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名稱Title
2021天文年會
國立嘉義大學蘭潭校區
5月21日至23日

論文摘要

An active galactic nucleus recognition model based on deep neural network

[ Poster ]

Bo Han Chen (Department of Physics, National Tsing Hua University), Tomotsugu Goto(Institute of Astronomy, National Tsing Hua University), Seong Jin Kim(Institute of Astronomy, National Tsing Hua University)

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%.