Predicting the redshift of gamma-ray loud AGNs using Supervised Machine
  Learning: Part 2
  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 (DR2) catalog to train a machine
learning model capable of predicting the redshift reliably. In addition, this
project aims at improving and extending with the new 4LAC Catalog the
predictive capabilities of the machine learning (ML) methodology published in
Dainotti et al. (2021). Furthermore, we implement feature engineering to expand
the parameter space and a bias correction technique to our final results. This
study uses additional machine learning techniques inside the ensemble method,
the SuperLearner, previously used in Dainotti et al.(2021). Additionally, we
also test a novel ML model called Sorted L-One Penalized Estimation (SLOPE).
Using these methods we 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 more redshift measurements.
