Estimating Photometric Redshifts of Quasars via K-nearest Neighbor Approach Based on Large Survey Databases

Abstract

We apply one of lazy learning methods named k-nearest neighbor algorithm (kNN) to estimate the photometric redshifts of quasars, based on various datasets from the Sloan Digital Sky Survey (SDSS), UKIRT Infrared Deep Sky Survey (UKIDSS) and Wide-field Infrared Survey Explorer (WISE) (the SDSS sample, the SDSS-UKIDSS sample, the SDSS-WISE sample and the SDSS-UKIDSS-WISE sample). The influence of the k value and different input patterns on the performance of kNN is discussed. kNN arrives at the best performance when k is different with a special input pattern for a special dataset. The best result belongs to the SDSS-UKIDSS-WISE sample. The experimental results show that generally the more information from more bands, the better performance of photometric redshift estimation with kNN. The results also demonstrate that kNN using multiband data can effectively solve the catastrophic failure of photometric redshift estimation, which is met by many machine learning methods. By comparing the performance of various methods for photometric redshift estimation of quasars, kNN based on KD-Tree shows its superiority with the best accuracy for our case.

28 pages, 4 figures, 3 tables, accepted for publication in AJ

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• Return of the features. Efficient feature selection and interpretation for photometric redshifts
• Finding Quasars behind the Galactic Plane. I. Candidate Selections with Transfer Learning
• Quasar Photometric Redshifts and Candidate Selection: A New Algorithm Based on Optical and Mid-Infrared Photometric Data
• PhotoRedshift-MML: a multimodal machine learning method for estimating photometric redshifts of quasars
• Photometric Redshift Estimation for Quasars by Integration of KNN and SVM
• Estimation of Photometric Redshifts. I. Machine Learning Inference for Pan-STARRS1 Galaxies Using Neural Networks
• PICZL: Image-based Photometric Redshifts for AGN
• CLAP. I. Resolving miscalibration for deep learning-based galaxy photometric redshift estimation
• A statistical study of lopsided galaxies using random forest