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%.

Viewing alternatives

Look up in Google Scholar

Download history

Metrics

Public Attention

Altmetrics from Altmetric

Number of Citations

Citations from Dimensions

Item Actions

Export

About

  • Item ORO ID
  • 74540
  • Item Type
  • Journal Item
  • ISSN
  • 1365-2966
  • Academic Unit or School
  • Faculty of Science, Technology, Engineering and Mathematics (STEM) > Physical Sciences
    Faculty of Science, Technology, Engineering and Mathematics (STEM)
  • Copyright Holders
  • © 2020 Bo Han Chen, © 2020 Tomotsugu Goto, © 2020 Seong Jin Kim, © 2020 Ting Wen Wang, © 2020 Daryl Joe D. Santos, © 2020 Simon C.-C. Ho, © 2020 Tetsuya Hashimoto, © 2020 Artem Poliszczuk, © 2020 Agnieszka Pollo, © 2020 Sascha Trippe, © 2020 Takamitsu Miyaji, © 2020 Yoshiki Toba, © 2020 Matthew Malkan, © 2020 Stephen Serjeant, © 2020 Chris Pearson, © 2020 Ho Seong Hwang, © 2020 Eunbin Kim, © 2020 Hyunjin Shim, © 2020 Ting Yi Lu, © 2020 Yu-Yang Hsiao, © 2020 Ting-Chi Huang, © 2020 MartÃŋn Herrera-Endoqui, © 2020 Blanca Bravo-Navarro, © 2020 Hideo Matsuhara
  • SWORD Depositor
  • Jisc Publications-Router
  • Depositing User
  • Jisc Publications-Router
CORE (COnnecting REpositories)

Recommendations