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AGNs are very powerful galaxies characterized by extremely bright emissionscoming out from their central massive black holes. Knowing the redshifts ofAGNs provides us with an opportunity to determine their distance to investigateimportant astrophysical problems such as the evolution of the early stars,their formation along with the structure of early galaxies. The redshiftdetermination is challenging because it requires detailed follow-up ofmulti-wavelength observations, often involving various astronomical facilities.Here, we employ machine learning algorithms to estimate redshifts from theobserved gamma-ray properties and photometric data of gamma-ray loud AGN fromthe Fourth Fermi-LAT Catalog. The prediction is obtained with the Superlearneralgorithm, using LASSO selected set of predictors. We obtain a tightcorrelation, with a Pearson Correlation Coefficient of 71.3% between theinferred and the observed redshifts, an average {\Delta}z_norm = 11.6 x 10^-4.We stress that notwithstanding the small sample of gamma-ray loud AGNs, weobtain a reliable predictive model using Superlearner, which is an ensemble ofseveral machine learning models.

Predicting the redshift of gamma-ray loud AGNs using supervised machine learning