J-PLUS DR3: Galaxy-Star-Quasar classification

Abstract

The Javalambre Photometric Local Universe Survey (J-PLUS) is a 12-band photometric survey using the 83-cm JAST telescope. Data Release 3 includes 47.4 million sources. J-PLUS DR3 only provides star-galaxy classification so that quasars are not identified from the other sources. Given the size of the dataset, machine learning methods could provide a valid alternative classification and a solution to the classification of quasars. Our objective is to classify J-PLUS DR3 sources into galaxies, stars and quasars, outperforming the available classifiers in each class. We use an automated machine learning tool called TPOT to find an optimized pipeline to perform the classification. The supervised machine learning algorithms are trained on the crossmatch with SDSS DR18, LAMOST DR8 and Gaia. We checked that the training set of about 660 thousand galaxies, 1.2 million stars and 270 thousand quasars is both representative and contain a minimal presence of contaminants (less than 1%). We considered 37 features: the twelve photometric bands with respective errors, six colors, four morphological parameters, galactic extinction with its error and the PSF relative to the corresponding pointing. With TPOT genetic algorithm, we found that XGBoost provides the best performance: the AUC for galaxies, stars and quasars is above 0.99 and the average precision is above 0.99 for galaxies and stars and 0.96 for quasars. XGBoost outperforms the classifiers already provided in J-PLUS DR3 and also classifies quasars.

arXiv comment19 pages, 22 figures

References in corpus (20)

• XGBoost: A Scalable Tree Boosting System
• Maps of Dust IR Emission for Use in Estimation of Reddening and CMBR Foregrounds
• Gaia Data Release 3: Summary of the content and survey properties
• The Eighteenth Data Release of the Sloan Digital Sky Surveys: Targeting and First Spectra from SDSS-V
• J-PLUS: The Javalambre Photometric Local Universe Survey
• The miniJPAS survey: a preview of the Universe in 56 colours
• Internal calibration of Gaia BP/RP low-resolution spectra
• The Extended Baryon Oscillation Spectroscopic Survey: Variability Selection and Quasar Luminosity Function
• Gaia Data Release 3: The extragalactic content
• Identifying galaxies, quasars, and stars with machine learning: A new catalogue of classifications for 111 million SDSS sources without spectra
• Quasar and galaxy classification in Gaia Data Release 2
• The miniJPAS survey: star-galaxy classification using machine learning
• J-PLUS: Morphological star/galaxy classification by PDF analysis
• J-PLUS: photometric calibration of large area multi-filter surveys with stellar and white dwarf loci
• Null test for interactions in the dark sector
• The value-added catalogue for LAMOST DR8 low-resolution spectra
• Finding Quasars behind the Galactic Plane. I. Candidate Selections with Transfer Learning
• J-PLUS: Support Vector Machine Applied to STAR-GALAXY-QSOClassification
• Inferring galaxy dark halo properties from visible matter with Machine Learning
• On the cosmological performance of photometrically classified supernovae with machine learning

Cited by in corpus (5)

• J-PLUS: Bayesian object classification with a strum of BANNJOS
• Semi-supervised classification of stars, galaxies and quasars using K-means and random-forest approaches
• Total and dark mass from observations of galaxy centers with Machine Learning
• A Morphological Model to Separate Resolved-Unresolved Sources in the DESI Legacy Surveys: Application in the LS4 Alert Stream
• The miniJPAS and J-NEP surveys: Machine learning for star-galaxy separation