Predicting the Redshift of gamma-Ray-loud AGNs Using Supervised Machine Learning

Abstract

Active galactic nuclei (AGNs) are very powerful galaxies characterized by extremely bright emissions coming from their central massive black holes. Knowing the redshifts of AGNs provides us with an opportunity to determine their distance to investigate important astrophysical problems, such as the evolution of the early stars and their formation, along with the structure of early galaxies. The redshift determination is challenging because it requires detailed follow-up of multiwavelength observations, often involving various astronomical facilities. Here we employ machine-learning algorithms to estimate redshifts from the observed gamma-ray properties and photometric data of gamma-ray-loud AGNs from the Fourth Fermi-LAT Catalog. The prediction is obtained with the Superlearner algorithm using a LASSO-selected set of predictors. We obtain a tight correlation, with a Pearson correlation coefficient of 71.3% between the inferred and observed redshifts and an average Delta z (norm) = 11.6 x 10(-4). We stress that, notwithstanding the small sample of gamma-ray-loud AGNs, we obtain a reliable predictive model using Superlearner, which is an ensemble of several machine-learning models.