An Active Galactic Nucleus Recognition Model based on Deep Neural Network

Chen, Bo Han; Goto, Tomotsugu; Kim, Seong Jin; Wang, Ting Wen; Santos, Daryl Joe D; Ho, Simon C-C; Hashimoto, Tetsuya; Poliszczuk, Artem; Pollo, Agnieszka; Trippe, Sascha; Miyaji, Takamitsu; Toba, Yoshiki; Malkan, Matthew; Serjeant, Stephen; Pearson, Chris; Hwang, Ho Seong; Kim, Eunbin; Shim, Hyunjin; Lu, Ting Yi; Hsiao, Yu-Yang; Huang, Ting-Chi; Herrera-Endoqui, Martín; Bravo-Navarro, Blanca and Matsuhara, Hideo (2021). An Active Galactic Nucleus Recognition Model based on Deep Neural Network. Monthly Notices of the Royal Astronomical Society, 501(3) pp. 3951–3961.

DOI: https://doi.org/10.1093/mnras/staa3865

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%.

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