Classification of Quasars and Stars by Supervised and Unsupervised Methods

Abstract

Targeting quasar candidates is always an important task for large spectroscopic sky survey projects. Astronomers never give up thinking out effective approaches to separate quasars from stars. The previous methods on this issue almost belong to supervised methods or color-color cut. In this work, we compare the performance of a supervised method -Support Vector Machine (SVM)-with that of an unsupervised method one-class SVM. The performance of SVM is better than that of one-class SVM. But one-class SVM is an unsupervised algorithm which is helpful to recognize rare or mysterious objects. Combining supervised methods with unsupervised methods is effective to improve the performance of a single classifier.

Machine Learning Classification of Stars, Galaxies, and Quasars

“…With modern telescopes recording an increasingly large amount of astronomical data, the usage of machine learning models in the task of classification has become more and more significant and prevalent because of their accuracy and speed (Clarke et al, 2020). While reviews of previous studies with similar objectives of classifications found both supervised and unsupervised machine learning models to be capable of great performance with accuracy and other metrics measured at over 90%, the supervised models had generally higher accuracies in classification, and unsupervised models were shown to be more effective at detecting unknown objects (Viquar et al, 2018;Zhang et al, 2013). With supervised ML models, the classification models of random forest and support vector model (SVM) were used, along with decision tree and logistic regression, to either solely identify and separate quasars from other objects or to classify all three of stars galaxies, and quasars (Carrasco et al, 2015).…”

Development of accurate classification of heavenly bodies using novel machine learning techniques

“…There is a significant increase in research works related to stellar spectra detection and classification. Many researchers focused on star-quasar (Zhang et al 2011;Jin et al 2019;Zhang et al 2009Zhang et al , 2013Viquar et al 2018), galaxy-quasar (Bailer-Jones et al 2019 or star-galaxy Philip et al (2002) binary classification. Others (López et al 2010, Becker et al 2020) focused on multi-class classification of stars, galaxies and quasars Cabanac et al (2002); Acharya et al (2018).…”

“…Many authors used classical machine learning algorithms such as support vector machines (SVM) or k-nearest neighbors (kNN) (Zhang et al 2011(Zhang et al , 2009(Zhang et al , 2013Tu et al 2015,[12,15], Jin et al 2019;Viquar et al 2018). Others adopted deep learning techniques (Becker et al 2020, 11) or developed their own novel solutions (Viquar et al 2018).…”

“…Many databases related to sky survey data are freely available. Among them, most popular databases are SDSS (Zhang et al 2011(Zhang et al , 2013(Zhang et al , 2009Viquar et al 2018, Acharya et al 2018), Gaia (Bailer-Jones et al 2019Becker et al 2020), WISE (Jin et al 2019;Becker et al 2020) and UKIDSS (Zhang et al 2011(Zhang et al , 2013. The summary of related works conducted using these databases is shown in Table 1 Zhang et al ( 2011) operated on the data from UKIDSS database.…”

“…In Zhang et al (2009) a kNN algorithm is used to distinguish star and quasar sources. Authors of Zhang et al (2013) used SVM classifier for the same purpose. They achieved very high accuracy scores using SDSS DR7 and UKIDSS DR7 catalogs of photometric data.…”