Predicting the redshift of gamma-ray loud AGNs using Supervised Machine learning

Measuring the redshift of active galactic nuclei (AGNs) requires the use of time-consuming and expensive spectroscopic analysis. However, obtaining redshift measurements of AGNs is crucial as it can enable AGN population studies, provide insight into the star formation rate, the luminosity function, and the density rate evolution. Hence, there is a requirement for alternative redshift measurement techniques. In this project, we aim to use the Fermi gamma-ray space telescope's 4LAC Data Release 2 (DR2) catalog to train a machine learning (ML) model capable of predicting the redshift of gamma ray loud AGNs reliably. We use the framework of the ensemble learning model Superlearner to train multiple ML models on the 4LAC data. We further implement feature engineering to expand the parameter space and a bias correction technique. We enhance the dataset with the use of an imputation method called Multivariate Imputation using Chained Equations (MICE) which is used to fill in 20% of the missing data. Additionally, we also test a novel ML model called Sorted L-One Penalized Estimation (SLOPE). Using these methods we achieved a correlation of 72% between the predicted and observed redshifts of 4LAC AGNs, with a low RMSE of 0.46. We also provide a catalog of estimated redshift values for those AGNs that do not have a spectroscopic redshift measurement. These estimates can serve as a redshift reference for the community to verify as updated Fermi catalogs are released with spectroscopic redshift measurements.