The Feasibility and Flexibility of Selecting Quasars by Variability Using Ensemble Machine Learning Algorithms
| Autor: | Zhang-Liang Xie, Jun-Xian Wang, Da-Ming Yang |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Physics
Boosting (machine learning) 010308 nuclear & particles physics FOS: Physical sciences Astronomy and Astrophysics Quasar Sample (statistics) Astrophysics::Cosmology and Extragalactic Astrophysics Astrophysics - Astrophysics of Galaxies 01 natural sciences Ensemble learning Random forest Stars Variable (computer science) Space and Planetary Science Astrophysics of Galaxies (astro-ph.GA) 0103 physical sciences 010303 astronomy & astrophysics Algorithm Selection (genetic algorithm) Astrophysics::Galaxy Astrophysics |
| DOI: | 10.48550/arxiv.2011.03160 |
| Popis: | In this work we train three decision-tree based ensemble machine learning algorithms (Random Forest Classifier, Adaptive Boosting and Gradient Boosting Decision Tree respectively) to study quasar selection in the variable source catalog in SDSS Stripe 82. We build training and test samples (both containing 1:1 of quasars and stars) using the spectroscopic confirmed sources in SDSS DR14 (including 8330 quasars and 3966 stars). We find that, trained with variation parameters alone, all three models can select quasars with similarly and remarkably high precision and completeness ($\sim$ 98.5% and 97.5%), even better than trained with SDSS colors alone ($\sim$ 97.2% and 96.5%), consistent with previous studies. Through applying the trained models on the variable sources without spectroscopic identifications, we estimate the spectroscopically confirmed quasar sample in Stripe 82 variable source catalog is $\sim$ 93% complete (95% for $m_i 20 pages, 13 figures, accepted to RAA |
| Databáze: | OpenAIRE |
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