An Active Galactic Nucleus Recognition Model based on Deep Neural Network

TL;DR

This paper introduces a deep neural network-based method for recognizing active galactic nuclei (AGNs) in astronomical data, outperforming traditional spectral energy distribution fitting, and provides a new AGN/SFG catalog for the NEPW field.

Contribution

The study develops and validates a neural network approach for AGN recognition, demonstrating improved accuracy over existing methods and releasing a new, reliable AGN/SFG catalog.

Findings

Neural network achieves 80.29%-85.15% recognition accuracy.

AGN completeness is around 85.42%-88.53%.

SFG completeness is around 81.17%-85.09%.

Abstract

To understand the cosmic accretion history of supermassive black holes, separating the radiation from active galactic nuclei (AGNs) and star-forming galaxies (SFGs) is critical. However, a reliable solution on photometrically recognising AGNs still remains unsolved. In this work, we present a novel AGN recognition method based on Deep Neural Network (Neural Net; NN). The main goals of this work are (i) to test if the AGN recognition problem in the North Ecliptic Pole Wide (NEPW) field could be solved by NN; (ii) to shows that NN exhibits an improvement in the performance compared with the traditional, standard spectral energy distribution (SED) fitting method in our testing samples; and (iii) to publicly release a reliable AGN/SFG catalogue to the astronomical community using the best available NEPW data, and propose a better method that helps future researchers plan an advanced NEPW database. Finally, according to our experimental result, the NN recognition accuracy is around 80.29% - 85.15%, with AGN completeness around 85.42% - 88.53% and SFG completeness around 81.17% - 85.09%.