Predicting the Redshift of Gamma-Ray Loud AGNs Using Supervised Machine Learning. II
(2022) In Astrophysical Journal, Supplement Series 259(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 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... (More)
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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- author
- Narendra, Aditya ; Gibson, Spencer James ; Dainotti, Maria Giovanna ; Bogdan, Malgorzata LU ; Pollo, Agnieszka ; Liodakis, Ioannis ; Poliszczuk, Artem and Rinaldi, Enrico
- organization
- publishing date
- 2022
- type
- Contribution to journal
- publication status
- published
- subject
- in
- Astrophysical Journal, Supplement Series
- volume
- 259
- issue
- 2
- article number
- 55
- publisher
- IOP Publishing
- external identifiers
-
- scopus:85129098989
- ISSN
- 0067-0049
- DOI
- 10.3847/1538-4365/ac545a
- language
- English
- LU publication?
- yes
- id
- db880e7b-7341-42b6-8d1f-8eb517a3ab7c
- date added to LUP
- 2022-07-04 12:51:45
- date last changed
- 2022-11-21 22:32:31
@article{db880e7b-7341-42b6-8d1f-8eb517a3ab7c,
abstract = {{<p>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. </p>}},
author = {{Narendra, Aditya and Gibson, Spencer James and Dainotti, Maria Giovanna and Bogdan, Malgorzata and Pollo, Agnieszka and Liodakis, Ioannis and Poliszczuk, Artem and Rinaldi, Enrico}},
issn = {{0067-0049}},
language = {{eng}},
number = {{2}},
publisher = {{IOP Publishing}},
series = {{Astrophysical Journal, Supplement Series}},
title = {{Predicting the Redshift of Gamma-Ray Loud AGNs Using Supervised Machine Learning. II}},
url = {{http://dx.doi.org/10.3847/1538-4365/ac545a}},
doi = {{10.3847/1538-4365/ac545a}},
volume = {{259}},
year = {{2022}},
}