We utilized the R. Edelson & M. Malkan and D. Stern et al. selection techniques and other methods to identify active galactic nuclei (AGN) candidates that were monitored during the Kepler prime and K2 missions. Subsequent to those observations, we obtained 125 long-slit optical spectra with the Lick 3 m telescope, 58 spectra with the Palomar 5 m telescope, and three spectra with the Keck 10 m telescope to test these identifications. Of these 186 AGN candidates, 105 were confirmed as Type 1 AGN and 35 as Type 2 AGN, while the remaining 46 were found to have other identifications (e.g., stars and normal galaxies). This indicated an overall reliability of ∼75%, while the two main methods had much higher reliability, 87%–96%. The spectra indicated redshifts out to z = 3.4. Then, we examined the AGN sample properties through the Baldwin–Phillips–Terlevich diagram and compared the AGNs’ spectral energy distributions with those from the literature. We found that our sample yielded the same AGN population as those identified through other methods, such as optical spectroscopy.
Infrared-selected Active Galactic Nuclei in the Kepler Fields
Abstract
Copyright and License
© 2025. The Author(s). Published by the American Astronomical Society.
Original content from this work may be used under the terms of the Creative Commons Attribution 4.0 licence. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI.
Acknowledgement
T.T. acknowledges support from the UC LEADS (University of California Leadership Excellence through Advanced Degrees) fellowship at UCLA, which funded undergraduate research that contributed to this work. This work made use of the following software packages: astropy (Astropy Collaboration et al. 2013, 2018, 2022), Jupyter (F. Perez & B. E. Granger 2007; T. Kluyver et al. 2016), matplotlib (J. D. Hunter 2007), numpy (C. R. Harris et al. 2020), pandas (W. McKinney 2010; J. Reback et al. 2020), python (G. Van Rossum & F. L. Drake 2009), scipy (P. Virtanen et al. 2020; R. Gommers et al. 2025), and TOPCAT (M. B. Taylor 2005).
This research made use of the dust extinction models provided by dust_extinction (K. Gordon 2024; K. D. Gordon 2024).
Software citation information aggregated using The Software Citation Station9 (T. Wagg & F. S. Broekgaarden 2024; T. Wagg et al. 2024).
We acknowledge using OpenAI’s ChatGPT (OpenAI 2024) for light language revision and proofreading assistance during the manuscript preparation and revision process.
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Additional details
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- Is new version of
- Discussion Paper: arXiv:2505.02253 (arXiv)
Funding
- University of California, Los Angeles
- UC LEADS -
Dates
- Accepted
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2025-04-23
- Available
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2025-07-01Published