Identifying Active Galactic Nuclei at $z\sim3$ from the HETDEX Survey Using Machine Learning

分类： 天文学 >> 天文学 提交时间： 2023-02-19

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 Chenxu Liu

摘要： We used data from the Hobby-Eberly Telescope Dark Energy Experiment (HETDEX) to study the incidence of AGN in continuum-selected galaxies at $z\sim3$. From optical and infrared imaging in the 24 deg$^{2}$ Spitzer HETDEX Exploratory Large Area (SHELA) survey, we constructed a sample of photometric-redshift selected $z\sim3$ 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 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 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 $z\sim3$ AGN luminosity function.

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