.
Peer Review Status:
Awaiting Review
Subjects: Astronomy >> Astrophysical processes submitted time 2023-02-19
H. -B. Yuan
L. Yang
P. Cruz
F. Jiménez-Esteban
S. Daflon
V. M. Placco
S. Akras
E. J. Alfaro
C. Andrés Galarza
D. R. Gonçalves
F. -Q. Duan
J. -F. Liu
J. Laur
E. Solano
M. Borges Fernandes
A. J. Cenarro
A. Marín-Franch
J. Varela
A. Ederoclite
Carlos López-Sanjuan
Abstract： With a unique set of 54 overlapping narrow-band and two broader filters covering the entire optical range, the incoming Javalambre-Physics of the Accelerating Universe Astrophysical Survey (J-PAS) will provide a great opportunity for stellar physics and near-field cosmology. In this work, we use the miniJPAS data in 56 J-PAS filters and 4 complementary SDSS-like filters to explore and prove the potential of the J-PAS filter system in characterizing stars and deriving their atmospheric parameters. We obtain estimates for the effective temperature with a good precision (<150 K) from spectral energy distribution fitting. We have constructed the metallicity-dependent stellar loci in 59 colours for the miniJPAS FGK dwarf stars, after correcting certain systematic errors in flat-fielding. The very blue colours, including uJAVA-r, J0378-r, J0390-r, uJPAS-r, show the strongest metallicity dependence, around 0.25 mag/dex. The sensitivities decrease to about 0.1 mag/dex for the J0400-r, J0410-r, and J0420-r colours. The locus fitting residuals show peaks at the J0390, J0430, J0510, and J0520 filters, suggesting that individual elemental abundances such as [Ca/Fe], [C/Fe], and [Mg/Fe] can also be determined from the J-PAS photometry. Via stellar loci, we have achieved a typical metallicity precision of 0.1 dex. The miniJPAS filters also demonstrate strong potential in discriminating dwarfs and giants, particularly the J0520 and J0510 filters. Our results demonstrate the power of the J-PAS filter system in stellar parameter determinations and the huge potential of the coming J-PAS survey in stellar and Galactic studies.
Peer Review Status:Awaiting Review
Subjects: Astronomy >> Astrophysical processes submitted time 2023-02-19
Cunshi Wang
Yu Bai
Haibo Yuan
Jifeng Liu
J. A. Fernández-Ontiveros
Paula R. T. Coelho
F. Jiménez-Esteban
Carlos Andrés Galarza
R. E. Angulo
A. J. Cenarro
D. Cristóbal-Hornillos
R. A. Dupke
A. Ederoclite
C. Hernández-Monteagudo
C. López-Sanjuan
A. Marín-Franch
M. Moles
L. Sodré
H. Vázquez Ramió
J. Varela
Abstract： Context. Stellar parameters are among the most important characteristics in studies of stars, which are based on atmosphere models in traditional methods. However, time cost and brightness limits restrain the efficiency of spectral observations. The J-PLUS is an observational campaign that aims to obtain photometry in 12 bands. Owing to its characteristics, J-PLUS data have become a valuable resource for studies of stars. Machine learning provides powerful tools to efficiently analyse large data sets, such as the one from J-PLUS, and enable us to expand the research domain to stellar parameters. Aims. The main goal of this study is to construct a SVR algorithm to estimate stellar parameters of the stars in the first data release of the J-PLUS observational campaign. Methods. The training data for the parameters regressions is featured with 12-waveband photometry from J-PLUS, and is cross-identified with spectrum-based catalogs. These catalogs are from the LAMOST, the APOGEE, and the SEGUE. We then label them with the stellar effective temperature, the surface gravity and the metallicity. Ten percent of the sample is held out to apply a blind test. We develop a new method, a multi-model approach in order to fully take into account the uncertainties of both the magnitudes and stellar parameters. The method utilizes more than two hundred models to apply the uncertainty analysis. Results. We present a catalog of 2,493,424 stars with the Root Mean Square Error of 160K in the effective temperature regression, 0.35 in the surface gravity regression and 0.25 in the metallicity regression. We also discuss the advantages of this multi-model approach and compare it to other machine-learning methods.
Peer Review Status:Awaiting Review
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