An Active Galactic Nucleus Recognition Model based on Deep Neural
  Network
  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%.
