Predicting the Redshift of γ-Ray-loud AGNs Using Supervised Machine Learning
Autor: | Kamil Wozniak, Trevor Nelson, Agnieszka Pollo, Aditya Narendra, Zooey Nguyen, Spencer James Gibson, Małgorzata Bogdan, Maria Giovanna Dainotti, Johan Larrson, Błażej Miasojedow, Ioannis Liodakis |
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Rok vydání: | 2021 |
Předmět: |
Astrophysics::High Energy Astrophysical Phenomena
FOS: Physical sciences Astrophysics::Cosmology and Extragalactic Astrophysics Machine learning computer.software_genre symbols.namesake Lasso (statistics) Coming out Instrumentation and Methods for Astrophysics (astro-ph.IM) Astrophysics::Galaxy Astrophysics High Energy Astrophysical Phenomena (astro-ph.HE) Physics business.industry Astronomy and Astrophysics Small sample Pearson product-moment correlation coefficient Galaxy Redshift Stars Space and Planetary Science Norm (mathematics) symbols Artificial intelligence Astrophysics - High Energy Astrophysical Phenomena Astrophysics - Instrumentation and Methods for Astrophysics business computer |
Zdroj: | The Astrophysical Journal. 920:118 |
ISSN: | 1538-4357 0004-637X |
Popis: | AGNs are very powerful galaxies characterized by extremely bright emissions coming out 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, their formation along with the structure of early galaxies. The redshift determination is challenging because it requires detailed follow-up of multi-wavelength observations, often involving various astronomical facilities. Here, we employ machine learning algorithms to estimate redshifts from the observed gamma-ray properties and photometric data of gamma-ray loud AGN from the Fourth Fermi-LAT Catalog. The prediction is obtained with the Superlearner algorithm, using LASSO selected set of predictors. We obtain a tight correlation, with a Pearson Correlation Coefficient of 71.3% between the inferred and the observed redshifts, an average {\Delta}z_norm = 11.6 x 10^-4. We stress that notwithstanding the small sample of gamma-ray loud AGNs, we obtain a reliable predictive model using Superlearner, which is an ensemble of several machine learning models. Comment: 29 pages, 19 Figures with a total of 39 panels |
Databáze: | OpenAIRE |
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