Predicting the redshift of gamma-ray loud AGNs using Supervised Machine Learning: Part 2

Abstract

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.

arXiv comment26 pages, 16 figures, 3 tables

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• Fermi LAT AGN classification using supervised machine learning
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• Estimation of redshift and associated uncertainty of Fermi/LAT extra-galactic sources with Deep Learning
• Astrometric properties of reference frame sources as a function of redshift
• Estimating the peak energy of Swift gamma-ray bursts using supervised machine learning
• A GeV Gamma-Ray Spectral Excess in the Fermi Blazar 4FGL J0136.5+3906