An active galactic nucleus recognition model based on deep neural network
Hashimoto, Tetsuya; Goto, Tomotsugu; Kim, Seong Jin; Ho, Simon C. -C.; Pearson, Chris; Serjeant, Stephen; Matsuhara, Hideo; Hwang, Ho Seong; Poliszczuk, Artem; Pollo, Agnieszka; Malkan, Matthew; Shim, Hyunjin; Toba, Yoshiki; Kim, Eunbin; Huang, Ting-Chi; Miyaji, Takamitsu; Santos, Daryl Joe D.; Herrera-Endoqui, Martín; Chen, Bo Han; Wang, Ting Wen; Trippe, Sascha; Lu, Ting Yi; Hsiao, Yu-Yang; Bravo-Navarro, Blanca
Taiwan, Poland, South Korea, Mexico, Germany, Japan, United States, United Kingdom
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 recognizing 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 show 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 data base. Finally, according to our experimental result, the NN recognition accuracy is around 80.29 per cent-85.15 per cent, with AGN completeness around 85.42 per cent-88.53 per cent and SFG completeness around 81.17 per cent-85.09 per cent.