Subjects: Astronomy >> Astrophysical processes submitted time 2023-02-19
Cunshi Wang
Yu Bai
C. López-Sanjuan
Haibo Yuan
Song Wang
Jifeng Liu
David Sobral
P. O. Baqui
E. L. Martín
Carlos Andres Galarza
J. Alcaniz
R. E. Angulo
A. J. Cenarro
D. Cristóbal-Hornillos
R. A. Dupke
A. Ederoclite
C. Hernández-Monteagudo
A. Marín-Franch
M. Moles
L. Sodré
Abstract： Context. In modern astronomy, machine learning has proved to be efficient and effective to mine the big data from the newesttelescopes. Spectral surveys enable us to characterize millions of objects, while long exposure time observations and wide surveysconstrain their strides from millions to billions. Aims.In this study, we construct a supervised machine learning algorithm, to classify the objects in the Javalambre Photometric LocalUniverse Survey first data release (J-PLUS DR1). Methods.The sample set is featured with 12-waveband photometry, and magnitudes are labeled with spectrum-based catalogs, in-cluding Sloan Digital Sky Survey spectroscopic data, Large Sky Area Multi-Object Fiber Spectroscopic Telescope, and VERONCAT- Veron Catalog of Quasars & AGN. The performance of the classifier is presented with applications of blind test validations basedon RAdial Velocity Extension, Kepler Input Catalog, 2 MASS Redshift Survey, and the UV-bright Quasar Survey. A new algorithmis applied to constrain the extrapolation that could decrease accuracies for many machine learning classifiers. Results.The accuracies of the classifier are 96.5% in blind test and 97.0% in training cross validation. The F1-scores for each classare presented to show the precision of the classifier. We also discuss different methods to constrain the po
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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