| 1. |
- Cooper, Nathaniel, et al.
(author)
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Fermi LAT AGN classification using supervised machine learning
- 2023
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In: Monthly Notices of the Royal Astronomical Society. - 0035-8711. ; 525:2, s. 1731-1745
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Journal article (peer-reviewed)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 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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| 2. |
- Dainotti, Maria Giovanna, et al.
(author)
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Predicting the Redshift of γ-Ray-loud AGNs Using Supervised Machine Learning
- 2021
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In: Astrophysical Journal. - : American Astronomical Society. - 0004-637X .- 1538-4357. ; 920:2
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Journal article (peer-reviewed)abstract
- Active galactic nuclei (AGNs) are very powerful galaxies characterized by extremely bright emissions coming from their central massive black holes. Knowing the redshifts of AGNs provides us with an opportunity to determine their distance to investigate important astrophysical problems, such as the evolution of the early stars and their formation, along with the structure of early galaxies. The redshift determination is challenging because it requires detailed follow-up of multiwavelength observations, often involving various astronomical facilities. Here we employ machine-learning algorithms to estimate redshifts from the observed γ-ray properties and photometric data of γ-ray-loud AGNs from the Fourth Fermi-LAT Catalog. The prediction is obtained with the Superlearner algorithm using a LASSO-selected set of predictors. We obtain a tight correlation, with a Pearson correlation coefficient of 71.3% between the inferred and observed redshifts and an average Δz norm = 11.6 10-4. We stress that, notwithstanding the small sample of γ-ray-loud AGNs, we obtain a reliable predictive model using Superlearner, which is an ensemble of several machine-learning models.
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| 3. |
- Gibson, Spencer James, et al.
(author)
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Using Multivariate Imputation by Chained Equations to Predict Redshifts of Active Galactic Nuclei
- 2022
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In: Frontiers in Astronomy and Space Sciences. - : Frontiers Media SA. - 2296-987X. ; 9
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Journal article (peer-reviewed)abstract
- Redshift measurement of active galactic nuclei (AGNs) remains a time-consuming and challenging task, as it requires follow up spectroscopic observations and detailed analysis. Hence, there exists an urgent requirement for alternative redshift estimation techniques. The use of machine learning (ML) for this purpose has been growing over the last few years, primarily due to the availability of large-scale galactic surveys. However, due to observational errors, a significant fraction of these data sets often have missing entries, rendering that fraction unusable for ML regression applications. In this study, we demonstrate the performance of an imputation technique called Multivariate Imputation by Chained Equations (MICE), which rectifies the issue of missing data entries by imputing them using the available information in the catalog. We use the Fermi-LAT Fourth Data Release Catalog (4LAC) and impute 24% of the catalog. Subsequently, we follow the methodology described in Dainotti et al. (ApJ, 2021, 920, 118) and create an ML model for estimating the redshift of 4LAC AGNs. We present results which highlight positive impact of MICE imputation technique on the machine learning models performance and obtained redshift estimation accuracy.
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| 4. |
- Narendra, Aditya, et al.
(author)
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Predicting the Redshift of Gamma-Ray Loud AGNs Using Supervised Machine Learning. II
- 2022
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In: Astrophysical Journal, Supplement Series. - : American Astronomical Society. - 0067-0049 .- 1538-4365. ; 259:2
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Journal article (peer-reviewed)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.
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