Identification of BASS DR3 Sources as Stars, Galaxies and Quasars by XGBoost
arXiv:2106.05563 · doi:10.1093/mnras/stab1650
Abstract
The Beijing-Arizona Sky Survey (BASS) Data Release 3 (DR3) catalogue was released in 2019, which contains the data from all BASS and the Mosaic z-band Legacy Survey (MzLS) observations during 2015 January and 2019 March, about 200 million sources. We cross-match BASS DR3 with spectral databases from the Sloan Digital Sky Survey (SDSS) and the Large Sky Area Multi-object Fiber Spectroscopic Telescope (LAMOST) to obtain the spectroscopic classes of known samples. Then, the samples are cross-matched with ALLWISE database. Based on optical and infrared information of the samples, we use the XGBoost algorithm to construct different classifiers, including binary classification and multiclass classification. The accuracy of these classifiers with the best input pattern is larger than 90.0 per cent. Finally, all selected sources in the BASS DR3 catalogue are classified by these classifiers. The classification label and probabilities for individual sources are assigned by different classifiers. When the predicted results by binary classification are the same as multiclass classification with optical and infrared information, the number of star, galaxy and quasar candidates is separately 12 375 838 (P_S>0.95), 18 606 073 (P_G>0.95) and 798 928 (P_Q>0.95). For these sources without infrared information, the predicted results can be as a reference. Those candidates may be taken as input catalogue of LAMOST, DESI or other projects for follow up observation. The classified result will be of great help and reference for future research of the BASS DR3 sources.
arXiv comment15 pages, 10 tables, 6 figures, accepted for publication in Monthly Notices of the Royal Astronomical Society Main Journal
References in corpus (41)
- Scikit-learn: Machine Learning in Python
- The Sloan Digital Sky Survey: Technical Summary
- The Wide-field Infrared Survey Explorer (WISE): Mission Description and Initial On-orbit Performance
- Sloan Digital Sky Survey IV: Mapping the Milky Way, Nearby Galaxies, and the Distant Universe
- Mid-Infrared Selection of Active Galaxies
- The First Data Release (DR1) of the LAMOST general survey
- The Sloan Digital Sky Survey Quasar Catalog: Sixteenth Data Release
- Deep Learning for Real-time Gravitational Wave Detection and Parameter Estimation: Results with Advanced LIGO Data
- Identifying Exoplanets with Deep Learning: A Five Planet Resonant Chain around Kepler-80 and an Eighth Planet around Kepler-90
- Deep Neural Networks to Enable Real-time Multimessenger Astrophysics
- Project Overview of the Beijing-Arizona Sky Survey
- Solar Flare Prediction Model with Three Machine-Learning Algorithms Using Ultraviolet Brightening and Vector Magnetogram
- CMU DeepLens: Deep Learning For Automatic Image-based Galaxy-Galaxy Strong Lens Finding
- Forecasting Solar Flares Using Magnetogram-based Predictors and Machine Learning
- Photometric redshifts and quasar probabilities from a single, data-driven generative model
- Finding strong lenses in CFHTLS using convolutional neural networks
- FRB microstructure revealed by the real-time detection of FRB170827
- A large population of mid-infrared selected, obscured active galaxies in the Bootes field
- Searching for Exoplanets Using Artificial Intelligence
- The FIRST Bright QSO Survey
- The Extended Baryon Oscillation Spectroscopic Survey: Variability Selection and Quasar Luminosity Function
- Applying Deep Learning to Fast Radio Burst Classification
- Identifying galaxies, quasars, and stars with machine learning: A new catalogue of classifications for 111 million SDSS sources without spectra
- LensExtractor: A Convolutional Neural Network in Search of Strong Gravitational Lenses
- Photometric redshifts for the Kilo-Degree Survey. Machine-learning analysis with artificial neural networks
- Classification methods for noise transients in advanced gravitational-wave detectors II: performance tests on Advanced LIGO data
- Separation of pulsar signals from noise with supervised machine learning algorithms
- Five new real-time detections of Fast Radio Bursts with UTMOST
- 3FGL Demographics Outside the Galactic Plane using Supervised Machine Learning: Pulsar and Dark Matter Subhalo Interpretations
- Quasar Probabilities and Redshifts from WISE mid-IR through GALEX UV Photometry
- Using Machine Learning Methods to Forecast If Solar Flares Will Be Associated with CMEs and SEPs
- Automated Clustering Algorithms for Classification of Astronomical Objects
- Cosmology with dropout selection: Straw-man surveys and CMB lensing
- Efficient Selection of Quasar Candidates Based on Optical and Infrared Photometric Data Using Machine Learning
- The Large Bright Quasar Survey VII. The LBQS and FIRST Surveys
- The Extremely Luminous Quasar Survey (ELQS) in the SDSS footprint I.: Infrared Based Candidate Selection
- XMMPZCAT: A catalogue of photometric redshifts for X-ray sources
- Selecting Quasar Candidates by a SVM Classification System
- The Third Data Release of the Beijing-Arizona Sky Survey
- The Second Data Release of the Beijing-Arizona Sky Survey
- The First Data Release of the Beijing-Arizona Sky Survey
Cited by in corpus (13)
- Photometric redshift-aided classification using ensemble learning
- Transferring spectroscopic stellar labels to 217 million Gaia DR3 XP stars with SHBoost
- Photometric Redshift Estimation of BASS DR3 Quasars by Machine Learning
- Carbon-Enhanced Metal-Poor star candidates from BP/RP Spectra in DR3
- CatNorth: An Improved Gaia DR3 Quasar Candidate Catalog with Pan-STARRS1 and CatWISE
- Classification of the \emph{Fermi}-LAT Blazar Candidates of Uncertain type using eXtreme Gradient Boosting
- Total and dark mass from observations of galaxy centers with Machine Learning
- Machine learning applications for the study of AGN physical properties using photometric observations
- Efficient Identification of Broad Absorption Line Quasars using Dimensionality Reduction and Machine Learning
- Exploring the Dependence of Gas Cooling and Heating Functions on the Incident Radiation Field with Machine Learning
- Improved source classification and performance analysis using Gaia DR3
- Photometric classification of QSOs from ALHAMBRA survey using random forest
- Cluster optical depth and pairwise velocity estimation using machine learning