J/ApJS/259/55 Predicted redshifts for 4FGL gamma-ray loud AGNs (Narendra+, 2022)
Predicting the redshift of gamma-ray loud AGNs using supervised machine
learning. II.
Narendra A., Gibson S.J., Dainotti M.G., Bogdan M., Pollo A., Liodakis I.,
Poliszczuk A., Rinaldi E.
<Astrophys. J. Suppl. Ser., 259, 55 (2022)>
=2022ApJS..259...55N 2022ApJS..259...55N
ADC_Keywords: Active gal. nuclei; Gamma rays; Models; Redshifts
Keywords: Active galactic nuclei ; High energy astrophysics ; Blazars
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 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.
File Summary:
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FileName Lrecl Records Explanations
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ReadMe 80 . This file
table3.dat 28 305 List of predictions for the generalization set
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See also:
IX/67 : Incremental Fermi LAT 4th source cat. (4FGL-DR3) (Fermi-LAT col., 2022)
J/ApJ/690/1236 : COSMOS photometric redshift catalog (Ilbert+, 2009)
J/A+A/523/A48 : Gaia photometry (Jordi+, 2010)
J/ApJ/774/157 : Swift GRBs with X-ray afterglows and z<9.5 (Dainotti+, 2013)
J/MNRAS/428/220 : Gamma-ray AGN type determination (Hassan+, 2013)
J/MNRAS/437/968 : AGN automatic photometric classification (Cavuoti+, 2014)
J/ApJ/782/41 : 231 AGN candidates from the 2FGL catalog (Doert+, 2014)
J/MNRAS/462/3180 : 3FGL Blazar of Unknown Type classification (Chiaro+, 2016)
J/A+A/602/A86 : Blazar cand. among Fermi/LAT 3FGL cat. (Lefaucheur+, 2017)
J/AJ/154/269 : A new photo-z method for quasars in Stripe 82 (Yang+, 2017)
J/A+A/619/A14 : Classification-aided zph estimation (Fotopoulou+, 2018)
J/A+A/611/A97 : Phot. quasar candidates in Stripe 82 (Pasquet-Itam+, 2018)
J/ApJS/247/33 : Fermi LAT fourth source catalog (4FGL) (Abdollahi+, 2020)
J/ApJ/892/105 : 4th cat. of Fermi LAT-detected AGNs (4LAC) (Ajello+, 2020)
J/A+A/633/A154 : HDBSCAN star, galaxy, QSO classification (Logan+, 2020)
Byte-by-byte Description of file: table3.dat
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Bytes Format Units Label Explanations
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1- 4 A4 --- --- [4FGL]
6- 17 A12 --- 4FGL 4FGL identifier (JHHMM.m+DDMM)
19- 22 F4.2 --- zPred [0.14/1.02] Predicted redshift (1)
24- 28 F5.2 --- zCor [-0.06/1.34] Bias-corrected, predicted redshift
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Note (1): From the SuperLearner (Van der Laan M.J. et al. 2007
Stat. Appl. Genet. Mol. Biol. 6) ensemble using O1 predictors.
See Section 3.4.
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History:
From electronic version of the journal
References:
Dainotti et al. Paper I. 2021ApJ...920..118D 2021ApJ...920..118D
(End) Prepared by [AAS], Emmanuelle Perret [CDS] 30-Jun-2022