Vertaisarvioitu alkuperäisartikkeli tai data-artikkeli tieteellisessä aikakauslehdessä (A1)

Predicting the Redshift of Gamma-Ray Loud AGNs Using Supervised Machine Learning. II

Julkaisun tekijät: Narendra Aditya, Gibson Spencer James, Dainotti Maria Giovanna, Bogdan Malgorzata, Pollo Agnieszka, Liodakis Ioannis, Poliszczuk Artem, Rinaldi Enrico

Kustantaja: IOP Publishing Ltd

Julkaisuvuosi: 2022

Journal: Astrophysical Journal Supplement


Lehden akronyymi: ASTROPHYS J SUPPL S

Volyymi: 259

Julkaisunumero: 2

Sivujen määrä: 16

ISSN: 0067-0049

eISSN: 1538-4365



Rinnakkaistallenteen osoite:

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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Last updated on 2022-20-05 at 14:44