Machine Learning–based Search of High-redshift Quasars

Abstract We present a machine learning search for high-redshift (5.0 < z < 6.5) quasars using the combined photometric data from the Dark Energy Spectroscopic Instrument (DESI) Imaging Legacy Surveys and the Wide-field Infrared Survey Explorer survey. We explore the imputation of missing values for high-redshift quasars, discuss the feature selections, compare different machine learning algorithms, and investigate the selections of class ensemble for the training sample, then we find that the random forest model is very effective in separating the high-redshift quasars from various contaminators. The 11 class random forest model can achieve a precision of 96.43% and a recall of 91.53% for high-redshift quasars for the test set. We demonstrate that the completeness of the high-redshift quasars can reach as high as 82.20%. The final catalog consists of 216,949 high-redshift quasar candidates with 476 high probable ones in the entire Legacy Surveys DR9 footprint, and we make the catalog publicly available. Using Multi Unit Spectroscopic Explorer (MUSE) and DESI early data release (EDR) public spectra, we find that 14 true high-redshift quasars (11 in the training sample) out of 21 candidates are correctly identified for MUSE, and 20 true high-redshift quasars (11 in the training sample) out of 21 candidates are correctly identified for DESI-EDR. Additionally, we estimate photometric redshift for the high-redshift quasar candidates using a random forest regression model with a high precision.