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Abstract We present an analysis of NuSTAR X-ray observations of three active galactic nuclei (AGN) that were identified as candidate subparsec binary supermassive black hole (SMBH) systems in the Catalina Real-Time Transient Survey based on apparent periodicity in their optical light curves. Simulations predict that close-separation accreting SMBH binaries will have different X-ray spectra than single accreting SMBHs. We previously observed these AGN with Chandra and found no differences between their low-energy X-ray properties and the larger AGN population. However, some models predict differences to be more prominent at energies higher than probed by Chandra. We find that even at the higher energies probed by NuSTAR, the spectra of these AGN are indistinguishable from the larger AGN population. This could rule out models predicting large differences in the X-ray spectra in the NuSTAR bands. Alternatively, it might mean that these three AGN are not binary SMBHs.
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AGNet: weighing black holes with deep learning
ABSTRACT Supermassive black holes (SMBHs) are commonly found at the centres of most massive galaxies. Measuring SMBH mass is crucial for understanding the origin and evolution of SMBHs. Traditional approaches, on the other hand, necessitate the collection of spectroscopic data, which is costly. We present an algorithm that weighs SMBHs using quasar light time series information, including colours, multiband magnitudes, and the variability of the light curves, circumventing the need for expensive spectra. We train, validate, and test neural networks that directly learn from the Sloan Digital Sky Survey (SDSS) Stripe 82 light curves for a sample of 38 939 spectroscopically confirmed quasars to map out the non-linear encoding between SMBH mass and multiband optical light curves. We find a 1σ scatter of 0.37 dex between the predicted SMBH mass and the fiducial virial mass estimate based on SDSS single-epoch spectra, which is comparable to the systematic uncertainty in the virial mass estimate. Our results have direct implications for more efficient applications with future observations from the Vera C. Rubin Observatory. Our code, AGNet, is publicly available at https://github.com/snehjp2/AGNet.
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- Award ID(s):
- 2108162
- PAR ID:
- 10384858
- Publisher / Repository:
- Oxford University Press
- Date Published:
- Journal Name:
- Monthly Notices of the Royal Astronomical Society
- Volume:
- 518
- Issue:
- 4
- ISSN:
- 0035-8711
- Format(s):
- Medium: X Size: p. 4921-4929
- Size(s):
- p. 4921-4929
- Sponsoring Org:
- National Science Foundation
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