Toward a More Complete Optical Census of Active Galactic Nuclei via Spatially Resolved Spectroscopy

Julia M. Comerford, James Negus, R. Scott Barrows, Dominika Wylezalek, Jenny E. Greene, Francisco Müller-Sánchez, Rebecca Nevin

Research output: Contribution to journalArticlepeer-review

4 Scopus citations

Abstract

While emission line flux ratio diagnostics are the most common technique for identifying active galactic nuclei (AGNs) in optical spectra, applying this approach to single-fiber spectra of galaxies can omit entire subpopulations of AGNs. Here, we use spatially resolved spectroscopy from the Mapping Nearby Galaxies at Apache Point Observatory (MaNGA) survey to construct a sample of 10 galaxies where Baldwin-Philips-Terlevich line flux ratio diagnostics classify each galaxy's central 3″ spectrum as LINER or star-forming, while >10% of the spaxels in the galaxy's MaNGA footprint are classified as Seyfert. We obtain Chandra observations of these 10 galaxies with off-nuclear Seyfert regions to determine whether AGNs are actually present in them. Our main result is that 7-10 (depending on strictness of criteria) of the galaxies host one or more X-ray AGNs, even though none of them were classified as AGNs based on their single-fiber optical spectra. We find that these AGNs were not identified in the single-fiber spectra because they are AGNs in the nuclei of companion galaxies, low-luminosity AGNs, dust-obscured AGNs, and/or flickering AGNs. In summary, we find that off-nuclear AGN signatures may increase the number of known AGNs by a factor of two over what conventional single nuclear fiber spectra identify. Our results show that spatially resolved spectroscopy can be leveraged to reveal a more complete census of AGNs that are traditionally missed by single-fiber spectra.

Original languageEnglish (US)
Article number23
JournalAstrophysical Journal
Volume927
Issue number1
DOIs
StatePublished - Mar 1 2022

All Science Journal Classification (ASJC) codes

  • Astronomy and Astrophysics
  • Space and Planetary Science

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