J-PLUS: Support Vector Machine Applied to STAR-GALAXY-QSOClassification

TL;DR

This paper develops a supervised SVM classifier for star-galaxy-quasar classification using multi-band photometry from J-PLUS DR1, achieving over 96% accuracy and demonstrating robustness across various validation datasets.

Contribution

It introduces a novel application of SVM to classify astronomical objects in J-PLUS data with high accuracy, incorporating a new algorithm to improve extrapolation performance.

Findings

Achieved 96.5% accuracy in blind tests.

F1-scores demonstrate high precision for each class.

Robust performance across multiple validation datasets.

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