We present a spectroscopic and photometric analysis of a sample of 416,288 galaxies from the Sloan Digital Sky Survey (SDSS) matched to mid-infrared (mid-IR) data from the Wide-field Infrared Survey Explorer (WISE). By using a new spectroscopic fitting package, GELATO (Galaxy/AGN Emission Line Analysis TOol), we are able to retrieve emission line fluxes and uncertainties for SDSS spectra and robustly determine the presence of broad lines and outflowing components, enabling us to investigate WISE color space as a function of optical spectroscopic properties. In addition, we pursue spectral energy distribution template fitting to assess the relative active galactic nucleus (AGN) contribution and nuclear obscuration to compare to existing mid-IR selection criteria with WISE. We present a selection criterion in mid-IR color space to select AGNs with an ∼80% accuracy and a completeness of ∼16%. This is the first mid-IR color selection defined by solely using the distribution of Type I and Type II optical spectroscopic AGNs in WISE mid-IR color space. Our selection is an improvement of ∼50% in the completeness of targeting spectroscopic AGNs with WISE down to an SDSS
We present the result of a spectroscopic campaign targeting active galactic nucleus (AGN) candidates selected using a novel unsupervised machine-learning (ML) algorithm trained on optical and mid-infrared photometry. AGN candidates are chosen without incorporating prior AGN selection criteria and are fainter, redder, and more numerous, ∼340 AGN deg−2, than comparable photometric and spectroscopic samples. In this work, we obtain 178 rest-optical spectra from two candidate ML-identified AGN classes with the Hectospec spectrograph on the MMT Observatory. We find that our first ML-identified group is dominated by Type I AGNs (85%) with a <3% contamination rate from non-AGNs. Our second ML-identified group is mostly comprised of Type II AGNs (65%), with a moderate contamination rate of 15% primarily from star-forming galaxies. Our spectroscopic analyses suggest that the classes recover more obscured AGNs, confirming that ML techniques are effective at recovering large populations of AGNs at high levels of extinction. We demonstrate the efficacy of pairing existing WISE data with large-area and deep optical/near-infrared photometric surveys to select large populations of AGNs and recover obscured growth of supermassive black holes. This approach is well suited to upcoming photometric surveys, such as Euclid, Rubin, and Roman.
more » « less- NSF-PAR ID:
- 10496071
- Publisher / Repository:
- DOI PREFIX: 10.3847
- Date Published:
- Journal Name:
- The Astronomical Journal
- Volume:
- 167
- Issue:
- 4
- ISSN:
- 0004-6256
- Format(s):
- Medium: X Size: Article No. 169
- Size(s):
- ["Article No. 169"]
- Sponsoring Org:
- National Science Foundation
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