The browser you are using is not supported by this website. All versions of Internet Explorer are no longer supported, either by us or Microsoft (read more here: https://www.microsoft.com/en-us/microsoft-365/windows/end-of-ie-support).

Please use a modern browser to fully experience our website, such as the newest versions of Edge, Chrome, Firefox or Safari etc.

Johan Larsson. Photo.

Johan Larsson

Doctoral student

Johan Larsson. Photo.

Predicting the Redshift of γ-Ray-loud AGNs Using Supervised Machine Learning

Author

  • Maria Giovanna Dainotti
  • Malgorzata Bogdan
  • Aditya Narendra
  • Spencer James Gibson
  • Blazej Miasojedow
  • Ioannis Liodakis
  • Agnieszka Pollo
  • Trevor Nelson
  • Kamil Wozniak
  • Zooey Nguyen
  • Johan Larsson
Show all

Summary, in English

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.

Department/s

  • Department of Statistics

Publishing year

2021-10-20

Language

English

Publication/Series

Astrophysical Journal

Volume

920

Issue

2

Document type

Journal article

Publisher

American Astronomical Society

Topic

  • Astronomy, Astrophysics and Cosmology

Status

Published

ISBN/ISSN/Other

  • ISSN: 0004-637X