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

Authors :
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
Source :
The Astronomical Journal, Vol 165, Iss 4, p 153 (2023)
Publication Year :
2023
Publisher :
IOP Publishing, 2023.

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 deg ^2 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 (photo z ) 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.

Details

Language :
English
ISSN :
15383881
Volume :
165
Issue :
4
Database :
Directory of Open Access Journals
Journal :
The Astronomical Journal
Publication Type :
Academic Journal
Accession number :
edsdoj.3986d45100646fcb0a4d53b3be916d4
Document Type :
article
Full Text :
https://doi.org/10.3847/1538-3881/acba92