Fermi LAT AGN classification using supervised machine learning
(2023) In Monthly Notices of the Royal Astronomical Society 525(2). p.1731-1745- Abstract
Classifying active galactic nuclei (AGNs) is a challenge, especially for BL Lacertae objects (BLLs), which are identified by their weak emission line spectra. To address the problem of classification, we use data from the fourth Fermi Catalog, Data Release 3. Missing data hinder the use of machine learning to classify AGNs. A previous paper found that Multivariate Imputation by Chain Equations (MICE) imputation is useful for estimating missing values. Since many AGNs have missing redshift and the highest energy, we use data imputation with MICE and k-nearest neighbours (kNN) algorithm to fill in these missing variables. Then, we classify AGNs into the BLLs or the flat spectrum radio quasars (FSRQs) using the SuperLearner, an ensemble... (More)
Classifying active galactic nuclei (AGNs) is a challenge, especially for BL Lacertae objects (BLLs), which are identified by their weak emission line spectra. To address the problem of classification, we use data from the fourth Fermi Catalog, Data Release 3. Missing data hinder the use of machine learning to classify AGNs. A previous paper found that Multivariate Imputation by Chain Equations (MICE) imputation is useful for estimating missing values. Since many AGNs have missing redshift and the highest energy, we use data imputation with MICE and k-nearest neighbours (kNN) algorithm to fill in these missing variables. Then, we classify AGNs into the BLLs or the flat spectrum radio quasars (FSRQs) using the SuperLearner, an ensemble method that includes several classification algorithms like logistic regression, support vector classifiers, Random Forest, Ranger Random Forest, multivariate adaptive regression spline (MARS), Bayesian regression, and extreme gradient boosting. We find that a SuperLearner model using MARS regression and Random Forest algorithms is 91.1 per cent accurate for kNN-imputed data and 91.2 per cent for MICE-imputed data. Furthermore, the kNN-imputed SuperLearner model predicts that 892 of the 1519 unclassified blazars are BLLs and 627 are FSRQs, while the MICE-imputed SuperLearner model predicts 890 BLLs and 629 FSRQs in the unclassified set. Thus, we can conclude that both imputation methods work efficiently and with high accuracy and that our methodology ushers the way for using SuperLearner as a novel classification method in the AGN community and, in general, in the astrophysics community.
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- author
- Cooper, Nathaniel ; Dainotti, Maria Giovanna ; Narendra, Aditya ; Liodakis, Ioannis and Bogdan, Malgorzata LU
- organization
- publishing date
- 2023-10-01
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- galaxies: active, methods: analytical
- in
- Monthly Notices of the Royal Astronomical Society
- volume
- 525
- issue
- 2
- pages
- 15 pages
- publisher
- Oxford University Press
- external identifiers
-
- scopus:85169915714
- ISSN
- 0035-8711
- DOI
- 10.1093/mnras/stad2193
- language
- English
- LU publication?
- yes
- id
- 452c9ffd-ad3e-4195-9fd0-7fb2484eab9a
- date added to LUP
- 2023-11-30 12:59:56
- date last changed
- 2023-11-30 13:01:40
@article{452c9ffd-ad3e-4195-9fd0-7fb2484eab9a,
abstract = {{<p>Classifying active galactic nuclei (AGNs) is a challenge, especially for BL Lacertae objects (BLLs), which are identified by their weak emission line spectra. To address the problem of classification, we use data from the fourth Fermi Catalog, Data Release 3. Missing data hinder the use of machine learning to classify AGNs. A previous paper found that Multivariate Imputation by Chain Equations (MICE) imputation is useful for estimating missing values. Since many AGNs have missing redshift and the highest energy, we use data imputation with MICE and k-nearest neighbours (kNN) algorithm to fill in these missing variables. Then, we classify AGNs into the BLLs or the flat spectrum radio quasars (FSRQs) using the SuperLearner, an ensemble method that includes several classification algorithms like logistic regression, support vector classifiers, Random Forest, Ranger Random Forest, multivariate adaptive regression spline (MARS), Bayesian regression, and extreme gradient boosting. We find that a SuperLearner model using MARS regression and Random Forest algorithms is 91.1 per cent accurate for kNN-imputed data and 91.2 per cent for MICE-imputed data. Furthermore, the kNN-imputed SuperLearner model predicts that 892 of the 1519 unclassified blazars are BLLs and 627 are FSRQs, while the MICE-imputed SuperLearner model predicts 890 BLLs and 629 FSRQs in the unclassified set. Thus, we can conclude that both imputation methods work efficiently and with high accuracy and that our methodology ushers the way for using SuperLearner as a novel classification method in the AGN community and, in general, in the astrophysics community.</p>}},
author = {{Cooper, Nathaniel and Dainotti, Maria Giovanna and Narendra, Aditya and Liodakis, Ioannis and Bogdan, Malgorzata}},
issn = {{0035-8711}},
keywords = {{galaxies: active; methods: analytical}},
language = {{eng}},
month = {{10}},
number = {{2}},
pages = {{1731--1745}},
publisher = {{Oxford University Press}},
series = {{Monthly Notices of the Royal Astronomical Society}},
title = {{Fermi LAT AGN classification using supervised machine learning}},
url = {{http://dx.doi.org/10.1093/mnras/stad2193}},
doi = {{10.1093/mnras/stad2193}},
volume = {{525}},
year = {{2023}},
}