Identifying Active Galactic Nuclei at $z\sim3$ from the HETDEX Survey Using Machine Learning
Valentina Tardugno Poleo, Steven Finkelstein, Gene C. K. Leung, Erin, Mentuch Cooper, Karl Gebhardt, Daniel Farrow, Eric Gawiser, Gregory Zeimann,, Donald Schneider, Leah Morabito, Daniel Mock, and Chenxu Liu

TL;DR
This study employs machine learning techniques on spectroscopic and photometric data from the HETDEX survey to identify AGN at redshift around 3, revealing insights into the AGN fraction and luminosity function shape.
Contribution
It introduces a novel machine learning pipeline combining autoencoders, t-SNE, and Gaussian mixture models to classify AGN in high-redshift galaxies using spectroscopic data.
Findings
Achieved 92% overall accuracy in photometric redshift labeling.
Identified the UV magnitude where 50% of galaxies host AGN as -23.8.
Found the bright end of the luminosity function follows a power-law, not exponential.
Abstract
We used data from the Hobby-Eberly Telescope Dark Energy Experiment (HETDEX) to study the incidence of AGN in continuum-selected galaxies at . From optical and infrared imaging in the 24 deg Spitzer HETDEX Exploratory Large Area (SHELA) survey, we constructed a sample of photometric-redshift selected 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 (t-SNE). Gaussian mixture models were employed to cluster the encoded data and a labeled dataset 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…
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Taxonomy
TopicsGalaxies: Formation, Evolution, Phenomena · Astronomy and Astrophysical Research · Gamma-ray bursts and supernovae
