Identifying Active Galactic Nuclei at z ∼ 3 from the HETDEX Survey Using Machine Learning

Valentina Tardugno Poleo, Steven L. Finkelstein, Gene Leung, Erin Mentuch Cooper, Karl Gebhardt, Daniel J. Farrow, Eric Gawiser, Greg Zeimann, Donald P. Schneider, Leah Morabito, Daniel Mock, Chenxu Liu

Research output: Contribution to journalArticlepeer-review

3 Scopus citations

Abstract

We used data from the Hobby-Eberly Telescope Dark Energy Experiment (HETDEX) to study the incidence of AGN in continuum-selected galaxies at z ∼ 3. From optical and infrared imaging in the 24 deg2 Spitzer HETDEX Exploratory Large Area survey, we constructed a sample of photometric-redshift selected z ∼ 3 galaxies. We extracted HETDEX spectra at the position of 716 of these sources and used machine-learning methods to identify those which exhibited AGN-like features. The dimensionality of the spectra was reduced using an autoencoder, and the latent space was visualized through t-distributed stochastic neighbor embedding. Gaussian mixture models were employed to cluster the encoded data and a labeled data set was used to label each cluster as either AGN, stars, high-redshift galaxies, or low-redshift galaxies. Our photometric redshift (photoz) sample was labeled with an estimated 92% overall accuracy, an AGN accuracy of 83%, and an AGN contamination of 5%. The number of identified AGN was used to measure an AGN fraction for different magnitude bins. The ultraviolet (UV) absolute magnitude where the AGN fraction reaches 50% is M UV = −23.8. When combined with results in the literature, our measurements of AGN fraction imply that the bright end of the galaxy luminosity function exhibits a power law rather than exponential decline, with a relatively shallow faint-end slope for the z ∼ 3 AGN luminosity function.

Original languageEnglish (US)
Article number153
JournalAstronomical Journal
Volume165
Issue number4
DOIs
StatePublished - Apr 1 2023

All Science Journal Classification (ASJC) codes

  • Astronomy and Astrophysics
  • Space and Planetary Science

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