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

TL;DR

This paper demonstrates that the k-nearest neighbor algorithm, when applied to large survey datasets, effectively estimates quasar photometric redshifts and outperforms other methods in accuracy.

Contribution

It introduces an optimized kNN approach using multiband survey data, highlighting its effectiveness and superiority over other techniques for quasar redshift estimation.

Findings

kNN achieves best results with specific input patterns and k values.

Multiband data improves redshift estimation accuracy.

kNN with KD-Tree outperforms other methods.

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.