Predicting the redshift of gamma-ray loud AGNs using supervised machine learning
arXiv:2107.10952 · doi:10.3847/1538-4357/ac1748
Abstract
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 Δz_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.
29 pages, 19 Figures with a total of 39 panels
References in corpus (14)
- COSMOS Photometric Redshifts with 30-bands for 2-deg2
- Photometric redshift estimation via deep learning
- Robust Machine Learning Applied to Astronomical Datasets III: Probabilistic Photometric Redshifts for Galaxies and Quasars in the SDSS and GALEX
- Unsupervised star, galaxy, qso classification: Application of HDBSCAN
- Blazar Flaring Patterns (B-FlaP): Classifying Blazar Candidates of Uncertain type in the third Fermi-LAT catalog by Artificial Neural Networks
- Machine-learning identification of galaxies in the WISExSuperCOSMOS all-sky catalogue
- A Machine Learning Method to Infer Fundamental Stellar Parameters from Photometric Light Curves
- Evaluating the optical classification of Fermi BCUs using machine learning
- Using X-Ray Morphological Parameters to Strengthen Galaxy Cluster Mass Estimates via Machine Learning
- Quasar Photometric Redshifts and Candidate Selection: A New Algorithm Based on Optical and Mid-Infrared Photometric Data
- Gamma-ray Luminosity and Photon Index Evolution of FSRQ Blazars and Contribution to the Gamma-ray Background
- New High-z Fermi BL Lacs with the Photometric Dropout Technique
- QSO photometric redshifts from SDSS, WISE and GALEX colours
- Probing the unidentified Fermi blazar-like population using optical polarization and machine learning