Refereed journal article or data article (A1)
Predicting the Redshift of Gamma-Ray Loud AGNs Using Supervised Machine Learning. II
List of Authors: Narendra Aditya, Gibson Spencer James, Dainotti Maria Giovanna, Bogdan Malgorzata, Pollo Agnieszka, Liodakis Ioannis, Poliszczuk Artem, Rinaldi Enrico
Publisher: IOP Publishing Ltd
Publication year: 2022
Journal: Astrophysical Journal Supplement
Journal name in source: ASTROPHYSICAL JOURNAL SUPPLEMENT SERIES
Journal acronym: ASTROPHYS J SUPPL S
Article number: 55
Volume number: 259
Issue number: 2
Number of pages: 16
ISSN: 0067-0049
eISSN: 1538-4365
DOI: http://dx.doi.org/10.3847/1538-4365/ac545a
URL: https://iopscience.iop.org/article/10.3847/1538-4365/ac545a
Self-archived copy’s web address: https://research.utu.fi/converis/portal/detail/Publication/175243819
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 catalog to train a machine-learning (ML) 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 ML methodology published in Dainotti et al. Furthermore, we implement feature engineering to expand the parameter space and a bias correction technique to our final results. This study uses additional ML techniques inside the ensemble method, the SuperLearner, previously used in Dainotti et al. Additionally, we also test a novel ML model called Sorted L-One Penalized Estimation. 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.
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